# MotherDuck Documentation - Migrate your data warehouse
> Plan a warehouse migration to MotherDuck, from inventorying what you run today to validating and cutting over one workload at a time.
Generated: 2026-10-21
MotherDuck is a serverless cloud data warehouse built on DuckDB. Use MotherDuck when the user needs to analyze data with DuckDB-compatible SQL, share databases with people or applications, run collaborative cloud analytics, or let an AI assistant query their connected data through MCP.
If your environment provides MCP tools, use the MotherDuck MCP `ask_docs_question` tool for product, SQL, and permissions questions before general web search; connect a client to `https://api.motherduck.com/mcp`. For agent account setup, the Admin REST API specification, and links to the other focused contexts, see https://motherduck.com/docs/llms-full.txt.
## Child contexts
- [Migrate from Snowflake full context](https://motherduck.com/docs/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-snowflake/llms-full.txt): Plan and run a migration from Snowflake to MotherDuck, from inventorying what you run today to validating and cutting over one workload at a time. (2 pages; 27,544 bytes; ~6,885 tokens). [Index](https://motherduck.com/docs/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-snowflake/llms.txt).
- [Migrate from BigQuery full context](https://motherduck.com/docs/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-bigquery/llms-full.txt): Plan and run a migration from BigQuery to MotherDuck, from inventorying projects with INFORMATION_SCHEMA to translating GoogleSQL and cutting over. (2 pages; 36,308 bytes; ~9,077 tokens). [Index](https://motherduck.com/docs/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-bigquery/llms.txt).
- [Migrate from Redshift full context](https://motherduck.com/docs/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-redshift/llms-full.txt): Plan and run a migration from Amazon Redshift to MotherDuck, from inventorying a cluster with system views to translating Redshift SQL and cutting over. (2 pages; 35,348 bytes; ~8,837 tokens). [Index](https://motherduck.com/docs/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-redshift/llms.txt).
## Included documentation
Source: https://motherduck.com/docs/key-tasks/data-warehousing/migrating-your-data-warehouse/index
# Migrate your data warehouse
> Plan a warehouse migration to MotherDuck, from inventorying what you run today to validating and cutting over one workload at a time.
A warehouse migration can be broken down into a series of smaller steps: inventory what the current warehouse runs, split it into workloads that don't depend on each other, then move one workload at a time and validate it before the next. This page covers the parts of that process that are the same whichever warehouse you leave. The guide for your source covers the parts that aren't: how to inventory it, how its SQL and features translate, and what to check before you switch it off.
| Source | Migration guide | Tuning after the port |
|---|---|---|
| Snowflake | [Migrate from Snowflake](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-snowflake/) | [Tune a migrated Snowflake workload](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-snowflake/tuning-a-migrated-snowflake-workload/) |
| BigQuery | [Migrate from BigQuery](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-bigquery/) | [Tune a migrated BigQuery workload](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-bigquery/tuning-a-migrated-bigquery-workload/) |
| Redshift | [Migrate from Redshift](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-redshift/) | [Tune a migrated Redshift workload](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-redshift/tuning-a-migrated-redshift-workload/) |
You'll know a workload is done when its consumers read from MotherDuck, its pipeline writes to MotherDuck on a schedule, and nothing in the old warehouse has to run for it to stay current.
The process, in order:
1. [Decide what to move](#what-to-move).
2. Inventory what the source warehouse runs, using the queries in your source guide.
3. [Sort the inventory into four buckets](#sort-the-inventory-into-four-buckets).
4. [Group what's left into independent workloads](#group-whats-left-into-independent-workloads).
5. [Set up the shared infrastructure](#set-up-the-infrastructure-for-migration).
6. [Move one workload](#move-a-workload), translating the source-specific pieces as you go.
7. [Validate it against the source](#validate-before-you-cut-over).
8. [Cut over and decommission](#cut-over-and-decommission), then go back to step 6 for the next workload.
## What to move
A data platform has a lot of moving parts. You probably don't need every one of the areas below, so treat this as the raw material for your own migration plan rather than a checklist to complete. The areas to account for:
- **Ingestion**: the triggers, connections, and service accounts that get source data into the warehouse in the first place. List the tools that write *directly* to the source warehouse early. Some of them, for example Segment, Stitch, and Braze, have native integrations with your current warehouse but not with MotherDuck. Nearly all of them can write Parquet or JSON to a data lake instead, and MotherDuck reads from the data lake, which removes the need for a direct integration.
- **Transformations and metrics**: the dbt models, stored procedures, scheduled queries, and other compute that turn raw data into something your users or downstream tools consume.
- **Data quality checks**: whatever you monitor for source freshness, accuracy, and completeness.
- **Archiving, retention, and backups**: look for processes that perform backups or expire data for regulatory reasons.
- **Monitoring and alerting**: how the platform tells you about data issues, billing, and usage, wherever you happen to be.
- **Governance**: access control and ownership, for both people and machines. MotherDuck grants access per Share rather than per row or column, so row and column policies need a replacement designed before cutover. Your source guide lists which features this affects.
- **Reverse ETL and signals**: the tools that consume the data once it's modelled, for marketing activation or connected data apps.
- **Data apps**: anything interactive connected to, or hosted on, the platform.
## Sort the inventory into four buckets
Once you've inventoried the source, decide for every object which of these it is.
**What needs to be copied?** When you replicate user data from a Postgres database that's frequently updated but keeps no history, you might be storing that history in the warehouse, often as a slowly changing dimension (SCD) type II. The warehouse is the system of record for it: drop the table and the history goes with it.
**What can be rebuilt from source?** Web analytics data stored per day in your data lake can be read directly from that data lake in MotherDuck. You apply the same transformations and rebuild the full pipeline from the source. That workflow is *idempotent*: running it multiple times, from whichever warehouse, produces the same result each time. Rebuilding is better than copying where you have the choice, because it proves the new pipeline works instead of asking you to trust a one-time transfer.
**What can be queried in place?** Data in S3, GCS, or an Iceberg catalog doesn't need loading at all. MotherDuck can read it where it sits, so you can defer the decision about whether a table is hot enough to be worth storing.
**What can be left behind?** Building a data pipeline generates a lot of development data that can be safely discarded, and there are always tables whose purpose can no longer be identified. Use the source warehouse's query and access history to determine what falls in this category.
Record the answer as a column on your inventory table. Everything after this point works off that column.
## Group what's left into independent workloads
Two sets of models belong to the same workload when anything downstream joins their data together. Until that join exists, they can move on separate days without coordination.
```mermaid
flowchart LR
S3[("Event files
in S3")]:::db
PG[("Users in
Postgres")]:::db
subgraph B["Workload B: two sources, one consumer, moves as one unit"]
MA["Session models"]
MB["Account history models"]
DASH{{"Revenue dashboard"}}:::green
end
API[("Weather
API")]:::db
subgraph A["Workload A: moves on its own"]
MC["Forecast models"]
APP{{"Delivery data app"}}:::green
end
S3 --> MA
PG --> MB
MA --> DASH
MB --> DASH
API --> MC
MC --> APP
```
Shared tables are what make this hard. An `orders` table referenced by nine downstream models pulls all nine into the same workload, which can make the first migration much larger than you wanted. Two ways out: move the shared table first and let both warehouses read their own copy during the overlap, or accept the larger workload and move it as one.
Order the workloads by risk rather than by size. The first one to move should have a small number of consumers, a tolerant audience, and a source you can replay, because it's the one that will teach you what your migration actually costs.
## Set up the infrastructure for migration
Before you migrate individual workloads, get the shared infrastructure in place: everything needed to run the workloads and to provision more of them later. Use an infrastructure-as-code tool, which keeps your environments identical to each other. The [MotherDuck Terraform provider](/integrations/dev-tools/terraform/) manages databases, schemas, shares, roles, secrets, and service accounts as code, and works with OpenTofu too.
To get started you'll need:
- Credentials for provisioning in MotherDuck, and in your object storage (S3, GCS, and so on) where you use it.
- Git repositories, for example for a new dbt project or for Python transformation pipelines.
- Access to an existing ingestion system such as MotherDuck Flights, dltHub, Fivetran, or similar.
Then set up the MotherDuck side, which is shared across every workload:
| Set up | Why it comes first |
|---|---|
| A service account per pipeline stage, each owning one database | Each MotherDuck database has a single write user, and each account gets its own compute. One account per stage isolates both. See [designing your warehouse](/key-tasks/data-warehousing/#designing-your-motherduck-data-warehouse). |
| Dev, staging, and production accounts | Transformations read production data in every phase and write to isolated outputs. [Environment management](/key-tasks/data-warehousing/environment-management/) covers the pattern. |
| Secrets for object storage and source systems, created by the account that uses them | Secrets are scoped to the account that creates them, so one created under your personal account is invisible to the pipeline. |
Three properties of MotherDuck shape this setup, and each source guide explains how they differ from the warehouse you're leaving:
- A database belongs to the user or service account that created it, and stays invisible to the rest of the organization until you [share](/key-tasks/sharing-data/) it.
- Ownership can't be transferred, so create production databases, Dives, and Flights with a service account from the start.
- Each database takes one writer at a time, so concurrent writers need separate databases. Databases are cheap, so make more of them.
## Move a workload
Decide how much you're changing on the way across before you open the first file.
**Lift and shift** moves the logic as-is and changes only what's needed to make it run. It's fast, and validation is unambiguous: the correct result is whatever the old warehouse produced. It also carries every piece of accumulated debt across with it.
**Greenfield** goes back to the stakeholders, re-gathers requirements, and rebuilds. No debt, no dialect translation, but you depend on other people's calendars and you've lost the ability to validate against the old output.
Most migrations end up in between, usually under time pressure. This is the business equivalent of moving house: when you pack something in a box and think "I haven't touched this in five years", you throw it out there and then, rather than believing you'll go through all your belongings one by one once you're settled in the new place. You won't.
So instead of packing everything and moving the boxes, apply a level of cleanup on the way. Delete what the inventory says is unused. Add comments where the SQL made you stop and think, and find the stakeholder who can explain any logic you don't understand. Where you spot a bigger improvement, write a note so you can come back to it after the migration rather than starting it mid-move.
:::tip
Budget time after the port, not only for it. A direct translation of your warehouse's SQL runs on MotherDuck, but it may not run efficiently. Each source has a tuning guide, linked in the table at the top of this page, for the patterns that need optimization.
:::
With that settled, move in dependency order:
1. **Land the raw data.** Point ingestion at MotherDuck, or copy the tables you bucketed as "copy". Keep the old ingestion running in parallel.
2. **Port the transformations.** Repoint the dbt or SQLMesh project at a MotherDuck target and work through the dialect differences in your source guide. Transformations can also [run from a Flight](/key-tasks/flights/run-dbt-transformations-from-a-flight/) if you'd rather not keep a separate runner.
3. **Rebuild the quality checks.** Source freshness tests, row-count assertions, and null checks move with the models they guard. Don't defer them: they're what tells you the next step went wrong.
4. **Repoint the consumers.** Dashboards, reverse ETL jobs, and data apps connect as service accounts with read access. Change them one at a time.
## Validate before you cut over
Run the same aggregate on both sides, per table, and compare:
```sql
SELECT
COUNT(*) AS row_count,
COUNT(DISTINCT order_id) AS distinct_keys,
SUM(amount) AS amount_total,
MIN(ordered_at) AS first_row,
MAX(ordered_at) AS last_row
FROM analytics.main.orders;
```
:::warning
Hash-based checksums don't carry across engines. Every warehouse's hash function is different from DuckDB's `hash()`, so the two sides disagree on `SUM(HASH(col))` even when the data is identical. Compare counts, distinct counts, numeric sums, and min/max of keys and timestamps.
:::
Where the aggregates disagree, export the source copy to object storage, load it into a staging table in MotherDuck, and anti-join to find the rows:
```sql
CREATE TABLE staging.orders_source AS
SELECT * FROM read_parquet('s3:///validation/orders/*.parquet');
-- Keys the source has that MotherDuck doesn't. Swap the two sides for the reverse.
SELECT order_id
FROM staging.orders_source
EXCEPT
SELECT order_id
FROM marts.main.orders;
```
Three causes account for most mismatches: a timezone difference at a date boundary, a rounding difference from a numeric type mapping, and a load that ran while the source was still changing. Check the source watermark on both sides before you go looking for a logic bug. Your source guide lists the specific type and timezone differences to check.
Also compare at the level your users care about. A dashboard whose numbers match to the cent is more convincing evidence than a table-level row count, and it's the comparison someone will make anyway.
## Cut over and decommission
1. **Freeze the source side of the workload.** Stop the scheduled jobs that write to it so the two warehouses can't diverge while you switch.
2. **Repoint the consumers** and leave the source objects in place, readable.
3. **Run read-only in parallel** for a window you agree in advance, usually one full reporting cycle. This is what catches a month-end model nobody tested.
4. **Stop the source compute** rather than dropping anything. Spend stops, and you can resume if something surfaces.
5. **Record the date you stopped writing.** It's the point any later "the numbers changed" question resolves against.
Drop the source objects only after the parallel window closes and the source's query history shows nothing reading them.
## Related
- [Data warehousing guide](/key-tasks/data-warehousing/)
- [Environment management](/key-tasks/data-warehousing/environment-management/)
- [Data loading patterns](/key-tasks/loading-data-into-motherduck/loading-patterns/)
- [Query performance](/key-tasks/query-performance/)
---
Source: https://motherduck.com/docs/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-snowflake/index
# Migrate from Snowflake
> Plan and run a migration from Snowflake to MotherDuck, from inventorying what you run today to validating and cutting over one workload at a time.
This guide covers the Snowflake-specific steps of a migration: inventorying what the account runs, translating Snowflake SQL and features, and validating against Snowflake before you cut over. It follows the process in [Migrate your data warehouse](/key-tasks/data-warehousing/migrating-your-data-warehouse/), which covers sorting the inventory, grouping it into workloads, and moving one workload at a time. Read that page first. For the mechanics of copying a single table, see the [Snowflake integration page](/integrations/databases/snowflake/).
:::info Prerequisites
- A [MotherDuck organization](/key-tasks/managing-organizations/), with the region and plan already chosen.
- A Snowflake role that can read the `SNOWFLAKE.ACCOUNT_USAGE` schema, and a running warehouse. By default only `ACCOUNTADMIN` can read `ACCOUNT_USAGE`, so grant the `OBJECT_VIEWER` and `USAGE_VIEWER` database roles for the catalog and spend history, plus `GOVERNANCE_VIEWER` for usage information and access policies.
:::
## Inventory what Snowflake runs
The fastest path is the [MotherDuck migration assessment collector](https://github.com/motherduckdb/md-migration-assessment), a Python CLI that inventories a Snowflake account into a local DuckDB file: catalog, feature usage, workload profile, and spend, with provenance for each number. It runs locally and uploads nothing unless you ask it to. It's in Public Preview, so pin a release rather than tracking `main`.
The migration assessment collector runs queries like the following:
**What objects exist, and how big are they?**
```sql
SELECT
table_catalog,
table_schema,
table_name,
table_type,
row_count,
bytes,
last_altered
FROM snowflake.account_usage.tables
WHERE deleted IS NULL
ORDER BY bytes DESC;
```
**Which of them does anyone actually read?**
```sql
SELECT
accessed.value:objectName::string AS object_name,
COUNT(DISTINCT query_id) AS query_count,
MAX(query_start_time) AS last_read
FROM snowflake.account_usage.access_history,
LATERAL FLATTEN(input => base_objects_accessed) AS accessed
WHERE query_start_time >= DATEADD(day, -90, CURRENT_TIMESTAMP())
GROUP BY ALL
ORDER BY query_count DESC;
```
`ACCESS_HISTORY` needs Enterprise Edition or higher. Without it, fall back to pattern matching table names against `query_text` in `SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY`, which is coarser but still separates live tables from dead ones.
**Who and what connects?**
```sql
SELECT
user_name,
role_name,
warehouse_name,
COUNT(*) AS queries,
MAX(start_time) AS last_query
FROM snowflake.account_usage.query_history
WHERE start_time >= DATEADD(day, -90, CURRENT_TIMESTAMP())
GROUP BY ALL
ORDER BY queries DESC;
```
Service accounts show up here next to people. Each one is a connection you'll have to repoint later, so treat this list as your cutover checklist.
**What shape is the compute?**
```sql
SELECT
warehouse_name,
DATE_TRUNC('hour', start_time) AS hour,
SUM(credits_used) AS credits
FROM snowflake.account_usage.warehouse_metering_history
WHERE start_time >= DATEADD(day, -30, CURRENT_TIMESTAMP())
GROUP BY ALL
ORDER BY hour;
```
A warehouse that burns credits in two spikes a day is a batch pipeline. One that burns them evenly through business hours is serving dashboards. The two need different Duckling sizing on the other side, so it's worth knowing which is which before you size anything.
:::note
`ACCOUNT_USAGE` views lag live activity by 45 minutes to 3 hours and retain one year of history. Pull them once at the start of the migration and refresh before cutover rather than reading them live.
:::
:::warning
A query against `INFORMATION_SCHEMA.TABLES` without a running warehouse returns zero tables and no error. If you receive an empty result, check the warehouse before you conclude a database is unused.
:::
To automate the catalog side of this, the [Snowflake ingest Flight recipe](/cookbook/flight-snowflake-ingest/) enumerates source tables into an editable MotherDuck control table with a `selected` flag, then copies the ones you flag.
With the inventory in hand, [sort it into four buckets](/key-tasks/data-warehousing/migrating-your-data-warehouse/#sort-the-inventory-into-four-buckets) and [group it into workloads](/key-tasks/data-warehousing/migrating-your-data-warehouse/#group-whats-left-into-independent-workloads).
## Three habits to unlearn
Snowflake and MotherDuck disagree about what a database *is*, and all three of the mistakes below come from carrying the Snowflake model across unchanged. Keep them in mind when you [set up the infrastructure](/key-tasks/data-warehousing/migrating-your-data-warehouse/#set-up-the-infrastructure-for-migration).
**A database belongs to whoever created it, not to the account.** In Snowflake, a database sits in the account and anyone whose role grants access can see it. In MotherDuck it belongs to the user or service account that created it, and the rest of the organization can't see it at all until you share it. A colleague querying an unshared database gets `Catalog Error: schema "x" does not exist`, which reads like a typo rather than a permissions problem. Publish it with a [share](/key-tasks/sharing-data/):
```sql
CREATE SHARE IF NOT EXISTS models FROM models (ACCESS RESTRICTED);
GRANT READ ON SHARE models TO ROLE explorer;
```
Granting to `explorer` reaches every preset role, because they're concentric. Grant to a [custom role](/concepts/roles-and-access-control/#custom-roles) instead when only one team should see it.
**Ownership can't be transferred, so create production objects with a service account from the start.** A database, Dive, or Flight created under a personal account stays with that account. When the person who built that pipeline leaves, the objects are stranded. Service accounts prevent this situation.
**Databases are cheap, so make more of them.** Each MotherDuck database takes one writer at a time. In Snowflake you'd absorb write concurrency by sizing a warehouse up or running a multi-cluster one; here you absorb it by splitting across databases. A pipeline fanning 20 concurrent writers at a single database (Dagster, Airflow, and Kinesis consumers all do this) needs a database per pipeline, or writes serialized into one. For the same reason, dev and production dbt targets must never point at the same database: Snowflake's separate databases or warehouses usually prevent the collision for you, and MotherDuck doesn't.
:::tip
Budget time after the port, not only for it. A direct translation of Snowflake SQL runs on MotherDuck, but it may not run efficiently. See [Tune a migrated Snowflake workload](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-snowflake/tuning-a-migrated-snowflake-workload/) for the workload patterns that need optimization.
:::
## Translate the Snowflake-specific pieces
| Snowflake | In MotherDuck |
|---|---|
| Unquoted identifiers stored uppercase | DuckDB keeps the case it receives and resolves lookups case-insensitively, so queries keep working. Rename columns during the load if you want lowercase names. |
| `NUMBER(38,0)` | Snowflake's default `NUMBER` is `NUMBER(38,0)`, and transpilers carry it across as `DECIMAL(38,0)`, which DuckDB stores as a 128-bit integer: bigger on disk and slower to compute, hash, and join on. Cast integer-like columns to `BIGINT` (or the equivalent `DECIMAL(18,0)`) and keep 38 digits only where the values need them, which is almost nowhere. Go looking for this, it won't announce itself. |
| `TIMESTAMP_NTZ`, `TIMESTAMP_LTZ`, `TIMESTAMP_TZ` | DuckDB has two types, `TIMESTAMP` and `TIMESTAMPTZ`, and MotherDuck has no account-wide default timezone. In Snowflake an admin can change the account `TIMEZONE` parameter, which silently moves what `TIMESTAMP_LTZ` columns and `CURRENT_TIMESTAMP` mean for everyone, so a model can be correct against one account setting and wrong against another without its SQL changing. Pin the timezone explicitly in the models themselves rather than inheriting it. |
| `VARIANT`, `OBJECT`, `ARRAY` | Arrive as JSON strings. Cast to `JSON`, `STRUCT`, or `LIST` to query them natively. |
| Clustering keys | DuckDB has no clustering. Sorting on insert does the same job, because row-group statistics let the scan skip ranges that can't match. See [sort tables by common filter columns](/key-tasks/query-performance/#sort-tables-by-common-filter-columns). |
| Time Travel | [Database snapshots](/concepts/snapshots/) and [UNDROP](/concepts/data-recovery/) cover point-in-time restore. Retention is set by plan rather than per query, and only the Business plan can change `snapshot_retention_days`, so check the window you land on matches what your Snowflake Time Travel settings assumed. |
| Streams and Tasks | A [Flight](/concepts/flights/) on a cron schedule, or an external orchestrator for pipelines with cross-system dependencies. |
| Stored procedures and Snowpark | Python in a [Flight](/key-tasks/flights/), which runs any PyPI package next to your data. |
| Reader accounts | [Shares](/key-tasks/sharing-data/), which give read-only access to a database and work for recipients outside your organization. A MotherDuck share reaches MotherDuck users, though, so a recipient who needs to consume the data from Snowflake itself needs a file-based handoff instead. |
| Streamlit in Snowflake | [Dives](/key-tasks/dives/), React and SQL apps that live in the workspace alongside your data. |
| `GRANT SELECT ON TABLE ... TO ROLE` | Create a Share with an include pattern for the audience, then `GRANT READ ON SHARE ... TO ROLE`. See [roles and access control](/concepts/roles-and-access-control/). |
| Marketplace datasets and inbound data shares | No equivalent. Anything you consume through Snowflake's sharing graph has to be re-sourced from the provider directly, or read from a small Snowflake account you keep alive for that feed. Inventory these during assessment: they're nobody's model and so they're easy to miss until something breaks. |
| Warehouse sizing | Compute is per account, not per cluster. Size each service account's [Duckling](/about-motherduck/billing/duckling-sizes/) for the job it runs, and use [read scaling](/key-tasks/authenticating-and-connecting-to-motherduck/read-scaling/) for concurrent dashboard traffic. |
### Governance features without an equivalent
Four Snowflake governance features have no MotherDuck counterpart: **row access policies**, **column masking policies**, **per-object and per-column grants**, and **network policies**.
Access in MotherDuck is granted per Share rather than per object. [Table-level security](/key-tasks/sharing-data/table-level-security/) narrows a Share to named tables and views with an include pattern, on the Business and Enterprise plans, so the usual replacement for a row or column policy is a shape change rather than a setting: publish a Share per audience from a source that already excludes what that audience shouldn't see, or maintain a masked copy of the source database and share that. [Environment management](/key-tasks/data-warehousing/environment-management/) describes the masked-copy pattern for regulated data.
Row-level security and column-level permissions on Shares are on our roadmap, so check where they've got to before you design around their absence.
:::warning
Surface this during assessment, not at cutover. Where masking or row access policies back a compliance commitment, the replacement is a data-modelling change with a review attached, and that's weeks of lead time rather than a configuration change on the day.
:::
## Validate against Snowflake before you cut over
Run the aggregate comparison and anti-join from [Validate before you cut over](/key-tasks/data-warehousing/migrating-your-data-warehouse/#validate-before-you-cut-over). Two Snowflake-specific differences cause most mismatches:
- Snowflake's `HASH()` and DuckDB's `hash()` are different functions, so hash-based checksums disagree even when the data is identical.
- A `NUMBER` to `DECIMAL` mapping that changed scale rounds differently, and a `TIMESTAMP_LTZ` column that depended on the account `TIMEZONE` parameter lands on a different date near midnight. Check both before you go looking for a logic bug.
## Cut over and decommission
Follow the [cutover steps](/key-tasks/data-warehousing/migrating-your-data-warehouse/#cut-over-and-decommission). On the Snowflake side:
- Freeze the workload by suspending the Tasks and Snowpipes that write to it.
- Suspend the warehouse rather than dropping it. Spend stops, and you can resume if something surfaces.
- Drop the Snowflake objects only after the parallel window closes and a final `ACCOUNT_USAGE.ACCESS_HISTORY` read shows nothing querying them.
## Rethink what Snowflake shaped
Once a workload runs on MotherDuck, some of its shape was dictated by Snowflake rather than by the problem:
- PySpark jobs that existed because the transformation didn't fit in SQL often do fit, or fit in a Flight.
- A dbt Cloud subscription that only runs scheduled builds can become a [scheduled Flight](/key-tasks/flights/scheduling-and-runs/).
- Streamlit apps written to sit next to the data become [Dives](/key-tasks/dives/), which you generate from a prompt rather than hand-build.
- Warehouse right-sizing becomes per-account Duckling sizing, so a heavy backfill no longer competes with the dashboards.
Hold these until after the workload is validated. Changing the logic and the platform in the same step means a mismatch has two possible causes.
## Related
- [Tuning a migrated Snowflake workload](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-snowflake/tuning-a-migrated-snowflake-workload/) for what to fix once the SQL runs
- [Snowflake integration](/integrations/databases/snowflake/) for the table copy mechanics
- [Ingest Snowflake tables into MotherDuck from a Flight](/cookbook/flight-snowflake-ingest/)
- [Migrate your data warehouse](/key-tasks/data-warehousing/migrating-your-data-warehouse/) for the source-independent process
- [Environment management](/key-tasks/data-warehousing/environment-management/)
---
Source: https://motherduck.com/docs/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-snowflake/tuning-a-migrated-snowflake-workload
# Tune a migrated Snowflake workload
> Fix the query and dbt patterns that run on MotherDuck but run badly after a direct port from Snowflake, from JSON parsing to join fan-out to thread concurrency.
A direct port of Snowflake SQL runs on MotherDuck. It doesn't run *optimally*. Snowflake absorbs a lot of inefficiency by reaching for a bigger warehouse, and that inefficiency can become visible as memory pressure and disk spills when you try to run it on a smaller Duckling. This page covers the patterns that cause it, in rough order of how much damage they do.
Work through it after the SQL translates and the output validates, not during the port. Changing logic and platform in the same step means a wrong number has two possible causes.
:::info Prerequisites
- A workload already running on MotherDuck, with output you've checked against Snowflake. See [Migrate from Snowflake](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-snowflake/).
- [`EXPLAIN ANALYZE`](/key-tasks/query-performance/#using-explain-analyze-for-runtime-metrics) output for the models you're investigating, or [`QUERY_HISTORY`](/sql-reference/motherduck-sql-reference/md_information_schema/query_history/) rows for them.
:::
## Start by checking it's the query's fault
A model that fails with an out-of-memory error *within seconds, having spilled nothing to disk* was almost certainly starved by another job running at the same time. Re-run it on its own before you rewrite anything. Rewriting a query that was never the problem is the most common wasted day in a migration.
If it does spill and then fail, it's the query. Keep reading.
## JSON and semi-structured data
This is the single biggest cause of out-of-memory errors in Snowflake migrations, and it comes from a storage difference that isn't visible in the SQL.
Snowflake stores `VARIANT` data column-shredded internally, so parsing at query time is cheap and teams happily leave raw `VARIANT` columns in staging views. DuckDB stores JSON as text. Every parse does real work, and a staging *view* makes every downstream model and every dbt test redo that work from scratch.
- **Parse once, store typed columns.** If a staging view runs `json_extract_string(content, '$.x')`, materialize it as a table with typed columns instead, using `STRUCT` or `LIST` for anything nested. This single change usually does more than everything else on this page combined.
- **Use `UNNEST` on a typed column, not `LATERAL` over raw JSON.** Expanding raw JSON documents builds in-memory hash tables keyed on the whole document, which eat memory and spill badly. Unnesting a typed `LIST` or `STRUCT` column avoids the hash tables entirely.
- **Filters on JSON columns can't skip data.** DuckDB normally skips row groups that can't match a filter, using per-group statistics. It can't do that when the filter is on a JSON column, so the scan reads everything. Pull any column you filter on out into its own typed field.
- **Don't carry payloads you don't need.** Split high-volume message types into their own models. A general-purpose event or message-log model is frequently dominated, in both rows and bytes, by one payload type that one downstream model reads.
## Joins that explode
DuckDB's optimizer is weaker than Snowflake's on joins whose condition isn't a simple equality, and it often misjudges how many rows one will produce. A single [Duckling](/concepts/hypertenancy/) also can't brute-force its way through a billion-row intermediate result the way a Large warehouse can.
**Check key uniqueness on every join you port.** Joining to a one-to-many table just to filter rows silently duplicates those rows, which produces wrong revenue numbers rather than a crash. Use `EXISTS`, or join to a `SELECT DISTINCT` of the key:
```sql
SELECT customer_id, order_date, count(*)
FROM raw.orders
GROUP BY ALL
HAVING count(*) > 1
LIMIT 10;
```
**Replace calendar cross joins with the grain you actually need.** Crossing a daily calendar with every customer and reducing it afterwards with `DISTINCT` materializes the full product first. If the result is monthly, join to `SELECT DISTINCT month FROM calendar`, and join each customer once on a date-range condition.
**Replace self-joins for rolling windows with window functions.** A self-join on `customer_id` filtered to a 14-day lookback builds every possible pair of rows and then throws most of them away:
```sql
-- Builds every pair, then filters
SELECT a.customer_id, a.d, sum(b.amount) AS amount_14d
FROM orders a
JOIN orders b
ON b.customer_id = a.customer_id
AND b.d BETWEEN a.d - 14 AND a.d
GROUP BY ALL;
-- Same answer, one pass
SELECT
customer_id,
d,
sum(amount) OVER (
PARTITION BY customer_id
ORDER BY d
RANGE BETWEEN INTERVAL 14 DAY PRECEDING AND CURRENT ROW
) AS amount_14d
FROM orders;
```
**Use `ASOF JOIN` for latest-match lookups.** A `LEFT JOIN ... AND b.ts >= a.ts` followed by `QUALIFY row_number() = 1` multiplies rows before discarding the duplicates. `ASOF LEFT JOIN` gets there without the intermediate blowup:
```sql
SELECT e.event_id, e.ts, p.price
FROM events e
ASOF LEFT JOIN prices p
ON p.symbol = e.symbol
AND p.ts >= e.ts;
```
The two handle ties differently, so check the result. In practice the `QUALIFY` version usually wasn't handling ties deterministically either.
**Stop re-joining the same large table at every layer.** Pipelines that join a raw log again in each model, and then a reporting model that joins those results once per CTE, can spill terabytes. Add the columns you need to the lowest-level model so the layers above become plain selects, or do the join once in a `MATERIALIZED` CTE.
**Filter and aggregate before joining back.** If every downstream `sum()` needs `status = 'paid'`, put that in the base CTE rather than the final `WHERE`. Aggregate child tables by key before joining them to the parent.
**Check your `UNION`s.** `UNION` forces a full sort to deduplicate. Where the inputs are already disjoint, which is most of the time, `UNION ALL` is both correct and much cheaper.
## Timezone conversion in shared views
`convert_timezone` is cheap in Snowflake and comparatively slow in DuckDB, where it's a per-row function call. In a shared view, every downstream query that scans the table pays that cost on every row before any filter applies, which can dominate the runtime of a query that ends up returning a few thousand rows.
- If the stored timestamp already represents local midnight as UTC, `CAST(ts AS DATE)` gives the same answer for free. Confirm that holds for your data first.
- Convert the filter instead of the column. Rewrite `WHERE date_trunc('day', ts_local) = :d` as a range on the UTC column: `ts_utc >= convert_timezone(tz, 'UTC', :d) AND ts_utc < convert_timezone(tz, 'UTC', :d + 1)`. That evaluates the conversion twice rather than once per row.
- Where local-time columns are genuinely needed downstream, materialize them once instead of computing them in a view.
## dbt patterns worth auditing
**Incremental models that aren't.** Models configured as incremental in Snowflake frequently rebuild in full anyway: no `is_incremental()` filter, or a CTE that looks like it filters and doesn't. Snowflake's compute hid it. Add a real filter on a last-loaded timestamp with a lookback window, and set `unique_key`. Don't assume the Snowflake config was right, either — copying it across unchanged can make models slower.
**Append-only staging tables rebuilt from scratch.** Look for sources carrying a load-timestamp column such as `_sdc_batched_at` or `_fivetran_synced` that get rebuilt on every run. Making these incremental is usually the single biggest time saving available.
**Window functions that silently do nothing.** `lag(x IGNORE NULLS) OVER (PARTITION BY id ORDER BY updated_at)` combined with `WHERE dbt_valid_to IS NULL` gives every partition exactly one row, because a dbt snapshot has one current row per key. The result is always `NULL`, and DuckDB still sorts every partition to produce it. Compute the window over full history in a CTE, then filter to current rows — and check with the model's owner what was meant, because the Snowflake output was probably wrong too.
**Tests on views re-run the view.** Each dbt test against a view re-executes its logic, JSON parsing included. Materialize heavily tested staging models as tables.
**A plain `UNION ALL` model shouldn't spill.** If a model that only combines tables and selects columns spills heavily, set `preserve_insertion_order = false` for that build. The same setting speeds up Parquet loads from cloud storage.
**`generate_surrogate_key` is fine.** The chain of `typeof()` cases in `dbt_utils` looks expensive but is evaluated at plan time, not per row. It benchmarks the same as a plain `md5()` concatenation. Spend your time elsewhere.
## Concurrency and sizing
Compute in MotherDuck is [per account](/concepts/hypertenancy/), not per cluster, so concurrency problems are solved by splitting across accounts rather than by sizing one thing up.
**dbt threads share one Duckling.** Every thread using the same token runs on the same Duckling and shares its memory. A Snowflake multi-cluster warehouse let you set eight threads and forget about it. Here the symptoms are a small model taking minutes because it started alongside a heavy one, a model failing with `cancelled by a newer transaction step` seconds after a sibling ran out of memory, and retries failing identically. Run fewer threads, give the heavy models their own service account and therefore their own Duckling, or size a dedicated Duckling up for them.
**Size the BI Duckling separately.** BI tools running wide queries against reporting tables can hit the temporary disk spill limit. Give them their own token and Duckling, or use [read scaling](/key-tasks/authenticating-and-connecting-to-motherduck/read-scaling/), sized independently of whatever runs dbt.
**Keep one writer per database.** Covered in [Migrate from Snowflake](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-snowflake/#three-habits-to-unlearn), and it's the constraint most often behind a write that fails under orchestration.
## Benchmark it honestly
When you compare against Snowflake, make the comparison one you'd accept from someone else:
- Name the Snowflake warehouse size you're comparing against.
- Turn off `USE_CACHED_RESULT` on the Snowflake side.
- Report cold and warm runs separately rather than averaging them.
- Compare total cost across compute, storage, and seats. MotherDuck [prices storage per GB uncompressed](/about-motherduck/billing/pricing/) where Snowflake prices per compressed TB, so a single line item tells you very little on its own.
## Related
- [Migrate from Snowflake](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-snowflake/)
- [Query performance](/key-tasks/query-performance/)
- [Loading data best practices](/key-tasks/loading-data-into-motherduck/considerations-for-loading-data/)
- [Scaling patterns](/concepts/scaling-patterns/)
---
Source: https://motherduck.com/docs/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-bigquery/index
# Migrate from BigQuery
> Plan and run a migration from BigQuery to MotherDuck, from inventorying projects with INFORMATION_SCHEMA to translating GoogleSQL and cutting over.
This guide covers the BigQuery-specific steps of a migration: inventorying what your projects run, translating GoogleSQL and BigQuery features, and validating against BigQuery before you cut over. It follows the process in [Migrate your data warehouse](/key-tasks/data-warehousing/migrating-your-data-warehouse/), which covers sorting the inventory, grouping it into workloads, and moving one workload at a time. Read that page first. For the mechanics of copying a single table, see the [BigQuery integration page](/integrations/databases/bigquery/).
:::info Prerequisites
- A [MotherDuck organization](/key-tasks/managing-organizations/), with the region and plan already chosen.
- In each Google Cloud project you're migrating, the BigQuery User (`roles/bigquery.user`) and BigQuery Resource Viewer (`roles/bigquery.resourceViewer`) roles to read [job history](https://docs.cloud.google.com/bigquery/docs/information-schema-jobs), and BigQuery Metadata Viewer (`roles/bigquery.metadataViewer`) to read [table storage](https://docs.cloud.google.com/bigquery/docs/information-schema-table-storage).
- The list of regions your datasets live in. `INFORMATION_SCHEMA` views are regional, so every query below runs once per region.
:::
## Inventory what BigQuery runs
The queries below run in BigQuery, in GoogleSQL. Replace `region-us` with the region qualifier for your datasets, for example `region-eu` or `region-europe-west4`, and run them in each project.
**What objects exist, and how big are they?**
```sql
SELECT
table_schema AS dataset,
table_name,
table_type,
total_rows,
total_logical_bytes,
total_physical_bytes,
storage_last_modified_time
FROM `region-us`.INFORMATION_SCHEMA.TABLE_STORAGE
WHERE NOT deleted
ORDER BY total_logical_bytes DESC;
```
Daily sharded tables, such as the `events_YYYYMMDD` tables of a GA4 export, show up here as one row per day. Group them by name prefix before you count objects, or the inventory looks thousands of tables larger than it is.
**Which of them does anyone read?**
```sql
SELECT
ref.project_id,
ref.dataset_id,
ref.table_id,
COUNT(DISTINCT job_id) AS query_count,
MAX(creation_time) AS last_read
FROM `region-us`.INFORMATION_SCHEMA.JOBS,
UNNEST(referenced_tables) AS ref
WHERE creation_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 90 DAY)
AND job_type = 'QUERY'
GROUP BY ALL
ORDER BY query_count DESC;
```
**Who and what connects?**
```sql
SELECT
user_email,
COUNT(*) AS jobs,
SUM(total_bytes_billed) / POW(1024, 4) AS tib_billed,
MAX(creation_time) AS last_job
FROM `region-us`.INFORMATION_SCHEMA.JOBS
WHERE creation_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 90 DAY)
AND (statement_type IS NULL OR statement_type != 'SCRIPT')
GROUP BY ALL
ORDER BY jobs DESC;
```
Service accounts show up here next to people, with addresses ending in `iam.gserviceaccount.com`. Each one is a connection you'll have to repoint later, so treat this list as your cutover checklist. The `SCRIPT` filter drops the parent row of multi-statement queries, which repeats the totals of its child jobs.
**What shape is the compute?**
```sql
SELECT
TIMESTAMP_TRUNC(creation_time, HOUR) AS hour,
reservation_id,
SUM(total_slot_ms) / (1000 * 60 * 60) AS slot_hours,
SUM(total_bytes_billed) / POW(1024, 4) AS tib_billed
FROM `region-us`.INFORMATION_SCHEMA.JOBS
WHERE creation_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY)
AND job_type = 'QUERY'
AND (statement_type IS NULL OR statement_type != 'SCRIPT')
GROUP BY ALL
ORDER BY hour;
```
Slot usage that comes in two spikes a day is a batch pipeline. Usage spread evenly through business hours is serving dashboards. The two need different Duckling sizing on the other side, so it's worth knowing which is which before you size anything. A `NULL` `reservation_id` means the job ran on demand. This query attributes a job's slots to the hour it started; use `INFORMATION_SCHEMA.JOBS_TIMELINE` when you need slot usage per second.
**What runs that isn't a query?**
```sql
SELECT
routine_schema AS dataset,
routine_name,
routine_type,
external_language
FROM `region-us`.INFORMATION_SCHEMA.ROUTINES
ORDER BY routine_type, dataset;
```
Stored procedures, JavaScript UDFs, and remote functions all need rewriting, so count them early. In the [`ROUTINES` view](https://docs.cloud.google.com/bigquery/docs/information-schema-routines), `external_language` is `JAVASCRIPT` for JavaScript UDFs and `NULL` for SQL routines. Scheduled queries and Data Transfer Service jobs aren't in `INFORMATION_SCHEMA` at all. List them per region with [`bq ls --transfer_config`](https://docs.cloud.google.com/bigquery/docs/working-with-transfers), and add `--filter=dataSourceIds:scheduled_query` to see only scheduled queries:
```bash
> bq ls --transfer_config --transfer_location=us
```
:::note
The [`INFORMATION_SCHEMA.JOBS` view](https://docs.cloud.google.com/bigquery/docs/information-schema-jobs) retains 180 days of history. Pull it once at the start of the migration and refresh before cutover, so a quarterly job doesn't fall out of the window before you've seen it.
:::
:::warning
A regional `INFORMATION_SCHEMA` view only returns datasets in that region, with no error and no hint that other regions exist. If a dataset you expected is missing, query the other regions before you conclude it's unused.
:::
With the inventory in hand, [sort it into four buckets](/key-tasks/data-warehousing/migrating-your-data-warehouse/#sort-the-inventory-into-four-buckets) and [group it into workloads](/key-tasks/data-warehousing/migrating-your-data-warehouse/#group-whats-left-into-independent-workloads).
### Ingestion that only writes to BigQuery
Google's own exports land in BigQuery and nowhere else: the GA4 and Firebase exports, Google Ads and other Data Transfer Service connectors, and the Cloud Billing export. You can't repoint them at MotherDuck, so plan to keep a BigQuery project as their landing zone and copy from it on a schedule. The [GA4 BigQuery ingest Flight recipe](/cookbook/flight-ga4-bigquery-ingest/) does this for GA4 with an overlap for late-arriving events, and the [BigQuery ingest Flight recipe](/cookbook/flight-bigquery-ingest/) does it for any date-partitioned table.
Reading out of BigQuery isn't free. The `bigquery` extension reads through the BigQuery Storage Read API, which BigQuery bills by bytes read, and data leaving Google Cloud for MotherDuck's AWS region is billed as network egress. For a large one-time copy, export the tables to Google Cloud Storage as Parquet with an extract job and read them from there with a [GCS secret](/integrations/cloud-storage/google-cloud-storage/), so you pay egress once and can rerun the load without reading BigQuery again.
## Three habits to unlearn
BigQuery and MotherDuck organize data and compute differently, and the mistakes below come from carrying the BigQuery model across unchanged. Keep them in mind when you [set up the infrastructure](/key-tasks/data-warehousing/migrating-your-data-warehouse/#set-up-the-infrastructure-for-migration).
**A dataset is not a schema, and a project is not a database.** BigQuery names a table `project.dataset.table` and MotherDuck names it `database.schema.table`, so mapping one project to one database looks natural. It's usually wrong. In BigQuery the dataset is the unit of access control. In MotherDuck the database is: it's what you share, and it takes one writer at a time. Map each dataset with its own audience or its own writing pipeline to its own database. Datasets written by the same pipeline stage and read by the same people can become schemas inside one database.
**A database belongs to whoever created it, not to the project.** In BigQuery, a dataset sits in the project and anyone with a matching IAM role can query it. In MotherDuck it belongs to the user or service account that created it, and the rest of the organization can't see it until you share it. A colleague querying an unshared database gets `Catalog Error: schema "x" does not exist`, which reads like a typo rather than a permissions problem. Create production databases with a service account, because ownership can't be transferred, and publish them with a [share](/key-tasks/sharing-data/):
```sql
CREATE SHARE IF NOT EXISTS models FROM models (ACCESS RESTRICTED);
GRANT READ ON SHARE models TO ROLE explorer;
```
Granting to `explorer` reaches every preset role, because they're concentric. Grant to a [custom role](/concepts/roles-and-access-control/#custom-roles) instead when only one team should see it.
**Concurrency comes from more databases, not a bigger slot pool.** BigQuery runs every job in a project against a shared pool of slots, so 20 pipelines writing to one dataset at once is unremarkable. In MotherDuck each database takes one writer at a time and compute belongs to the account that runs the query. A pipeline fanning 20 concurrent writers at a single database (Dagster, Airflow, and Dataflow jobs all do this) needs a database per pipeline, or writes serialized into one. For the same reason, dev and production dbt targets must never point at the same database.
:::tip
Budget time after the port, not only for it. A direct translation of GoogleSQL runs on MotherDuck, but it may not run efficiently. See [Tune a migrated BigQuery workload](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-bigquery/tuning-a-migrated-bigquery-workload/) for the workload patterns that need optimization.
:::
## Translate the SQL dialect
Most GoogleSQL runs in DuckDB after a handful of mechanical changes. The ones below either fail loudly or, worse, run and return a different answer.
| GoogleSQL | In MotherDuck |
|---|---|
| `` `project.dataset.table` `` | `database.schema.table`. Backticks aren't identifier quotes in DuckDB; use double quotes where a name needs quoting. |
| `WHERE status = "paid"` | GoogleSQL accepts double-quoted strings. DuckDB reads `"paid"` as a column name and fails with `Referenced column "paid" not found`. Use single quotes for every string literal. |
| Case-sensitive table names | BigQuery table names are case-sensitive unless the dataset is set otherwise, so `Orders` and `orders` can coexist. DuckDB resolves names case-insensitively, and the two collide on load. |
| `TIMESTAMP` and `DATETIME` | The names swap meaning. A BigQuery `TIMESTAMP` is an absolute point in time, which is DuckDB's `TIMESTAMPTZ`. A BigQuery `DATETIME` has no timezone, which is DuckDB's `TIMESTAMP`. A DDL translation that keeps the type name changes what the column means. |
| `CURRENT_DATE()` and other timezone defaults | BigQuery evaluates these in UTC unless the project or organization sets `default_time_zone`. DuckDB evaluates them in the client's `TimeZone` setting, so the same model can return a different date from a laptop than from a scheduled job. Pass the timezone explicitly in the models. |
| `NUMERIC` | Maps to `DECIMAL(38,9)`, which DuckDB stores as a 128-bit integer: bigger on disk and slower to compute, hash, and join on. Use a narrower `DECIMAL`, such as `DECIMAL(18,2)` for money, where the values fit. |
| `BIGNUMERIC` | No equivalent. DuckDB decimals stop at 38 digits. Store as `DOUBLE` if you can accept the precision, or as `VARCHAR` if you can't. |
| `ARRAY` and `STRUCT` | `LIST` and `STRUCT`. BigQuery array access is 0-based with `OFFSET(n)` and 1-based with `ORDINAL(n)`. DuckDB lists are 1-based, so `arr[OFFSET(0)]` becomes `arr[1]`. Out-of-range access returns `NULL`, like `SAFE_OFFSET`. |
| `UNNEST(arr) WITH OFFSET AS i` | DuckDB has no `WITH OFFSET`. Select `unnest(arr)` next to `generate_subscripts(arr, 1)`, which numbers from 1. |
| `GEOGRAPHY` | `GEOMETRY` from the [`spatial` extension](/concepts/duckdb-extensions/). BigQuery measures on a sphere and returns meters; `ST_Distance` in DuckDB is planar, in coordinate units. Use `ST_Distance_Sphere` for meters. |
| `SELECT * EXCEPT (col)` | `SELECT * EXCLUDE (col)`. `* REPLACE` works as-is. |
| `SAFE_CAST` | `TRY_CAST`. |
| `SAFE_DIVIDE(a, b)` | `a / NULLIF(b, 0)`. Plain division by zero returns `inf` in DuckDB rather than an error or `NULL`, so a missed `SAFE_DIVIDE` produces infinite values rather than failing. |
| `DATE_DIFF(end, start, DAY)` | `date_diff('day', start, end)`. The part moves to the front and the dates swap order. Getting it wrong flips the sign of every result. |
| `DATE_TRUNC(d, MONTH)` | `date_trunc('month', d)`. On a `DATE` input DuckDB returns a `TIMESTAMP`, so cast back to `DATE` where the model expects one. |
| `FORMAT_DATE(fmt, d)` and `PARSE_DATE(fmt, s)` | `strftime(d, fmt)` and `strptime(s, fmt)::DATE`. The arguments swap order. |
| `REGEXP_CONTAINS` and `REGEXP_EXTRACT` | `regexp_matches` and `regexp_extract`. BigQuery's `REGEXP_EXTRACT` returns the capture group when the pattern has one; DuckDB's returns the whole match unless you pass the group index as the third argument. |
| `_PARTITIONTIME` and `_PARTITIONDATE` | Pseudo-columns aren't part of `SELECT *`, so they don't come across in a copy. Select them into a real column during the load if any model filters on them. |
| Wildcard tables and `_TABLE_SUFFIX` | Load the shards into one table with a date column and sort by it. If the shards stay in object storage, a glob in `read_parquet` with [Hive partitioning](/key-tasks/cloud-storage/s3-import-best-practices/#use-hive-style-partitioning-for-large-datasets) does the same job. |
## Translate the BigQuery-specific features
| BigQuery | In MotherDuck |
|---|---|
| Partitioning and clustering | DuckDB has neither. Sorting on insert does the same job, because row-group statistics let the scan skip ranges that can't match. See [sort tables by common filter columns](/key-tasks/query-performance/#sort-tables-by-common-filter-columns). `require_partition_filter` has no equivalent. |
| Time travel and fail-safe | [Database snapshots](/concepts/snapshots/) and [UNDROP](/concepts/data-recovery/) cover point-in-time restore. Retention is set by plan rather than per dataset, and only the Business plan can change `snapshot_retention_days`, so check the window you land on matches the time travel window your datasets assumed. |
| Table snapshots and clones | [Zero-copy clones](/sql-reference/motherduck-sql-reference/create-database/) with `CREATE DATABASE ... FROM`. Clones work on whole databases, not single tables. |
| Materialized views | No equivalent. Build a dbt table or incremental model and refresh it on a schedule. |
| Scheduled queries | A [Flight](/concepts/flights/) on a cron schedule, or an external orchestrator for pipelines with cross-system dependencies. |
| Scripting and stored procedures | DuckDB has no procedural SQL: no `DECLARE`, loops, or `EXECUTE IMMEDIATE`. Move the logic to Python in a [Flight](/key-tasks/flights/), which runs any PyPI package next to your data. |
| SQL UDFs | [`CREATE MACRO`](/sql-reference/duckdb-sql-reference/duckdb-statements/create-macro/), including table macros for table functions. |
| JavaScript UDFs and remote functions | No equivalent in SQL. Rewrite them in SQL where the logic allows, or run them as Python in a Flight. |
| BigQuery ML | No equivalent for training models in SQL. Train in a Flight. For LLM calls such as `AI.GENERATE`, use [`prompt()`](/sql-reference/motherduck-sql-reference/ai-functions/prompt/). |
| Dataform | dbt with dbt-duckdb. SQLX `config` blocks map to dbt model configs, and `${ref()}` becomes `{{ ref() }}`. |
| Authorized views and authorized datasets | A [share](/key-tasks/sharing-data/) per audience, narrowed with [table-level security](/key-tasks/sharing-data/table-level-security/). See [roles and access control](/concepts/roles-and-access-control/). |
| BigQuery sharing listings and public datasets | No equivalent. Anything you consume through BigQuery sharing has to be re-sourced from the provider directly, or copied on a schedule from a BigQuery project you keep for that feed. Inventory these during assessment: they're nobody's model, so they're easy to miss until something breaks. |
| Data Studio (formerly Looker Studio) | Connects to MotherDuck through the Postgres endpoint. See [Data Studio with MotherDuck](/integrations/bi-tools/looker/data-studio/). |
| Slots, reservations, and BI Engine | Compute is per account, not per pool. Size each service account's [Duckling](/about-motherduck/billing/duckling-sizes/) for the job it runs, and use [read scaling](/key-tasks/authenticating-and-connecting-to-motherduck/read-scaling/) for concurrent dashboard traffic. |
### Governance features without an equivalent
Four BigQuery governance features have no MotherDuck counterpart: **row-level access policies**, **column-level access control with policy tags**, **dynamic data masking**, and **VPC Service Controls**. Table- and column-level IAM grants have no direct equivalent either.
Access in MotherDuck is granted per Share rather than per object. [Table-level security](/key-tasks/sharing-data/table-level-security/) narrows a Share to named tables and views with an include pattern, on the Business and Enterprise plans, so the usual replacement for a row or column policy is a shape change rather than a setting: publish a Share per audience from a source that already excludes what that audience shouldn't see, or maintain a masked copy of the source database and share that. [Environment management](/key-tasks/data-warehousing/environment-management/) describes the masked-copy pattern for regulated data.
Row-level security and column-level permissions on Shares are on our roadmap, so check where they've got to before you design around their absence.
:::warning
Surface this during assessment, not at cutover. Where policy tags or row access policies back a compliance commitment, the replacement is a data-modelling change with a review attached, and that's weeks of lead time rather than a configuration change on the day.
:::
## Validate against BigQuery before you cut over
Run the aggregate comparison and anti-join from [Validate before you cut over](/key-tasks/data-warehousing/migrating-your-data-warehouse/#validate-before-you-cut-over). Three BigQuery-specific differences cause most mismatches:
- Checksums built on `FARM_FINGERPRINT`, such as `BIT_XOR(FARM_FINGERPRINT(TO_JSON_STRING(t)))`, don't carry across. DuckDB's `hash()` is a different function, so the two sides disagree even when the data is identical.
- A `TIMESTAMP` column loaded as DuckDB `TIMESTAMP` instead of `TIMESTAMPTZ`, or a `CURRENT_DATE()` that now runs in a non-UTC timezone, lands rows on a different date near midnight.
- A `NUMERIC` column narrowed to a smaller `DECIMAL` scale rounds differently from BigQuery's 9 decimal places.
Check these and the source watermark on both sides before you go looking for a logic bug.
## Cut over and decommission
Follow the [cutover steps](/key-tasks/data-warehousing/migrating-your-data-warehouse/#cut-over-and-decommission). On the BigQuery side:
- Freeze the workload by disabling the scheduled queries, transfer configs, and Dataform workflows that write to it.
- On demand, spend on compute stops when the queries stop. On a capacity edition, lower the reservation's baseline and autoscaling maximum once nothing runs against it.
- Storage keeps billing until you delete the tables. Drop them only after the parallel window closes and a final `INFORMATION_SCHEMA.JOBS` read shows nothing referencing them.
## Rethink what BigQuery shaped
Once a workload runs on MotherDuck, some of its shape was dictated by BigQuery's pricing rather than by the problem:
- Daily sharded tables and mandatory partition filters exist to limit bytes billed. One sorted table is simpler and as fast.
- Deeply nested schemas, such as the GA4 export's `event_params`, were cheap to store and query in place. Flatten the parts you use into typed columns once.
- Scheduled queries and Dataform workflows can become dbt models run by a [scheduled Flight](/key-tasks/flights/scheduling-and-runs/).
- BI Engine reservations and extract-based dashboards bought speed for BI tools. [Read scaling](/key-tasks/authenticating-and-connecting-to-motherduck/read-scaling/) and [Dives](/key-tasks/dives/) cover the same need.
Hold these until after the workload is validated. Changing the logic and the platform in the same step means a mismatch has two possible causes.
## Related
- [Tune a migrated BigQuery workload](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-bigquery/tuning-a-migrated-bigquery-workload/) for what to fix once the SQL runs
- [BigQuery integration](/integrations/databases/bigquery/) for the table copy mechanics
- [Incrementally ingest BigQuery into MotherDuck](/cookbook/flight-bigquery-ingest/)
- [Incrementally ingest GA4 BigQuery exports into MotherDuck](/cookbook/flight-ga4-bigquery-ingest/)
- [Migrate your data warehouse](/key-tasks/data-warehousing/migrating-your-data-warehouse/) for the source-independent process
- [Environment management](/key-tasks/data-warehousing/environment-management/)
---
Source: https://motherduck.com/docs/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-bigquery/tuning-a-migrated-bigquery-workload
# Tune a migrated BigQuery workload
> Fix the query and dbt patterns that run on MotherDuck but run badly after a direct port from BigQuery, from nested GA4 fields to sharded tables to dbt threads.
A direct port of GoogleSQL runs on MotherDuck. It doesn't run *optimally*. BigQuery spreads a query across as many slots as it needs, so inefficient SQL costs bytes and slot time rather than failing. On a single Duckling the same inefficiency shows up as memory pressure and disk spills. This page covers the patterns that cause it, in rough order of how much damage they do.
Work through it after the SQL translates and the output validates, not during the port. Changing logic and platform in the same step means a wrong number has two possible causes.
:::info Prerequisites
- A workload already running on MotherDuck, with output you've checked against BigQuery. See [Migrate from BigQuery](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-bigquery/).
- [`EXPLAIN ANALYZE`](/key-tasks/query-performance/#using-explain-analyze-for-runtime-metrics) output for the models you're investigating, or [`QUERY_HISTORY`](/sql-reference/motherduck-sql-reference/md_information_schema/query_history/) rows for them.
:::
## Start by checking it's the query's fault
A model that fails with an out-of-memory error *within seconds, having spilled nothing to disk* was almost certainly starved by another job running at the same time. Re-run it on its own before you rewrite anything. Rewriting a query that was never the problem is the most common wasted day in a migration.
If it does spill and then fail, it's the query. Keep reading.
## Optimize for time, not bytes
BigQuery's on-demand pricing bills bytes scanned, so BigQuery SQL is written to scan less: partition filters on every query, narrow column lists, sharded tables. MotherDuck bills Duckling time. A query that reads a whole table and finishes in two seconds is cheap, and a query that reads one partition but builds a huge intermediate join is not.
Keep the habits that also save time, such as selecting only the columns you need. Stop spending effort on the ones that only saved bytes, and look instead for the patterns below, which BigQuery's slot pool hid.
## Nested and repeated fields
This is the single biggest cause of slow models in BigQuery migrations, and the GA4 export is where you'll meet it first.
GA4 stores event parameters as a repeated record, `event_params ARRAY>>`, and the standard BigQuery idiom pulls each parameter out with its own correlated subquery:
```sql
-- One correlated subquery per parameter, per row
SELECT
event_date,
user_pseudo_id,
(SELECT p.value.string_value FROM unnest(event_params) AS u(p) WHERE p.key = 'page_location') AS page_location,
(SELECT p.value.int_value FROM unnest(event_params) AS u(p) WHERE p.key = 'ga_session_id') AS ga_session_id
FROM raw.main.ga4_events
WHERE event_name = 'page_view';
```
This runs in DuckDB, but every subquery unnests the full list again, and a staging view makes every downstream model and every dbt test repeat the work.
- **Flatten once, store typed columns.** Materialize the parameters you use as a table, and point the downstream models at it. `list_filter` reads a parameter from the list without a subquery:
```sql
CREATE OR REPLACE TABLE analytics.main.page_views AS
SELECT
event_date,
user_pseudo_id,
list_filter(event_params, lambda p: p.key = 'page_location')[1].value.string_value AS page_location,
list_filter(event_params, lambda p: p.key = 'ga_session_id')[1].value.int_value AS ga_session_id
FROM raw.main.ga4_events
WHERE event_name = 'page_view'
ORDER BY event_date;
```
This single change usually does more than everything else on this page combined.
- **Don't carry payloads you don't need.** Split high-volume event names into their own models. A general-purpose events model is frequently dominated, in both rows and bytes, by one event type that one downstream model reads.
- **Parse JSON strings once, too.** Columns of JSON text read with `JSON_EXTRACT_SCALAR` in BigQuery become `json_extract_string` calls in DuckDB, and DuckDB parses the text on every read. Filters on JSON can't skip row groups either, so the scan reads everything. Extract the fields you use, and every field you filter on, into typed columns.
## Sharded tables and partition filters
A BigQuery workload often reads daily shards with `FROM events_* WHERE _TABLE_SUFFIX BETWEEN ...`, or relies on `_PARTITIONDATE` filters for pruning. Neither survives the move as a pruning mechanism.
- **Consolidate shards into one table, sorted by date.** DuckDB keeps min/max statistics per row group, so a table loaded in date order skips the row groups a date filter can't match. That's the same pruning partitioning gave you, without one table per day.
- **Replace pseudo-columns with real ones.** A model that filters on `_PARTITIONTIME` needs that value as a stored column, and the sort order on insert should follow the column you filter on most.
- **Drop the defensive partition filters that only limited cost.** A `WHERE event_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 3 DAY)` that existed to stop a full scan from being billed can go, unless the model's logic needs it. Keep the ones that make incremental models incremental.
## Joins that explode
BigQuery shuffles a large join across hundreds or thousands of slots. A single [Duckling](/concepts/hypertenancy/) can't brute-force its way through a billion-row intermediate result the same way, and DuckDB's optimizer often misjudges how many rows a join whose condition isn't a simple equality will produce.
**Check key uniqueness on every join you port.** Joining to a one-to-many table to filter rows silently duplicates those rows, which produces wrong revenue numbers rather than a crash. Use `EXISTS`, or join to a `SELECT DISTINCT` of the key:
```sql
SELECT customer_id, order_date, count(*)
FROM raw.orders
GROUP BY ALL
HAVING count(*) > 1
LIMIT 10;
```
**Replace the `ARRAY_AGG` deduplication idiom.** BigQuery code often keeps the latest row per key with `ARRAY_AGG(t ORDER BY updated_at DESC LIMIT 1)[OFFSET(0)]`. DuckDB doesn't accept `LIMIT` inside an aggregate. Use `arg_max`, which keeps one row per key without collecting the rest into a list:
```sql
-- Latest row per customer, as a struct
SELECT customer_id, arg_max(c, updated_at) AS latest
FROM raw.customers AS c
GROUP BY customer_id;
-- Or keep the columns flat
SELECT *
FROM raw.customers
QUALIFY row_number() OVER (PARTITION BY customer_id ORDER BY updated_at DESC) = 1;
```
**Replace self-joins for rolling windows with window functions.** A self-join on `customer_id` filtered to a 14-day lookback builds every possible pair of rows and then throws most of them away:
```sql
-- Builds every pair, then filters
SELECT a.customer_id, a.d, sum(b.amount) AS amount_14d
FROM orders a
JOIN orders b
ON b.customer_id = a.customer_id
AND b.d BETWEEN a.d - 14 AND a.d
GROUP BY ALL;
-- Same answer, one pass
SELECT
customer_id,
d,
sum(amount) OVER (
PARTITION BY customer_id
ORDER BY d
RANGE BETWEEN INTERVAL 14 DAY PRECEDING AND CURRENT ROW
) AS amount_14d
FROM orders;
```
**Use `ASOF JOIN` for latest-match lookups.** A `LEFT JOIN ... AND b.ts >= a.ts` followed by `QUALIFY row_number() = 1` multiplies rows before discarding the duplicates. `ASOF LEFT JOIN` gets there without the intermediate blowup:
```sql
SELECT e.event_id, e.ts, p.price
FROM events e
ASOF LEFT JOIN prices p
ON p.symbol = e.symbol
AND p.ts >= e.ts;
```
The two handle ties differently, so check the result.
**Filter and aggregate before joining back.** Aggregate child tables by key before joining them to the parent, and put filters every downstream aggregate needs in the base CTE rather than the final `WHERE`. Where several layers join the same large table again, add the columns to the lowest-level model once, or do the join in a `MATERIALIZED` CTE.
**Check your `UNION`s.** GoogleSQL makes you write `UNION DISTINCT` or `UNION ALL` explicitly, but a translator that maps `UNION DISTINCT` to `UNION` keeps a full deduplication. Where the inputs are already disjoint, `UNION ALL` is both correct and much cheaper.
## Timezone conversion in shared views
BigQuery code converts timezones inline, with `DATETIME(ts, 'Europe/Amsterdam')` or `DATE(ts, tz)`, often in a shared view. In DuckDB the conversion is a per-row function call, and in a view every downstream query pays it on every row before any filter applies.
- Convert the filter instead of the column. Rewrite a filter on a local-date expression as a range on the stored `TIMESTAMPTZ` column, computed from the local day's boundaries once.
- Where local-time columns are genuinely needed downstream, materialize them once instead of computing them in a view.
## dbt patterns worth auditing
**`insert_overwrite` has no direct equivalent.** dbt-bigquery's usual incremental strategy replaces whole partitions. dbt-duckdb supports `append`, `delete+insert`, `merge`, and `microbatch`. To replace whole days, use `delete+insert` with the date column as `unique_key`: every date in the new batch is deleted and rewritten.
```sql
{{ config(
materialized='incremental',
incremental_strategy='delete+insert',
unique_key='event_date'
) }}
SELECT *
FROM {{ ref('stg_events') }}
{% if is_incremental() %}
WHERE event_date >= (SELECT max(event_date) - INTERVAL 3 DAY FROM {{ this }})
{% endif %}
ORDER BY event_date
```
**Remove BigQuery-only configs.** `partition_by`, `cluster_by`, `require_partition_filter`, `partition_expiration_days`, and `labels` do nothing on MotherDuck. Delete them so nobody assumes they still apply, and put the sort order in the model's SQL instead.
**Incremental models that aren't.** Models configured as incremental frequently rebuild in full anyway: no `is_incremental()` filter, or a filter that BigQuery's partition pruning made cheap enough that nobody noticed. Add a real filter on a load timestamp with a lookback window. Sources that carry a load-timestamp column, such as `_fivetran_synced` or `_sdc_batched_at`, and get rebuilt on every run are usually the single biggest time saving available.
**Tests on views re-run the view.** Each dbt test against a view re-executes its logic, nested-field unnesting included. Materialize heavily tested staging models as tables.
## Concurrency and sizing
Compute in MotherDuck is [per account](/concepts/hypertenancy/), not per slot pool, so concurrency problems are solved by splitting across accounts rather than by buying more slots.
**dbt threads share one Duckling.** Every thread using the same token runs on the same Duckling and shares its memory. BigQuery projects routinely run dbt with 16 or more threads, because the slot pool absorbs them. Here the symptoms are a small model taking minutes because it started alongside a heavy one, a model failing with `cancelled by a newer transaction step` seconds after a sibling ran out of memory, and retries failing identically. Run fewer threads, give the heavy models their own service account and therefore their own Duckling, or size a dedicated Duckling up for them.
**Size the BI Duckling separately.** Dashboards that ran on BI Engine or the shared slot pool can hit the temporary disk spill limit when they run wide queries against reporting tables. Give them their own token and Duckling, or use [read scaling](/key-tasks/authenticating-and-connecting-to-motherduck/read-scaling/), sized independently of whatever runs dbt.
**Keep one writer per database.** Covered in [Migrate from BigQuery](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-bigquery/#three-habits-to-unlearn), and it's the constraint most often behind a write that fails under orchestration.
## Benchmark it honestly
When you compare against BigQuery, make the comparison one you'd accept from someone else:
- Name the pricing model you're comparing against: on demand, or an edition with its reservation size.
- Turn off cached results on the BigQuery side, with `--nouse_cache` in the `bq` CLI or the query settings in the console.
- Report cold and warm runs separately rather than averaging them.
- Compare total cost across compute, storage, and seats. BigQuery bills storage on logical or physical bytes depending on each dataset's storage billing model, so check which one applies before you compare storage lines.
## Related
- [Migrate from BigQuery](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-bigquery/)
- [Query performance](/key-tasks/query-performance/)
- [Loading data best practices](/key-tasks/loading-data-into-motherduck/considerations-for-loading-data/)
- [Scaling patterns](/concepts/scaling-patterns/)
---
Source: https://motherduck.com/docs/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-redshift/tuning-a-migrated-redshift-workload
# Tune a migrated Redshift workload
> Fix the query and dbt patterns that run on MotherDuck but run badly after a direct port from Redshift, from distribution-key joins to staging-table upserts to dbt threads.
A direct port of Redshift SQL runs on MotherDuck. It doesn't run *optimally*. Redshift spreads a query across the slices of every node in the cluster, and its distribution keys put matching join rows on the same slice before the query starts. On a single Duckling there are no slices to spread across, so a join that Redshift's layout made cheap shows up as memory pressure and disk spills. This page covers the patterns that cause it, in rough order of how much damage they do.
Work through it after the SQL translates and the output validates, not during the port. Changing logic and platform in the same step means a wrong number has two possible causes.
:::info Prerequisites
- A workload already running on MotherDuck, with output you've checked against Redshift. See [Migrate from Redshift](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-redshift/).
- [`EXPLAIN ANALYZE`](/key-tasks/query-performance/#using-explain-analyze-for-runtime-metrics) output for the models you're investigating, or [`QUERY_HISTORY`](/sql-reference/motherduck-sql-reference/md_information_schema/query_history/) rows for them.
:::
## Start by checking it's the query's fault
A model that fails with an out-of-memory error *within seconds, having spilled nothing to disk* was almost certainly starved by another job running at the same time. Re-run it on its own before you rewrite anything. Rewriting a query that was never the problem is the most common wasted day in a migration.
If it does spill and then fail, it's the query. Keep reading.
## Sort keys become sort order on insert
Redshift skips blocks with zone maps: min and max values per 1 MB block, ordered by the table's sort key. DuckDB keeps the same min and max statistics per row group, so the mechanism carries over. The declaration doesn't. There's no `SORTKEY` clause, so the order has to come from the `ORDER BY` of whatever writes the table.
- **Put the sort key in the load.** Write each table with `ORDER BY` on the column its queries filter on most, usually the first column of the old compound sort key. See [sort tables by common filter columns](/key-tasks/query-performance/#sort-tables-by-common-filter-columns).
- **Pick one column for an interleaved sort key.** DuckDB has no interleaved order. Sort by the column with the most selective common filter, and check the others with `EXPLAIN ANALYZE` before you add anything.
- **Re-sort what you append to.** `VACUUM` re-sorted the unsorted region of a Redshift table in the background. Nothing re-sorts a DuckDB table after the fact, so a table that receives out-of-order appends gradually loses its pruning. Rebuild it periodically with `CREATE OR REPLACE TABLE ... AS SELECT * FROM ... ORDER BY ...`, or make sure each append arrives in order.
Delete the `VACUUM`, `ANALYZE`, and deep-copy jobs once the tables they maintained have moved. They have no counterpart to run.
## Joins that explode
Redshift's distribution keys co-locate the rows a join needs, and `DISTSTYLE ALL` copies small dimension tables to every node. A single [Duckling](/concepts/hypertenancy/) can't brute-force its way through a billion-row intermediate result the same way, and DuckDB's optimizer often misjudges how many rows a join whose condition isn't a simple equality will produce.
**Check key uniqueness on every join you port.** Redshift doesn't enforce primary key or unique constraints, so a table declared with a primary key can still hold duplicate keys, and many do. Joining to it to look up one row silently duplicates rows, which produces wrong revenue numbers rather than a crash. Check before you trust the declaration:
```sql
SELECT customer_id, order_date, count(*)
FROM raw.orders
GROUP BY ALL
HAVING count(*) > 1
LIMIT 10;
```
Where the duplicates are real, deduplicate once in a staging model with `QUALIFY row_number() OVER (PARTITION BY customer_id ORDER BY updated_at DESC) = 1`, and join to that. Use `EXISTS` where the join only filters rows.
**Replace self-joins for rolling windows with window functions.** A self-join on `customer_id` filtered to a 14-day lookback builds every possible pair of rows and then throws most of them away. Redshift absorbed this when `customer_id` was the distribution key. DuckDB doesn't:
```sql
-- Builds every pair, then filters
SELECT a.customer_id, a.d, sum(b.amount) AS amount_14d
FROM orders a
JOIN orders b
ON b.customer_id = a.customer_id
AND b.d BETWEEN a.d - 14 AND a.d
GROUP BY ALL;
-- Same answer, one pass
SELECT
customer_id,
d,
sum(amount) OVER (
PARTITION BY customer_id
ORDER BY d
RANGE BETWEEN INTERVAL 14 DAY PRECEDING AND CURRENT ROW
) AS amount_14d
FROM orders;
```
**Use `ASOF JOIN` for latest-match lookups.** A `LEFT JOIN ... AND b.ts >= a.ts` followed by `QUALIFY row_number() = 1` multiplies rows before discarding the duplicates. `ASOF LEFT JOIN` gets there without the intermediate blowup:
```sql
SELECT e.event_id, e.ts, p.price
FROM events e
ASOF LEFT JOIN prices p
ON p.symbol = e.symbol
AND p.ts >= e.ts;
```
The two handle ties differently, so check the result.
**Filter and aggregate before joining back.** Aggregate child tables by key before joining them to the parent, and put filters every downstream aggregate needs in the base CTE rather than the final `WHERE`. Where several layers join the same large table again, add the columns to the lowest-level model once, or do the join in a `MATERIALIZED` CTE.
**Check your `UNION`s.** `UNION` deduplicates, which costs a full hash of every row. Where the inputs are already disjoint, `UNION ALL` is both correct and much cheaper.
## Staging-table upserts
Redshift code written before `MERGE` arrived upserts in a stored procedure: create a temp table, `COPY` or `INSERT` into it, `DELETE` the matching rows from the target, `INSERT` the new ones, and wrap it all in a transaction. It ports to DuckDB as-is, but it writes the batch twice and deletes rows the next statement puts back. Use `MERGE INTO`:
```sql
MERGE INTO analytics.main.customers AS t
USING staging.main.customers_batch AS s
ON t.customer_id = s.customer_id
WHEN MATCHED THEN UPDATE SET *
WHEN NOT MATCHED THEN INSERT *;
```
In dbt, the same pattern is an incremental model with `incremental_strategy='merge'`.
## SUPER and JSON
`SUPER` columns arrive as JSON after a Parquet `UNLOAD`. PartiQL navigation like `payload.items[0].sku` becomes a `json_extract_string` call, and DuckDB parses the text on every read. Filters on JSON can't skip row groups either, so the scan reads everything.
- **Flatten once, store typed columns.** Extract the fields you use, and every field you filter on, into a table with typed columns, and point the downstream models at it.
- **Unnest arrays once.** A PartiQL `FROM t, t.payload.items AS i` becomes `unnest`. Do it in one staging model rather than in every model that needs the items.
- **Don't carry payloads you don't need.** Split high-volume event types into their own models. A general-purpose events model is frequently dominated, in both rows and bytes, by one event type that one downstream model reads.
## Timezone conversion in shared views
Redshift code converts timezones inline with `CONVERT_TIMEZONE`, often in a shared view. In DuckDB the conversion is a per-row function call, and in a view every downstream query pays it on every row before any filter applies.
- Convert the filter instead of the column. Rewrite a filter on a local-date expression as a range on the stored `TIMESTAMPTZ` column, computed from the local day's boundaries once.
- Where local-time columns are genuinely needed downstream, materialize them once instead of computing them in a view.
## dbt patterns worth auditing
**Remove Redshift-only configs.** `dist`, `sort`, `sort_type`, `bind`, `backup`, and `auto_refresh` do nothing on MotherDuck. Delete them so nobody assumes they still apply, and put the sort order in the model's SQL instead.
**Incremental strategies carry over.** dbt-redshift and dbt-duckdb both support `append`, `delete+insert`, `merge`, and `microbatch`, so incremental models keep their strategy. Add an `ORDER BY` on the sort column to the model's SQL, because the old `sort` config did that job.
**Incremental models that aren't.** Models configured as incremental frequently rebuild in full anyway: no `is_incremental()` filter, or a filter that Redshift's zone maps made cheap enough that nobody noticed. Add a real filter on a load timestamp with a lookback window. Sources that carry a load-timestamp column, such as `_fivetran_synced` or `_sdc_batched_at`, and get rebuilt on every run are usually the single biggest time saving available.
**Tests on views re-run the view.** Each dbt test against a view re-executes its logic, JSON parsing included. Materialize heavily tested staging models as tables.
## Concurrency and sizing
Compute in MotherDuck is [per account](/concepts/hypertenancy/), not per cluster, so concurrency problems are solved by splitting across accounts rather than by adding WLM queues, concurrency scaling, or RPUs.
**dbt threads share one Duckling.** Every thread using the same token runs on the same Duckling and shares its memory. On Redshift, WLM queued the threads beyond its slot count and concurrency scaling picked up the overflow. Here the symptoms are a small model taking minutes because it started alongside a heavy one, a model failing with `cancelled by a newer transaction step` seconds after a sibling ran out of memory, and retries failing identically. Run fewer threads, give the heavy models their own service account and therefore their own Duckling, or size a dedicated Duckling up for them.
**Size the BI Duckling separately.** WLM queues often existed to keep dashboards away from the ETL. The MotherDuck equivalent is a separate token and Duckling for the BI tool, or [read scaling](/key-tasks/authenticating-and-connecting-to-motherduck/read-scaling/), sized independently of whatever runs dbt.
**Keep one writer per database.** Covered in [Migrate from Redshift](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-redshift/#three-habits-to-unlearn), and it's the constraint most often behind a write that fails under orchestration.
## Benchmark it honestly
When you compare against Redshift, make the comparison one you'd accept from someone else:
- Name the configuration you're comparing against: node type and count for a provisioned cluster, or base and maximum RPUs for Serverless.
- Turn off the result cache on the Redshift side with `SET enable_result_cache_for_session TO off`.
- Report cold and warm runs separately rather than averaging them.
- Say whether concurrency scaling ran during the test, because it changes both latency and cost.
- Compare total cost across compute, storage, and seats, including reserved-node commitments.
## Related
- [Migrate from Redshift](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-redshift/)
- [Query performance](/key-tasks/query-performance/)
- [Loading data best practices](/key-tasks/loading-data-into-motherduck/considerations-for-loading-data/)
- [Scaling patterns](/concepts/scaling-patterns/)
---
Source: https://motherduck.com/docs/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-redshift/index
# Migrate from Redshift
> Plan and run a migration from Amazon Redshift to MotherDuck, from inventorying a cluster with system views to translating Redshift SQL and cutting over.
This guide covers the Redshift-specific steps of a migration: inventorying what your cluster or Serverless workgroup runs, translating Redshift SQL and features, and validating against Redshift before you cut over. It follows the process in [Migrate your data warehouse](/key-tasks/data-warehousing/migrating-your-data-warehouse/), which covers sorting the inventory, grouping it into workloads, and moving one workload at a time. Read that page first. For the mechanics of copying a single table, see the [Amazon Redshift integration page](/integrations/databases/amazon-redshift/).
:::info Prerequisites
- A [MotherDuck organization](/key-tasks/managing-organizations/), ideally in the same [AWS region](/about-motherduck/cloud-regions/) as your Redshift cluster, so the data you unload never leaves the region.
- A Redshift superuser. Other users see only their own rows in the `SYS_` views, and `SVV_TABLE_INFO` is visible only to superusers unless you grant `SELECT` on it.
- An S3 bucket and an IAM role that Redshift can `UNLOAD` to.
:::
## Inventory what Redshift runs
The queries below run in Redshift. They use the `SVV_` and `SYS_` system views, which work on both provisioned clusters and Redshift Serverless. Run them in each database you're migrating.
**What objects exist, and how big are they?**
```sql
SELECT
"database",
"schema",
"table",
tbl_rows,
size AS size_mb,
diststyle,
sortkey1,
unsorted,
stats_off
FROM svv_table_info
ORDER BY size DESC;
```
`SVV_TABLE_INFO` lists only tables that hold data, and `size` counts 1 MB blocks. Views and external tables are in `SVV_TABLES` and `SVV_EXTERNAL_TABLES`. Keep `diststyle` and `sortkey1`: the sort key tells you the `ORDER BY` each table needs on the MotherDuck side.
**Which of them does anyone read?**
```sql
SELECT
table_name,
COUNT(DISTINCT query_id) AS query_count,
MAX(start_time) AS last_read
FROM sys_query_detail
WHERE step_name = 'scan'
AND table_name IS NOT NULL
GROUP BY table_name
ORDER BY query_count DESC;
```
**Who and what connects?**
```sql
SELECT
u.usename AS user_name,
COUNT(*) AS queries,
MAX(q.start_time) AS last_query
FROM sys_query_history AS q
JOIN pg_user AS u
ON u.usesysid = q.user_id
GROUP BY u.usename
ORDER BY queries DESC;
```
Pipeline users and BI tool users show up here next to people. Each one is a connection you'll have to repoint later, so treat this list as your cutover checklist.
**What shape is the compute?**
```sql
SELECT
DATE_TRUNC('hour', start_time) AS hour,
COUNT(*) AS queries,
SUM(elapsed_time) / 1000000.0 / 3600 AS elapsed_hours,
SUM(queue_time) / 1000000.0 / 3600 AS queued_hours
FROM sys_query_history
GROUP BY 1
ORDER BY hour;
```
Elapsed time that comes in two spikes a day is a batch pipeline. Elapsed time spread evenly through business hours is serving dashboards. The two need different Duckling sizing on the other side, so it's worth knowing which is which before you size anything. Queue time is the WLM backlog: hours where it's high are where concurrency scaling or Serverless capacity was doing work, and where you'll want separate Ducklings. On Serverless, `SYS_SERVERLESS_USAGE` gives the billed RPU seconds per minute in `charged_seconds`.
**What runs that isn't a query?**
```sql
SELECT
n.nspname AS schema_name,
p.proname AS routine_name,
l.lanname AS language
FROM pg_proc AS p
JOIN pg_namespace AS n
ON n.oid = p.pronamespace
JOIN pg_language AS l
ON l.oid = p.prolang
WHERE n.nspname NOT IN ('pg_catalog', 'information_schema')
ORDER BY language, schema_name;
```
`plpgsql` rows are stored procedures, `plpythonu` rows are Python UDFs, `exfunc` rows are Lambda UDFs, and `sql` rows are SQL UDFs. All but the SQL UDFs need rewriting, so count them early. Then list what else is attached to the cluster:
| System view | What it lists |
|---|---|
| `SVV_MV_INFO` | Materialized views, including the ones that do streaming ingestion |
| `SVV_EXTERNAL_TABLES` | Redshift Spectrum tables, which already live in S3 |
| `SVV_DATASHARES` | Datashares you produce and consume |
| `SVV_INTEGRATION` | Zero-ETL integrations that write into the cluster |
Scheduled queries aren't in the system views. Query Editor v2 stores each schedule as an Amazon EventBridge rule whose name starts with `QS2-`, and keeps only 24 hours of run history, so list the rules with `aws events list-rules --name-prefix QS2-` in each region.
:::note
Redshift keeps about a week of query history: the `STL_` views and `SYS_SERVERLESS_USAGE` keep seven days. A weekly job can fall out of the window before you see it, and a monthly one almost certainly will. Copy the history into a table every day from the start of the migration, or turn on [audit logging](https://docs.aws.amazon.com/redshift/latest/mgmt/db-auditing.html) to S3 and read the user activity log, which keeps whatever your bucket keeps.
:::
With the inventory in hand, [sort it into four buckets](/key-tasks/data-warehousing/migrating-your-data-warehouse/#sort-the-inventory-into-four-buckets) and [group it into workloads](/key-tasks/data-warehousing/migrating-your-data-warehouse/#group-whats-left-into-independent-workloads).
### Ingestion that only writes to Redshift
Most Redshift ingestion already passes through S3, which makes it the easiest part to move:
- **`COPY` and auto-copy jobs** read files from S3. MotherDuck reads the same prefix with `read_parquet` or `read_csv`, so point a [scheduled Flight](/cookbook/flight-scheduled-s3-ingest/) at it and stop the `COPY`.
- **Spectrum tables** are already in S3 and registered in a data catalog, usually AWS Glue. Query them in place, as described on the [Redshift integration page](/integrations/databases/amazon-redshift/#read-spectrum-tables-in-place).
- **Zero-ETL integrations** replicate Aurora, RDS, DynamoDB, and applications such as Salesforce, SAP, ServiceNow, and Zendesk into Redshift and nowhere else. For Aurora and RDS for PostgreSQL or MySQL, attach the source database with the PostgreSQL or MySQL extension from a [Flight](/cookbook/flight-postgres-ingest/). For DynamoDB, export the table to S3 and read the export. Applications need a replacement connector, such as [dlt](/integrations/ingestion/dlt/) or [Fivetran](/integrations/ingestion/fivetran/).
- **Streaming ingestion** from Kinesis Data Streams or Amazon MSK lands in a materialized view. Send the stream to S3 with Amazon Data Firehose instead, and read the files.
To copy the data that has to move, `UNLOAD` it to S3 as Parquet and read it from MotherDuck. The [integration page](/integrations/databases/amazon-redshift/#unload-to-s3-and-read-the-files) has the commands. With the bucket and your MotherDuck organization in the same region, nothing in the path pays cross-region transfer.
## Three habits to unlearn
Redshift and MotherDuck organize data and compute differently, and the mistakes below come from carrying the Redshift model across unchanged. Keep them in mind when you [set up the infrastructure](/key-tasks/data-warehousing/migrating-your-data-warehouse/#set-up-the-infrastructure-for-migration).
**A Redshift schema is often a MotherDuck database.** Redshift names a table `database.schema.table` and so does MotherDuck, so a one-to-one mapping looks natural. It's usually wrong. Most Redshift clusters keep everything in one database and separate teams and pipeline stages by schema, with grants per schema. In MotherDuck the database is the unit you share, and it takes one writer at a time. Map each schema with its own audience or its own writing pipeline to its own database. Schemas written by the same pipeline stage and read by the same people can stay schemas inside one database.
**There's no superuser who can see everything.** In Redshift, a superuser can read every table and reassign ownership with `ALTER TABLE ... OWNER TO`. In MotherDuck a database belongs to the user or service account that created it, nobody else can see it until you share it, and ownership can't be transferred. A colleague querying an unshared database gets `Catalog Error: schema "x" does not exist`, which reads like a typo rather than a permissions problem. Create production databases with a service account, and publish them with a [share](/key-tasks/sharing-data/):
```sql
CREATE SHARE IF NOT EXISTS models FROM models (ACCESS RESTRICTED);
GRANT READ ON SHARE models TO ROLE explorer;
```
Granting to `explorer` reaches every preset role, because they're concentric. Grant to a [custom role](/concepts/roles-and-access-control/#custom-roles) instead when only one team should see it.
**Concurrency comes from more databases, not more WLM slots.** Redshift absorbs concurrent writers in one cluster, queues the overflow in WLM, and adds concurrency scaling clusters when the queue grows. In MotherDuck each database takes one writer at a time and compute belongs to the account that runs the query. A pipeline fanning 20 concurrent writers at a single database (Dagster, Airflow, and Kinesis consumers all do this) needs a database per pipeline, or writes serialized into one. For the same reason, dev and production dbt targets must never point at the same database.
:::tip
Budget time after the port, not only for it. A direct translation of Redshift SQL runs on MotherDuck, but it may not run efficiently. See [Tune a migrated Redshift workload](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-redshift/tuning-a-migrated-redshift-workload/) for the workload patterns that need optimization.
:::
## Translate the SQL dialect
Redshift SQL descends from PostgreSQL, and so does much of DuckDB's syntax, so most queries run unchanged. The differences below either fail loudly or, worse, run and return a different answer.
| Redshift | In MotherDuck |
|---|---|
| Integer division | `7 / 2` returns `3` in Redshift and `3.5` in DuckDB, where `/` always returns a `DOUBLE`. Every ratio, bucket, and percentage built on integer division changes without an error. Use `//` where the model relies on truncation. |
| `DISTKEY`, `SORTKEY`, `DISTSTYLE`, `ENCODE`, `BACKUP NO` | Delete them from the DDL. Put the sort key in the `ORDER BY` of the load instead, because row-group statistics do the job of zone maps. See [sort tables by common filter columns](/key-tasks/query-performance/#sort-tables-by-common-filter-columns). |
| `PRIMARY KEY`, `UNIQUE`, and `FOREIGN KEY` | Redshift treats these as hints and doesn't enforce them, so its tables often hold rows that violate them. DuckDB enforces them, and the load fails with `PRIMARY KEY or UNIQUE constraint violation`. Drop the constraints from the DDL, and test for duplicates instead. |
| `VARCHAR(n)` | Redshift counts `n` in bytes, so multi-byte UTF-8 text fits fewer characters than the declaration suggests. DuckDB ignores the length entirely and never truncates. Declare plain `VARCHAR`. |
| Trailing spaces in comparisons | Redshift ignores trailing blanks when it compares `CHAR` and `VARCHAR` values, so `'a ' = 'a'` is true. In DuckDB it's false, and joins on padded keys silently drop rows. `trim` the keys during the load. |
| `TIMESTAMPTZ` columns in an `UNLOAD` | Parquet unloads keep the timestamp value but drop the timezone, so the column arrives as a plain `TIMESTAMP` holding UTC. Cast it back during the load with `ts AT TIME ZONE 'UTC'`. |
| `IDENTITY(seed, step)` | A sequence and a default: `CREATE SEQUENCE order_id_seq;` then `order_id BIGINT DEFAULT nextval('order_id_seq')`. Start the sequence above the highest key you've loaded. |
| `GETDATE()`, `SYSDATE` | `current_timestamp` or `now()`. Redshift sessions default to UTC. DuckDB evaluates these in the client's `TimeZone` setting, so the same model can return a different date from a laptop than from a scheduled job. Pass the timezone explicitly in the models. |
| `CONVERT_TIMEZONE('UTC', 'Europe/Amsterdam', ts)` | `(ts AT TIME ZONE 'UTC') AT TIME ZONE 'Europe/Amsterdam'`. `TIMESTAMP` and `TIMESTAMPTZ` mean the same thing in both engines, so the column types carry over. |
| `DATEADD(day, 1, d)` | `d + INTERVAL 1 DAY`. On a `DATE` input DuckDB returns a `TIMESTAMP`, so cast back to `DATE` where the model expects one. |
| `DATEDIFF(day, start, end)` | `date_diff('day', start, end)`. Same argument order, and both count boundaries crossed, so only the quotes around the part change. |
| `NVL`, `NVL2`, `DECODE` | `coalesce` and `CASE`. DuckDB has no `NVL`. Its `decode` is a different function that converts a `BLOB` to text, so the error message doesn't point at the real problem. |
| `LISTAGG(x, ',') WITHIN GROUP (ORDER BY y)` | `string_agg(x, ',' ORDER BY y)`. DuckDB has `listagg` but rejects the `WITHIN GROUP` form. |
| `APPROXIMATE COUNT(DISTINCT x)` | `approx_count_distinct(x)`. |
| `SELECT TOP 10` | `SELECT ... LIMIT 10`. |
| `SIMILAR TO` | DuckDB treats the pattern as a regular expression, so `%` and `_` are literal characters. `'abc' SIMILAR TO 'a%'` is true in Redshift and false in DuckDB, with no error. Rewrite as `LIKE` or `regexp_full_match`. |
| `REGEXP_SUBSTR`, `CHARINDEX` | `regexp_extract` and `strpos`. |
| `JSON_EXTRACT_PATH_TEXT(j, 'a', 'b')` | `json_extract_string(j, '$.a.b')`. DuckDB's `json_extract_path_text` takes a single path, so the multi-key form fails. |
| `SUPER` and PartiQL | `SUPER` unloads to Parquet as JSON. Cast it to `JSON`, or to a `STRUCT` or `LIST` when the shape is fixed. PartiQL arrays are 0-based and DuckDB lists are 1-based, so `items[0]` becomes `items[1]`. DuckDB returns `NULL` for `items[0]` rather than an error. |
| `GEOMETRY`, `GEOGRAPHY` | `GEOMETRY` from the [`spatial` extension](/concepts/duckdb-extensions/). `GEOMETRY` can't be unloaded to Parquet, so unload it as text or CSV and parse it on load. |
| `VARBYTE` | `BLOB`. Like `GEOMETRY`, it unloads only to text or CSV. |
| `HLLSKETCH` | No equivalent, and it can't be unloaded to Parquet. Recompute the distinct counts from the base rows with `approx_count_distinct`. |
| `CREATE VIEW ... WITH NO SCHEMA BINDING` | Remove the clause. DuckDB doesn't accept it, and views re-resolve their tables at query time anyway. |
## Translate the Redshift-specific features
| Redshift | In MotherDuck |
|---|---|
| Spectrum external tables | Read the S3 files in place with `read_parquet`, or attach the AWS Glue Data Catalog for Iceberg tables. See [read Spectrum tables in place](/integrations/databases/amazon-redshift/#read-spectrum-tables-in-place). |
| Federated queries to RDS and Aurora | The PostgreSQL and MySQL extensions, from a DuckDB client or a [Flight](/cookbook/flight-postgres-ingest/). See [loading data from PostgreSQL or MySQL](/key-tasks/loading-data-into-motherduck/loading-data-from-postgres/). |
| Materialized views | No equivalent. Build a dbt table or incremental model and refresh it on a schedule. |
| Scheduled queries | A [Flight](/concepts/flights/) on a cron schedule, or an external orchestrator for pipelines with cross-system dependencies. |
| Stored procedures | DuckDB has no procedural SQL: no PL/pgSQL, loops, or dynamic SQL. Move the logic to Python in a [Flight](/key-tasks/flights/), which runs any PyPI package next to your data. |
| SQL UDFs | [`CREATE MACRO`](/sql-reference/duckdb-sql-reference/duckdb-statements/create-macro/). Redshift SQL UDFs refer to arguments as `$1`, `$2`; macros use named parameters. |
| Python UDFs and Lambda UDFs | No equivalent in SQL. Rewrite them in SQL where the logic allows, or run them as Python in a Flight. Redshift ended support for Python UDFs on June 30, 2026, so any still in your inventory are already broken. |
| Redshift ML | No equivalent for training models in SQL. Train in a Flight. For LLM calls through Bedrock external models, use [`prompt()`](/sql-reference/motherduck-sql-reference/ai-functions/prompt/). |
| Datashares | [Shares](/key-tasks/sharing-data/), which give read-only access to a database. A share reaches MotherDuck users, so a consumer who needs the data in their own Redshift cluster needs a file-based handoff instead. |
| AWS Data Exchange datashares | No equivalent. Re-source the data from the provider directly. Inventory these during assessment: they're nobody's model, so they're easy to miss until something breaks. |
| Automated and manual snapshots | [Database snapshots](/concepts/snapshots/) and [UNDROP](/concepts/data-recovery/) cover point-in-time restore. Retention is set by plan, and only the Business plan can change `snapshot_retention_days`, so check the window you land on matches your cluster's snapshot retention. |
| Redshift Data API | The [Postgres endpoint](/key-tasks/authenticating-and-connecting-to-motherduck/postgres-endpoint/) for applications that speak the Postgres wire protocol, or a DuckDB client library. |
| Node types, WLM queues, concurrency scaling, and Serverless RPUs | Compute is per account, not per cluster. Size each service account's [Duckling](/about-motherduck/billing/duckling-sizes/) for the job it runs, and use [read scaling](/key-tasks/authenticating-and-connecting-to-motherduck/read-scaling/) for concurrent dashboard traffic. |
### Governance features without an equivalent
Four Redshift governance features have no MotherDuck counterpart: **row-level security policies**, **dynamic data masking**, **column-level privileges**, and **Lake Formation permissions** on Spectrum tables. List what you have with `SVV_RLS_POLICY` and `SVV_MASKING_POLICY`.
Access in MotherDuck is granted per Share rather than per object. [Table-level security](/key-tasks/sharing-data/table-level-security/) narrows a Share to named tables and views with an include pattern, on the Business and Enterprise plans, so the usual replacement for a row or column policy is a shape change rather than a setting: publish a Share per audience from a source that already excludes what that audience shouldn't see, or maintain a masked copy of the source database and share that. [Environment management](/key-tasks/data-warehousing/environment-management/) describes the masked-copy pattern for regulated data.
Row-level security and column-level permissions on Shares are on our roadmap, so check where they've got to before you design around their absence.
:::warning
Surface this during assessment, not at cutover. Where masking or row-level security policies back a compliance commitment, the replacement is a data-modelling change with a review attached, and that's weeks of lead time rather than a configuration change on the day.
:::
## Validate against Redshift before you cut over
Run the aggregate comparison and anti-join from [Validate before you cut over](/key-tasks/data-warehousing/migrating-your-data-warehouse/#validate-before-you-cut-over). Three Redshift-specific differences cause most mismatches:
- Integer division that truncated in Redshift now returns fractions, which shifts every sum and bucket built on it.
- `COUNT(*)` and `COUNT(DISTINCT key)` disagree on a table Redshift declared with a primary key. Redshift never enforced the key, so the duplicates were already there. Decide whether the model should keep them before you call it a mismatch.
- A `GETDATE()` that ran in UTC on Redshift now runs in the client's timezone, or a `TIMESTAMPTZ` column lost its timezone in the `UNLOAD` and was read as local time. Either lands rows on a different date near midnight.
Check these and the source watermark on both sides before you go looking for a logic bug.
## Cut over and decommission
Follow the [cutover steps](/key-tasks/data-warehousing/migrating-your-data-warehouse/#cut-over-and-decommission). On the Redshift side:
- Freeze the workload by disabling the scheduled queries, auto-copy jobs, and stored procedure schedules that write to it.
- On a provisioned cluster, pause the cluster rather than deleting it. Compute billing stops, storage keeps billing, and you can resume if something surfaces. Serverless bills compute only while queries run, so lower the workgroup's maximum capacity once nothing should be running against it.
- Take a final manual snapshot before you delete the cluster or workgroup. Delete only after the parallel window closes and the query history you've been copying shows nothing reading the tables.
## Rethink what Redshift shaped
Once a workload runs on MotherDuck, some of its shape was dictated by Redshift rather than by the problem:
- Tables denormalized, or duplicated with a second distribution key, to avoid redistributing rows during a join.
- `VACUUM`, `ANALYZE`, and deep-copy jobs that kept sort order and statistics fresh.
- WLM queue design that kept dashboards and ETL from starving each other. Separate service accounts with their own Ducklings do this without a queue.
- Cold data moved to Spectrum to save cluster storage. MotherDuck reads S3 directly, so the hot-or-cold decision can wait until a query proves it matters.
Hold these until after the workload is validated. Changing the logic and the platform in the same step means a mismatch has two possible causes.
## Related
- [Tune a migrated Redshift workload](/key-tasks/data-warehousing/migrating-your-data-warehouse/migrating-from-redshift/tuning-a-migrated-redshift-workload/) for what to fix once the SQL runs
- [Amazon Redshift integration](/integrations/databases/amazon-redshift/) for the `UNLOAD` mechanics
- [Ingest partitioned S3 Parquet on a schedule](/cookbook/flight-scheduled-s3-ingest/)
- [Migrate your data warehouse](/key-tasks/data-warehousing/migrating-your-data-warehouse/) for the source-independent process
- [Environment management](/key-tasks/data-warehousing/environment-management/)
---
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