DuckDB vs. Postgres for embedded analytics: How to choose (and when to use a hybrid architecture)

When to use Postgres as your system of record (OLTP)

• Your workload is transactional (OLTP): You need high-frequency, single-row INSERT, UPDATE, and DELETE operations where ACID compliance and referential integrity aren't optional.
• You need granular concurrency control: Your application has hundreds or thousands of concurrent users modifying data simultaneously. Postgres's pessimistic locking and mature MVCC implementation are the gold standard here.
• You rely on complex constraints: Your data model depends heavily on foreign keys, unique constraints, and database-level triggers to enforce business logic.
• You need geospatial routing: While DuckDB has spatial capabilities, PostGIS remains the undisputed leader for complex topological operations and routing networks.

When to use MotherDuck (DuckDB) as your analytics engine (OLAP)

• You're building "Embedded Analytics": You need to surface data directly to end-users via dashboards that require sub-second latency on large datasets (10GB to 100TB+). See our guide on Embedded Analytics for more details.
• Your queries are aggregation-heavy: Your workload consists primarily of GROUP BY, window functions, or complex joins over millions of rows.
• You face "Noisy Neighbor" issues: Your analytical queries consume excessive CPU and RAM, degrading the performance of your transactional application.
• You want a local-first developer experience: You want your data engineers to develop and test pipelines locally on their laptops without managing Docker containers or SSH tunnels.

Recommendation: Use a hybrid architecture (Postgres + MotherDuck)

Don't view this as a binary choice between "sticking with Postgres" or "migrating to MotherDuck." The winning strategy is Hybrid Architecture. Keep your operational state in Postgres. Offload your read-heavy, analytical compute to MotherDuck.

Why Postgres runs slower for analytics: Row store vs. column store

Look at the disk I/O to understand the performance difference.

Postgres uses a row-store. Even if you only need to calculate the average of a single column like revenue, Postgres reads the entire row from disk. User IDs, timestamps, metadata, blob fields you don't care about. All of it.

DuckDB uses a column-store. It stores data column-by-column. To calculate average revenue, DuckDB reads only the revenue column. Nothing else. This reduces I/O by orders of magnitude.

And there's more. DuckDB uses vectorized execution. Instead of processing data one row at a time (Postgres's "Volcano" model), DuckDB processes batches of data (vectors) that fit into your CPU's L1 cache. This lets DuckDB use modern CPU instructions (SIMD) to process thousands of data points in a single clock cycle.

Local-first analytics development with DuckDB (works on my machine)

DuckDB runs as a single binary with no dependencies. A developer can install it via pip install duckdb or brew install duckdb and have a fully functional analytical database running locally in seconds.

This enables a "local-first" data engineering workflow. Build a dbt model, run tests, and debug queries against a local subset of data (say, a Parquet file) without ever connecting to the internet.

No more mocking databases. No more wrestling with complex local Docker setups that plague Postgres development.

How MotherDuck dual execution works (ATTACH 'md:')

The bridge between local experience and production scale is MotherDuck's Dual Execution model.

In a traditional setup, moving from local dev to cloud prod often means changing connection strings, drivers, and, often, the SQL dialect itself. With MotherDuck, you authenticate and run:

Copy code
ATTACH 'md:my_cloud_db';

That's it. Your local DuckDB instance and the MotherDuck cloud service now act as a single, distributed system. You can write a query that joins a CSV file on your MacBook with a 10TB table in the cloud:

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SELECT
    l.category,
    SUM(r.revenue) as total_revenue
FROM 'local_upload.csv' l
JOIN my_cloud_db.sales_data r ON l.id = r.category_id
GROUP BY 1;

MotherDuck's optimizer offloads intensive computation to the cloud while efficiently handling local file joins. This Hybrid Execution means you don't have to "deploy" data to query it. You can instantly augment your warehouse data with local context.

Benchmark results: minutes to seconds

UDisc, the dominant disc golf app with 90% market share, hit a wall when their analytics needs outgrew their transactional database. They tried the usual suspects — ClickHouse, Snowflake, Databricks, BigQuery, and Postgres — but as a bootstrapped, employee-owned startup, those solutions were either too expensive, too complex, or both.

Then they ran a proof of concept with MotherDuck. A typical analytical query that was still running after 2 minutes in Postgres finished in 5 seconds on MotherDuck. Their fully optimized dbt job went from 6 hours in Postgres to 30 minutes. Annual usage reports that required several hour-long scripts now complete in a couple of seconds.

The difference isn't just speed. Before MotherDuck, UDisc's course stats dashboard was limited to 30 days of data because anything more would choke their database. Now their volunteer ambassadors can load the full lifetime history of any course with all stats and charts in seconds. That kind of query simply wasn't possible before.

What dashboard latency users expect (100 ms vs 1 s vs 10 s)

For embedded analytics, latency is a feature.

• 100ms: Feels instant. Users explore data freely.
• 1 second: Users notice the delay but stay engaged.
• 10 seconds: Users lose focus.
• 30+ seconds: Users assume the app is broken.

Postgres performs admirably when tables are small. But as your data grows to tens of millions of rows, aggregations that used to take 200ms now take 10 seconds.

Indexes can help, but they have diminishing returns for complex queries with multiple filters and groupings. MotherDuck consistently delivers sub-second response times for these "slice and dice" workloads, regardless of complexity.

How MotherDuck prevents noisy-neighbor analytics (ducklings)

Performance isn't just about speed. It's about stability.

A common failure mode in Postgres-backed analytics is resource contention. Picture a SaaS platform where a large enterprise customer runs a massive report. In a shared Postgres instance, that query might consume all available IOPS and RAM (work_mem). Suddenly, transactional inserts for other customers slow down or time out. The analytical workload has effectively DOS-ed the operational database.

MotherDuck solves this with "Ducklings."

Unlike a monolithic warehouse where everyone shares a cluster, MotherDuck spins up isolated compute instances (Ducklings) for each user or tenant. This provides True Multi-Tenant Isolation.

• If Tenant A runs a massive query, that query consumes their specific Duckling's resources.
• Tenant B's dashboard stays lightning-fast because it runs on a completely separate compute instance.
• The "Noisy Neighbor" problem is engineered out of existence.

This isolation is critical for embedded analytics. You can't afford to have one power user degrade the experience for your entire user base.

Pattern 1: Batch export from Postgres to Parquet (S3)

For many internal dashboards, real-time data isn't strictly necessary. "Fresh as of this morning" often suffices.

The simplest integration uses Postgres's native COPY command to export tables to Parquet files in S3.

1. Extract: A nightly cron job runs COPY (SELECT * FROM orders) TO 's3://bucket/orders.parquet' (FORMAT PARQUET);.
2. Load: MotherDuck queries these files directly in S3 or ingests them into native storage for higher performance.

This approach is bulletproof. It requires zero complex infrastructure and uses the efficiency of the Parquet format. Batch exports decouple your analytics entirely from your production database.

Pattern 2: Near real-time replication with change data capture (CDC)

For customer-facing analytics where users expect to see their actions reflected immediately, batch exports are too slow. You need a streaming pipeline.

Tools like Debezium or Fivetran can read the Postgres Write-Ahead Log (WAL), capture changes as they happen, and stream them into MotherDuck.

CDC offers near real-time freshness, but it introduces significant infrastructure complexity (Kafka, connector management). It's the standard "Enterprise" pattern, but it may be overkill for lean teams.

Pattern 3: Accelerate Postgres analytics with pg_duckdb

The most exciting development here is pg_duckdb, an open-source extension that runs the DuckDB engine inside your Postgres server.

This extension is the perfect "bridge" for teams hitting the Postgres Wall. Instead of setting up new pipelines, you just install the extension. Then you write queries that look like standard Postgres SQL but are executed by DuckDB's vectorized engine.

Why Postgres analytics gets expensive: Idle capacity and operational overhead

In the Postgres world, scaling analytics usually means vertical scaling (upgrading to a larger instance) or adding read replicas. The economic flaw? You provision for peak capacity.

If your dashboard traffic spikes at 9 AM and 5 PM but stays quiet overnight, you still pay for that massive db.r6g.4xlarge instance 24 hours a day. You're paying for idle silicon.

And the operational cost of maintaining large analytical tables in Postgres is real. Engineers spend hours tuning autovacuum settings to prevent bloat, managing indexes that slow down writes, and manually partitioning tables.

How MotherDuck reduces analytics cost: Serverless scale-to-zero compute

MotherDuck flips this economic model with a Serverless, Scale-to-Zero architecture.

Unlike traditional cloud warehouses (like Snowflake), which often have a 60-second minimum billing increment, MotherDuck bills based on usage with much tighter granularity, down to the second.

• Start-up time: A "Duckling" spins up in milliseconds, not minutes.
• Scale-to-Zero: When no queries run, you pay $0 for compute.
• The "Pulse" Advantage: For bursty, user-facing workloads, this changes everything. A user loads a dashboard, MotherDuck wakes up, executes the query in 200ms, and goes back to sleep. You pay only for work done.

Cost attribution by tenant for embedded analytics

For SaaS providers, understanding who drives cost is critical. In a shared Postgres instance, calculating how much CPU time Tenant A used versus Tenant B is nearly impossible.

Because MotherDuck isolates tenants into separate Ducklings, cost attribution becomes native. You can track compute spend per tenant or per user. This lets you identify unprofitable customers, optimize query patterns for specific heavy users, and even pass through costs in premium pricing tiers.