--- title: "Why Postgres Is Slow at Analytics & How to Fix It | MotherDuck" description: "Postgres slows down on analytics as your app grows. Learn the signs you've hit its ceiling and how to offload analytical queries to MotherDuck." canonical: "https://motherduck.com/videos/postgres-slow-analytics-queries/" --- [BACK TO VIDEOS](/videos/) [Webinar](/videos/?category=Webinar#videos-and-webinars-library)[SQL](/videos/?category=SQL#videos-and-webinars-library)[Data Pipelines](/videos/?category=Data%20Pipelines#videos-and-webinars-library)[MotherDuck Features](/videos/?category=MotherDuck%20Features#videos-and-webinars-library) # Is Your Postgres Slow at Answering Analytics Questions? 2026/06/09Featuring: [Mehdi Ouazza](/authors/mehdi-ouazza/) > **TL;DR:** Postgres is a transactional database, not an analytical one. When dashboards and customer-facing analytics start timing out, the fix isn't a bigger instance — it's routing analytical reads to a columnar engine. Mehdi Ouazza shows how to keep Postgres as your system of record while MotherDuck handles the heavy queries. ## When Postgres hits its analytical ceiling Postgres is a transactional database. When you start running large aggregations and concurrent dashboard reads against row-based storage, it shows. Pages take ten seconds to load, API gateways time out, read replicas don't actually help. These aren't tuning problems. You've outgrown Postgres for analytical work. ## The usual patches stop scaling Read replicas, materialized views, bigger instances — they all buy time but none fix the underlying mismatch. Replicas still do row-based scans. Materialized views go stale or get expensive to refresh. Scaling up costs more every quarter and you're still running [OLAP](/learn-more/what-is-OLAP/) queries on an OLTP engine. ## Keep Postgres, add a columnar engine Postgres stays as the system of record. Analytical reads go to an engine built for them. [Columnar storage](/learn-more/columnar-storage-guide/) reads only the columns a query touches, so aggregations over millions of rows come back in milliseconds instead of seconds. MotherDuck runs DuckDB in the cloud — you get that speed without standing up a full data warehouse. ## Pick the right ingest pattern Mehdi walks through four ways to move Postgres data into MotherDuck: batch loads, change data capture (CDC), in-place reads, and the Postgres wire protocol. Each trades off freshness against complexity differently. The session ends with a live demo connecting a Postgres database to MotherDuck, running analytical queries, and feeding results into a dashboard. You can [try it yourself](/docs/getting-started/) in a few minutes. TABLE OF CONTENTS - When Postgres hits its analytical ceiling - The usual patches stop scaling - Keep Postgres, add a columnar engine - Pick the right ingest pattern Start using MotherDuck now! [Try 7 Days Free](https://auth.motherduck.com/authorize?app_source=web&response_type=code&client_id=bza3KWQpxRAFlTlRFXUo29AOg9xD7zcp&redirect_uri=https%3A%2F%2Fapp.motherduck.com%2F&state=STATE&auth_flow=signup&screen_hint=signup&ext-ph_distinct_id=58d264ff-1066-4105-8c9b-7effe6348f4f) ## FAQS ### Why is Postgres slow at answering analytics questions? Postgres stores data in rows, which works well for the kind of read-a-record, write-a-record work most applications do. But when you run a query that scans millions of rows to compute a sum or a group-by, it reads every column in every row even if you only care about two of them. That's a lot of wasted I/O, and it's happening on the same database your application is trying to use for normal operations. This is the OLTP-vs-OLAP mismatch. Postgres is built for the former. Analytical queries belong in the latter. ### Will read replicas or materialized views fix slow Postgres analytics? Read replicas and materialized views help for a while, but they don't fix the real problem. Read replicas still run the same row-based scans. Materialized views go stale, and refreshing them gets slower as your data grows. Scaling the instance up just means a bigger bill every quarter. If your analytics workload keeps growing, you're better off routing those queries to a columnar engine than trying to patch Postgres into something it isn't. ### How do you move analytics off Postgres without a data warehouse? Keep Postgres as your system of record and route analytical queries to a columnar engine like MotherDuck. You can load data by batch, change data capture (CDC), in-place reads, or the Postgres wire protocol. Pick whichever matches how fresh the data actually needs to be. This saves you from standing up a full data warehouse when you don't need one. See the [getting started guide](/docs/getting-started/) to connect your first database. ### What is the difference between OLTP and OLAP? OLTP (online transaction processing) handles many small reads and writes — inserting an order, updating a user record. It's what Postgres was built for. OLAP (online analytical processing) sits at the other end: big scans across millions of rows, like computing revenue by region over a full year. There's more on this in our [OLAP guide](/learn-more/what-is-OLAP/). ### Which workloads should I move off Postgres first? Start with the queries that hurt most: big aggregations, dashboard rollups, and the ad-hoc exploratory stuff where someone scans an entire table. Those are the ones hogging resources and making your application feel sluggish. Keep transactional reads and writes on Postgres, where they belong. ## Related Videos [60:29](/videos/ai-agents-semantic-layer/)[2026-08-26](/videos/ai-agents-semantic-layer/) ### [Do AI Agents Need a Semantic Layer?](/videos/ai-agents-semantic-layer/) Do AI agents need a semantic layer? Evals comparing plain-text context with a Malloy semantic layer show where each wins, and what it costs. Stream AI, ML and LLMs BI & Visualization SQL [63:17](/videos/agentic-data-engineering-stack/)[2026-08-18](/videos/agentic-data-engineering-stack/) ### [Beyond Copilots: We're Building a Data Stack Live with AI Agents](/videos/agentic-data-engineering-stack/) Agentic data engineering in practice: AI agents build dlt ingestion into MotherDuck and a governed Lightdash semantic layer, live in an hour. 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