Postgres to MotherDuck: Offload Analytics Without Slowing Down

Your app runs on PostgreSQL. It is fast, reliable, and great for transactions. But once your team asks for analytics (like user signups, sales trends, or product performance), you might start running complex queries against the same database.

That is when the issues begin.

A query like SELECT COUNT(*) FROM orders WHERE created_at > now() - interval '30 days' gets slow. Dashboards time out. CPU usage climbs. The app feels sluggish because the same system handles transactional and analytical workloads.

You could scale up or add read replicas, which only delays the problem. The real issue is trying to do analytics on a database that was built for transactions.

This article shows a better approach. You can stream data from Postgres into MotherDuck, a fast analytics engine built on DuckDB. With Change Data Capture (CDC) and Estuary Flow, you get a continuous sync of your data, so you can run heavy queries in MotherDuck without touching your production database.

Let’s take a closer look.

Why Postgres Isn’t Built for Mixed Workloads

PostgreSQL is designed for OLTP, which processes online transactions. It handles small, fast operations like inserting new rows, updating records, or reading a specific user by ID. This makes it ideal for powering applications where low-latency transactions matter.

But when you introduce analytical workloads, things change.

Analytical queries are usually broader and more expensive. Instead of fetching a few rows, they might scan millions. You might run a GROUP BY across a full table, or join several large tables to build a funnel or calculate customer lifetime value.

Here’s a typical example:

plaintext
SELECT country, COUNT(*) 
FROM users 
WHERE created_at > now() - interval '90 days' 
GROUP BY country;

This kind of query causes PostgreSQL to scan lots of data, use memory and CPU heavily, and potentially hold locks. If your application is trying to write data simultaneously, you get contention. Over time, this leads to slow performance, long-running queries, and even outages.

You may try to fix it by creating read replicas. But now you’re managing replication lag and routing logic. You can increase instance size, but costs go up fast. And you’re still dealing with vacuuming, bloat, and Postgres' limits as an analytical engine.

The truth is, PostgreSQL was not designed to handle both transactional and analytical workloads at scale in the same environment. For analytics, you need a system optimized for large scans, columnar storage, and separation of compute and storage.

That’s where MotherDuck comes in and why moving analytics out of Postgres is a safer and more scalable path forward.

Scaling Analytics the Easy Way: Introducing MotherDuck

MotherDuck is a cloud-native analytics platform built on DuckDB, a fast in-process OLAP database that has been gaining popularity for its speed and simplicity. DuckDB is designed for analytical workloads: scanning large datasets, performing aggregations, and running complex queries efficiently.

MotherDuck extends this by providing a managed, collaborative, and serverless experience. You get the performance of DuckDB with the flexibility and scale of the cloud.

Here’s why that matters:

• Columnar storage: Unlike Postgres’ row-based format, MotherDuck uses columnar storage, which is far more efficient for analytical queries. Scans, aggregations, and filters are faster and use less memory.
• No infrastructure to manage: With MotherDuck, no instances or clusters exist to scale. It runs in the cloud and uses DuckDB under the hood, so you can focus on querying data instead of managing systems.
• Compatible with DuckDB locally: Analysts and developers can query data using the same engine locally or in the cloud, which makes debugging and development easier.
• Built-in performance for large scans: Whether you're summarizing millions of rows or joining several tables, it efficiently handles these workloads.

Imagine taking the same analytical query that slowed down Postgres:

plaintext
SELECT country, COUNT(*) 
FROM users 
WHERE created_at > now() - interval '90 days' 
GROUP BY country;

Now it runs in a purpose-built system for this query type, without touching your production database.

By streaming data from Postgres to MotherDuck, you decouple transactional and analytical workloads, avoid costly infrastructure scaling, and give your team the fast, flexible analytics experience they need.

Up next: How to make this sync happen in real time with Estuary Flow and CDC.

How to Stream Postgres to MotherDuck with CDC and Estuary Flow

To move data from Postgres to MotherDuck without relying on brittle batch jobs or manual exports, you can use Change Data Capture (CDC). CDC tracks changes, like inserts, updates, and deletes, in your database and streams them to another system. This keeps your target (in this case, MotherDuck) continuously up to date with minimal overhead on your source database.

Instead of pulling full tables on a schedule, CDC lets you capture what and when it changed, and apply it downstream. It’s efficient, low-latency, and ideal for syncing transactional systems with analytics platforms.

That’s where Estuary Flow comes in.

Estuary Flow is a real-time data integration platform that makes CDC reliable and straightforward. It connects directly to Postgres using logical replication, listens for changes, and pushes them downstream to various destinations, including MotherDuck.

Here’s why Estuary Flow is the ideal tool for building a Postgres to MotherDuck pipeline:

• Automatic backfill: Flow will capture historical data before switching to live updates when you set up a connector. No need to stitch together manual jobs.
• Schema evolution support: If your Postgres schema changes (for example, you add a new column), Flow can detect it and update the downstream schema automatically.
• Exactly-once delivery: Flow ensures that data lands in your destination without duplication or loss, even in the face of failures.
• Declarative config: You define your pipelines as code or in a UI, and Flow handles the infrastructure and orchestration for you.

The result is a robust pipeline that continuously replicates your Postgres data into MotherDuck. You can then run analytical queries, power dashboards, or build models on that data without touching your operational database.

Flow handles the heavy lifting. You just connect the systems and let it stream.

In the next section, we’ll learn how to set this up.

Step-by-Step: Stream Postgres to MotherDuck with Estuary Flow

Setting up a pipeline from Postgres to MotherDuck with Estuary Flow is straightforward. You don’t need to write custom scripts or manage complex infrastructure. Here’s how to get started:

Step 1: Connect Postgres to Estuary Flow

First, set up a source connector in Flow for your Postgres database. This involves enabling logical replication and creating a replication slot.

Then create a user with replication privileges and grant access to the relevant tables.

Flow will use this connection to stream changes as they happen.

Step 2: Select the Tables You Want to Capture

Once connected, you can choose which tables to include in your pipeline. Flow supports filtering specific schemas, tables, or even columns, so you only move the data you care about.

For example, you might select users, orders, and products for analytics, and skip high-churn internal tables.

Step 3: Set Up the MotherDuck Destination

Next, configure a MotherDuck materialization in Flow. This sets up the destination schema and tells Flow where to deliver the captured data.

Flow automatically handles table creation and schema mapping. You can optionally define how you want the data structured, for example, flattening nested fields or renaming columns.

Step 4: See the Data Flow in action!

With source and destination configured, data will start flowing into Motherduck. Flow will:

• Backfill existing data from Postgres
• Begin streaming new changes using CDC.
• Write those changes into MotherDuck incrementally.

Your analytical data is now decoupled from your transactional systems.

Step 5: Query and Build on Top

Once your data is in MotherDuck, you can start querying it directly using DuckDB SQL syntax. You can also connect BI tools like Hex, Mode, or Tableau using MotherDuck’s integrations.

For example, this query now runs on MotherDuck instead of your production database:

plaintext
SELECT category, SUM(quantity) 
FROM orders 
WHERE order_date >= date_trunc('month', current_date) 
GROUP BY category;

You’ve just shifted heavy read workloads off Postgres without disrupting your application.

Want to Learn Easily? Watch This Video!

In the next section, we’ll cover the benefits you’ll notice immediately, both operationally and for your team.

Why Stream Postgres to MotherDuck? Key Benefits You’ll Notice Instantly

The improvements are hard to miss once you offload analytics from Postgres to MotherDuck using Estuary Flow. You’ll see value across performance, team productivity, and operational simplicity.

1. Your Production Database Stays Fast and Stable

The most immediate impact is that heavy analytical queries no longer bog down your transactional database. Inserts, updates, and deletes stay snappy. You reduce lock contention and avoid slowdowns caused by long-running scans or joins.

For example, if you used to see CPU spikes during a weekly dashboard refresh or when a team member ran a large GROUP BY, those disappear once the query workload moves to MotherDuck.

2. Analysts Get Fresh Data Without Waiting

Flow keeps your analytical copy in sync continuously using CDC. Analysts and business users no longer rely on outdated snapshots or overnight batch jobs.

If a new order is placed or a user signs up, that data is reflected in MotherDuck in near real time, often within seconds. This opens the door to more responsive reporting, better monitoring, and faster decision-making.

3. You Can Scale Analytics Without Touching Infrastructure

With Postgres, scaling analytics often means provisioning replicas, tuning indexes, or rewriting queries to work around performance limits. With MotherDuck, that complexity goes away.

There’s no cluster to manage. It’s serverless, so you only pay for what you use. You can run large aggregations, filter millions of rows, or explore data freely without worrying about affecting production systems.

Final Thoughts: Move Postgres Analytics to MotherDuck the Smart Way

Running analytics on your primary Postgres database works until it doesn’t. As your business grows and data needs increase, combining transactional and analytical workloads in the same system becomes risky and expensive.

With Estuary Flow and MotherDuck, you can decouple analytics from your operational systems. CDC gives you continuous updates, Estuary Flow makes the pipeline reliable and easy to manage, and MotherDuck gives you a fast, scalable engine that is purpose-built for analysis.

This setup keeps your production database healthy while giving your data team the flexibility and performance they need to work effectively. No late-night alerts, no slow dashboards, and no last-minute query rewrites.

Whether you're streaming from Postgres, BigQuery, or Snowflake, Estuary Flow makes real-time analytics with MotherDuck simple and scalable.

Ready to get started?

Sign up for Estuary Flow and connect your Postgres database to MotherDuck in minutes. Already using it? Share your experience in our Slack channel.