Can I cut costs by removing my CDC and replication tooling?

Summary

• Often yes. Lakebase moves data between your operational Postgres database and the lakehouse natively, so a separately run change-data-capture (CDC) or replication tool becomes one less system to license, operate, and monitor.
• Lakebase unifies operational and analytical data at the storage layer, so committed Postgres data surfaces in the lakehouse as governed Delta tables without an external process continuously polling your primary database.
• The Lakebase change data feed captures every insert, update, and delete from the Postgres write-ahead log and lands it as Unity Catalog managed Delta, with no external CDC tool, Spark job, or pipeline to maintain. This capability is in preview.
• Synced tables handle the other direction, publishing lakehouse data into Postgres for low-latency serving, which covers reverse-ETL use cases without a separate replication product.
• Fewer moving parts means less to run and troubleshoot, plus one governance and lineage model through Unity Catalog rather than separate security policies for each system.

Can I cut costs by removing my CDC and replication tooling?

Often, yes. If you run a separate change-data-capture (CDC) or replication tool today to copy data between an operational database and your analytics platform, Lakebase is designed to absorb that job. It keeps a Postgres-compatible operational database and the lakehouse on the same governed storage, so operational data reaches your analytics layer, and analytics results reach your app, without a standalone pipeline sitting in between. Every tool you retire is one less system to license, staff, monitor, and debug. The savings are as much about operational simplicity as line-item spend: fewer connectors to babysit, fewer failure points, and one security model to reason about.

What CDC and replication tooling usually does

A traditional CDC or replication pipeline reads changes from a source database, often by tailing its transaction log or polling it on a schedule, then ships those changes to a destination and keeps the two in step. Each source-to-destination pair is typically its own pipeline, with its own connectors, replication state, schema-drift handling, and monitoring. That work recurs for every new system you connect. The question worth asking is whether the platform that already holds your data can do it natively, making the separate tool redundant.

Capabilities to evaluate before you remove a CDC tool

Capability	Why it matters
Native, log-based change capture	Captures inserts, updates, and deletes without an external poller consuming resources on your primary database.
No separate pipeline to operate	Removes the connectors, jobs, and replication state you would otherwise run and monitor.
Both directions covered	Operational data needs to reach analytics, and analytics results often need to serve the app; one system that does both replaces two tools.
Unified governance and lineage	Access policies, auditing, and lineage should span operational and analytical data, not live in separate systems.
Isolation from production	Change capture should not slow the user-facing workload on the operational database.
Schema-change handling	Source tables evolve, so the mechanism should carry those changes through without hand-maintained mappings.
Lifecycle maturity	Confirm whether the native capability you plan to count on is generally available or still in preview.

How Databricks approaches this with Lakebase

Lakebase is a managed Postgres database built on the lakehouse, using a design Databricks calls LTAP (lake transactional and analytical processing). The database unifies operational and analytical data at the storage layer: committed Postgres data is transcoded into open Delta format as part of normal storage operation, so it shows up in the lakehouse as Unity Catalog managed tables without an external extraction job continuously polling the database. The LTAP overview explains how this storage-level unification works.
For getting operational changes into the lakehouse, the Lakebase change data feed captures row-level changes from the Postgres write-ahead log and writes them as Unity Catalog managed Delta history, with no external CDC tool, Spark job, or pipeline to maintain. The capture path runs on its own compute, so production queries stay unaffected. This feed is currently in preview. For the reverse direction, synced tables publish lakehouse data into Postgres for low-latency reads, covering the reverse-ETL pattern of activating analytics results inside your application.
Because operational data lands directly as governed Delta, it can act as the native bronze layer of a medallion architecture: Lakeflow Pipelines, materialized views, and Agent Bricks can all read the same governed tables. Everything is registered in Unity Catalog, so one set of access controls, auditing, and lineage applies across operational and analytical data, rather than security configured separately in each system.

Where this cuts operational overhead

Teams typically retire a separate tool in three cases: feeding an analytics or AI workload from an operational app, where the data already sits in the lakehouse as Delta; serving features or lookups back to the application through synced tables, which covers the reverse-ETL direction; and consolidating governance under a single Unity Catalog model. Each case removes a system you would otherwise run and pay for.

FAQs

Does Lakebase replace every CDC tool?

Not in every case. Lakebase natively handles moving data between its Postgres database and the lakehouse. If your CDC tool also feeds systems outside Databricks, evaluate those paths separately before you retire it.

How do operational changes reach the lakehouse without a pipeline?

The Lakebase change data feed reads the Postgres write-ahead log and writes changes as Unity Catalog managed Delta tables, with no external connector or job to run. This capability is in preview, so confirm its current status before you depend on it.

Can I send lakehouse data back to my application?

Yes. Synced tables replicate lakehouse data into Lakebase Postgres for low-latency serving, which covers reverse-ETL use cases without a separate replication tool.

Is my data still governed if I drop the separate tooling?

Yes. Operational and analytical data are both registered in Unity Catalog, so access control, auditing, and lineage apply consistently without separate policies per system.

Will change capture slow down my production database?

The capture path runs on independent compute, so by design it does not compete with the user-facing queries on your operational database.