CDC that holds up in production
Most CDC tools work fine in testing. Estuary is built for the databases you can't afford to break. Low WAL impact, reliable replication, and no surprise bills.
At this stage, the question is not whether to use CDC… It's which approach will hold up under production load.
If you are searching for CDC Solutions:
Stop paying MAR pricing for row-level changes.
Move from polling-based queries that lock your production database.
Replicate to Snowflake, Databricks, or Redshift without manual schema work.
Recover gracefully when things go wrong, without a full backfill.
Numbers from actual Estuary customers
40%
Headset cut Snowflake compute costs by 40% after switching from Airbyte, with 100% data integrity and no missing records
50%
Curri reduced data sync costs by 50% vs. Fivetran, and eliminated a 12-hour Stripe payment sync lag with real-time CDC
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50%
Livble achieved 50% cost reduction while reaching real-time operational excellence, replacing batch pipelines with log-based CDC
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Not all Change Data Capture is the same
CDC tools typically detect changes in one of two ways:
1. Some approaches rely on query-based polling, periodically checking tables for updated rows. This can work well for lower-volume workloads or scheduled syncs.
2. Other approaches read directly from database transaction logs.
3. What happens when something breaks. Some tools require a full backfill from scratch if a replication slot gets dropped. Others resume from a transaction checkpoint — no data loss, no multi-day recovery window.
The difference becomes more important as:
Write volume increases.
Latency requirements tighten.
Source systems are performance-sensitive.
Data must be delivered in order and without gaps.
A pipeline failure would mean days of recovery time.
Log-Based captures changes at the source
By reading directly from transaction logs, log-based CDC:
Never queries your production tables, reads directly from the transaction log.
Supports read replicas, keeping application databases untouched.
Continuously clears WAL data as it's captured, your log stays clean.
Resumes from a transaction checkpoint after slot drops, no full backfill needed.
For teams building real-time pipelines or replicating into Snowflake, Databricks, or streaming systems, this approach provides stronger guarantees as workloads grow.
Let's talk“We're a big fan of Estuary's real-time, no code model. It's magic that we're getting real time data without much effort and we don't have to spend time thinking about broken pipelines. We've also experienced fantastic support by Estuary.”
Capturing changes is one step.
Delivering them efficiently at scale is another.
Estuary separates how data is captured from how it's delivered. A slow destination never stalls your pipeline, and a fast one never overloads your source.
Push the same captured data to multiple destinations without going back to the source.
Destination outages don't stall your capture. Data keeps flowing and catches up automatically
$0.50 per GB and $100 per connector. No MAR, no row counts, no surprise bills
Millisecond latency or batch. Same price either way
This matters when CDC is not just a feature, but a core part of your data infrastructure.
Tell us your sources and destinations and we'll show you what your pipeline looks like on Estuary and what it would cost.
We support over 200+ systems and would like to hear more about your sources and targets.