The Best Change Data Capture (CDC) Tools for Databases in 2026

TL;DR: The best change data capture tools for databases in 2026 are Debezium (open-source, log-based, the industry standard for streaming CDC), Estuary Flow (managed real-time CDC), Fivetran (managed, log-based for the databases it supports), AWS DMS (cheap CDC inside AWS), and ingestr (a simple open-source CLI for incremental and replication loads when you do not need sub-second streaming). Most teams reach for streaming CDC before they need it. The first question is not "which CDC tool" but "do I actually need log-based CDC, or is a good incremental load enough?"

Change data capture means keeping a destination in sync with a source database by moving only what changed (inserts, updates, deletes) instead of reloading the whole table. Done well, it is how you replicate a production Postgres or MySQL database into a warehouse cheaply and continuously. Done wrong, it is a Kafka cluster you did not need and an on-call rotation you did not want. This guide is about picking correctly.

Two kinds of CDC (and why the difference matters)

• Log-based CDC reads the database's write-ahead log (Postgres WAL, MySQL binlog, SQL Server transaction log). It captures every change, including deletes, in order, with minimal load on the source. This is "real" CDC and it is what you need for low-latency replication or when you cannot miss a delete. It is also more operationally involved.
• Query-based / incremental CDC periodically queries the source for rows changed since the last run, using an updated-at timestamp or an incrementing id. It is far simpler to run, it does not touch the transaction log, and it is plenty for most analytics use cases. Its weaknesses: it can miss hard deletes (unless you soft-delete) and it has latency equal to your sync interval.

A large share of teams that think they need streaming log-based CDC actually need a reliable incremental load every few minutes. Being honest about which one you need is the single biggest cost decision in this whole category.

The contenders

Debezium

The open-source standard for log-based streaming CDC. It reads the transaction log and emits change events, typically into Kafka. If you need genuine real-time replication and you have (or want) a streaming platform, Debezium is the reference implementation. The cost is operational: you are running Kafka Connect, managing connectors, and handling schema evolution and offsets. Powerful, and heavier than most teams expect.

Estuary Flow

Managed, real-time CDC without you standing up Kafka. It gives you log-based capture from databases into warehouses and other destinations with low latency. A good fit if you genuinely need streaming but do not want to operate the streaming infrastructure yourself.

Fivetran

Fully managed, log-based CDC for the databases it supports, with the least maintenance of anything here. You configure a connector and it keeps your warehouse in sync. The tradeoff is cost, which is usage-based and can climb with high-change-volume tables, and that it is closed and cloud-only.

AWS DMS

If you are already in AWS, DMS does log-based CDC into targets like S3, Redshift, and RDS cheaply. It is less polished than the dedicated vendors and its schema handling can be fiddly, but the price is hard to argue with inside AWS.

Airbyte

Airbyte offers CDC on major databases using Debezium under the hood, wrapped in its connector platform and UI. You get streaming CDC plus a large connector catalog, at the cost of self-hosting the Airbyte server (or paying for Airbyte Cloud).

ingestr

We build ingestr, and it is deliberately the simple option here: an open-source CLI that does incremental and replication loads from databases and SaaS sources into your warehouse, with no server to run. It is query-based incremental for the general case, which covers most analytics replication cleanly:

ingestr ingest \
    --source-uri 'postgresql://user:pass@host:5432/db' \
    --source-table 'public.orders' \
    --dest-uri 'snowflake://...' \
    --dest-table 'raw.orders' \
    --incremental-strategy merge \
    --incremental-key updated_at \
    --primary-key id

If your requirement is "keep the warehouse copy of these tables current every few minutes without operating Kafka," ingestr does that with one command and a scheduler. If your requirement is sub-second streaming or guaranteed capture of every hard delete from the log, use Debezium or Estuary. We would rather tell you that than sell you streaming you do not need.

How to choose without over-engineering

1. Do you need sub-second latency? If no, you probably do not need streaming log-based CDC. A scheduled incremental load (ingestr, or Fivetran if you want it fully managed) is simpler and cheaper.
2. Do you need to capture hard deletes exactly? If yes and you cannot switch to soft deletes, you need log-based CDC (Debezium, Estuary, Fivetran, DMS).
3. Do you want to run infrastructure? If no, rule out self-hosted Debezium and lean managed (Estuary, Fivetran, DMS) or serverless CLI (ingestr on a scheduler).
4. Are you cost-sensitive on high-change tables? Usage-priced managed CDC gets expensive on chatty tables. Open-source (Debezium, ingestr) or DMS controls that cost.

CDC is one piece of the pipeline

Whichever CDC tool you land on, capturing changes is step one. The changes still need to be modeled into clean tables, quality-checked, and scheduled alongside the rest of your pipeline. ingestr is the ingestion layer of Bruin, an open-source platform that also handles transformation, data quality, and orchestration, so replication and everything downstream live in one place rather than in a CDC tool plus a transformation tool plus a scheduler. If you use streaming CDC, the same principle holds: keep the modeling and quality layer close to where the changes land.

For the wider ingestion landscape see our best data ingestion tools in 2026 guide, and for replicating into a specific warehouse, the guides for Snowflake, BigQuery, and Databricks.

I work at Bruin, so this is an informed but interested take. Corrections welcome at [email protected].