DIY is brittle
Log readers, offset tracking, restart logic, schema drift - a self-built CDC stack is a permanent engineering project that pages someone at 3 a.m.
Solutions / Real-Time CDC
AI and analytics on real-time data - not yesterday's snapshot.
Capture every change from your operational databases as it happens and deliver it to your warehouse, lake, or AI context.
No streaming platform to build, no capture jobs to babysit. Change data capture, managed end to end.
The problem
Stale data, stale decisions.
The highest-value uses of your data - fraud checks, live recommendations, support agents acting on the current state of an order or account - only pay off when your warehouse reflects what is true now. Every minute of lag is a decision made on yesterday's business. Most teams that try to close that gap hit one of three walls:
Streaming platforms need a specialist
The classic fix - Kafka, Debezium, connectors - solves freshness, but managing a streaming platform is a specialized, full-time job your team takes on forever.
Query-based sync has real limits
Polling queries add repeated load to your production database, wait for the next schedule, and can't see a row that's been deleted - real constraints for latency-sensitive workloads.
No log access? We also do query-based - see Database Replication →
Dataddo's log-based CDC removes all three: managed capture, no platform to run, and a replica that stays faithful - deletes included.
How it works
Read the log. Not the database.
Dataddo reads committed changes directly from the database engine's transaction log - change data capture (CDC) - the binlog on MySQL, WAL logical replication on PostgreSQL, CDC tables on SQL Server, MLOG on Oracle. That single architectural choice is where the benefits come from:
Cross-technology: any supported source engine feeds any supported destination - BigQuery, Snowflake, Databricks, operational databases, lakes, or event systems.
Technology
Real-time by construction
Changes are picked up as they are committed, not when the next extraction query runs.
Performance
Near-zero production load
Reading the log has very low performance impact. No polling queries compete with your application traffic.
Guarantee
Nothing is missed
Every committed insert, update, and delete becomes one ordered change event - including deletes, which no query-based method can see.
| Source engine | Change stream | Position tracking |
|---|---|---|
| MySQL | Binary log (binlog) | Binlog position |
| PostgreSQL | WAL logical replication | Replication slot |
| SQL Server | CDC tables | LSN |
| Oracle | MLOG (automatic management) | SCN |
Every committed insert, update, and delete becomes one change event the moment it commits - reading the transaction log, not hammering your tables.
Performance
Real-time change capture, sustained at scale.
351 ms
p50 latency
568 ms
p90 latency
35,000/s
events sustained
SQL Server CDC, internal benchmark
What that means in practice: a customer places an order and immediately asks your chatbot where it is. The agent already sees the order - it can confirm it, change the delivery address, or apply a credit based on what is true right now. And a stolen card trips your fraud rule seconds after the transaction.
Capabilities
A mirror you can trust. A pipeline that runs itself.
A mirror, not a change feed to postprocess
With the CDC write mode, Dataddo automatically squashes the sequence of operations into a fully materialized table: inserts create rows, updates modify them, deletes remove rows. Your warehouse holds an always-current mirror of production - no merge jobs to write, no post-processing to schedule.
It heals itself
A built-in CDC Supervisor health-checks every replication process and automatically restarts any that stall, so a restarted process resumes exactly where it left off - no committed change is lost, and no one gets paged.
Deletes are captured
Log-based capture is the only method that detects deleted rows while asking practically nothing of your production database. Fraud checks, inventory, order state - use cases where a vanished row matters stay correct.
Ordered, verifiable change events
Dataddo maintains commit order, and every event carries its operation type and a sequence identifier from the engine's change stream. If you need to verify or re-establish ordering downstream, the metadata to do it travels with the data.
History on demand
CDC captures changes going forward, and a one-time backfill brings the table's existing history into the destination, so your mirror starts complete, not empty.
Audit trail when you want one
Prefer the full story over the current state? Deliver every change in commit order - to event systems, lakes, or an insert-only history table.
Time to value
A one-time database setting and you're ready to go.
Standing up your own CDC stack usually means running log readers, offset stores, and recovery logic forever. With Dataddo, the database side is a one-time configuration - enable the engine's change stream and grant read access - and everything after that is managed: capture, ordering, materialization, monitoring, and automatic recovery. Your team gets live data in the warehouse; we run the machinery.
See real-time CDC on your own data.
A scoped, time-boxed POC for your CDC use case. Bring your own workload - the more complex, the better.