Solutions / Real-Time CDC

AI and analytics on real-time data - not yesterday's snapshot.

Fraud checks, live recommendations, and agents acting on an order's current state only pay off when your data is fresh - every minute of lag is a decision made on yesterday's business.

Log-based change data capture keeps your warehouse, lake, and AI a live, faithful mirror of your operational databases - every insert, update, and delete, the moment it commits, with near-zero load on production.

No streaming platform to build, no capture jobs to babysit. Change data capture, managed end to end.

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Source

Your operational database

Tables

untouched - no polling, no triggers

commits

Transaction log

PostgreSQL MySQL SQL Server Oracle MongoDB SAP HANA + any Postgres-compatible

reads the log

Dataddo

Change Data Capture

+ INSERT new row
~ UPDATE changed row
− DELETE removed row

change events

Destination

Warehouse · Lake · Stream · AI context

always-current mirror

Snowflake Databricks Google BigQuery Amazon Redshift Azure Synapse Kafka Azure Event Hub Google Pub/Sub + any other
Any source engine → any destination
Native CDC across your source databases

One log-based approach. Every major engine.

Because Dataddo reads each database's native change log, one approach covers PostgreSQL and every Postgres-compatible database - Aurora, AlloyDB, CockroachDB, and managed cloud Postgres - alongside MySQL, SQL Server, Oracle, MongoDB, and SAP HANA.

PostgreSQL PostgreSQL MySQL MySQL MSSQL MSSQL Oracle Oracle MongoDB MongoDB SAP HANA SAP HANA AWS Aurora (PostgreSQL) AWS Aurora (PostgreSQL) AWS RDS (PostgreSQL) AWS RDS (PostgreSQL) Google Cloud SQL (PostgreSQL) Google Cloud SQL (PostgreSQL) AlloyDB AlloyDB CockroachDB CockroachDB
See all 400+ connectors →
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
MongoDB Oplog (change streams) Resume token
Performance

Real-time change capture, sustained at scale.

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.

Metric Result
p50 latency 351 ms
p90 latency 568 ms
events sustained 35,000/s

SQL Server CDC, internal benchmark

What it guarantees

Real-time data, without the trade-offs.

Real-time by construction

Changes are captured the moment they commit - there's no polling interval to tune and no schedule to wait for. Your warehouse tracks production continuously, so agents, fraud rules, and dashboards act on the current state of an order or account, not a snapshot from minutes ago.

Near-zero production load

Reading the transaction log places almost no burden on the source - no repeated polling queries competing with application traffic, no extra indexes or triggers. The database that runs your business keeps serving users at full speed while replication happens quietly alongside it.

Nothing is missed

Every committed insert, update, and delete becomes exactly one ordered change event - deletes included. Query-based sync simply can't see a row that's been removed; log-based capture always can, so fraud checks, inventory counts, and order state stay correct even when records disappear.

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.

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.

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