Change Data Capture (CDC)
The most efficient, fastest way to replicate databases—at your fingertips.
Unlock Powerful Benefits with CDC
Blazing fast
Reading from WALs, binlogs, and redo logs is faster than standard querying
Highly efficient
Don’t read the same data over and over again—only changes
Capture ALL changes
Batch snapshots can miss important updates while CDC captures every change as it happens
CDC Is Our Specialty
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Change Data Capture (CDC) Done Correctly - Best Practices for Implementing Change Data Capture
Learn how to implement change data capture (CDC) the right way, and some of the CDC best practices.
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Why is Everyone Buying Change Data Capture?
Why are world-class engineering teams buying Change Data Capture instead of building it? Explore the hidden complexity of CDC, the wave of billion-dollar acquisitions, and why even top companies admit it’s easier to buy than build.
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Change Data Capture Isn’t Magic: The Reality of Handling CDC Yourself
Struggling with Debezium and Kafka Connect? Discover the hidden costs of DIY CDC and why managed solutions like Estuary Flow are a smarter choice.
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Snowflake Change Data Capture (CDC): A Comprehensive Setup Guide
Learn how to set up change data capture pipelines within Snowflake, plus tips to build CDC pipelines into Snowflake.
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Oracle Change Data Capture (CDC): Complete Guide to Methods, Tools & Best Practices
Discover the best practices, methods, and tools for implementing Oracle Change Data Capture (CDC). Learn how to optimize your Oracle CDC and maintain real-time data consistency.
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A New Reference Architecture for Change Data Capture (CDC)
Change data capture has been around for a while, but it's due for an upgrade. Let's take a look at what has been working and what needs to change to make it fit into the modern data stack.
Estuary Makes CDC Seamless
Keep your data pipelines fast and reliable with Estuary's real-time CDC capabilities. Stream changes from transactional databases like PostgreSQL with second-level latency and zero data loss.
No need to code a custom solution with our pre-built in-house connectors
Millisecond latency powers real-time analytics for instantaneous insights
Automatic table discovery and schema inference make setup quick—even for 100s of tables
Optimized CDC for Each System
Estuary knows that one size doesn't always fit all. That's why our CDC solutions are tailored to each source system for maximum speed and efficiency.
Blazing-fast CDC for all SQL captures
Incremental data capture on SaaS connectors
Connectors with dual-stream captures to solve eventual consistency
Effortless Efficiency
Even if your data is destined for batch reports rather than real-time processing, CDC makes reads more efficient—and can save on costly compute over deduplicating results from batch queries.
Stream updates, inserts, and deletes as they occur
Incremental replication minimizes strain on the source system
Optimized and continuously maintained connectors provide top performance
Secure Data at Every Step
Move fast—and don’t break things. Pair speedy CDC with Estuary’s enterprise-grade security.
Private and BYOC deployment options keep control in your hands
Protect sensitive data with SOC 2 Type II and HIPAA compliance with no exceptions
Enable teamwork and partnerships with role-based access control and data sharing
RevUnit
Estuary is our preferred CDC solution for importing data from application databases into BigQuery for analytics. It offers a transparent pricing structure, timely support responses, and an intuitive CLI tool for bulk configuration tasks. In contrast, other market solutions often have ambiguous pricing and fewer options for precise data replication across environments. This makes choosing to use Estuary an obvious decision.
What Customers Are Saying
Estuary makes it easy to implement state-of-the-art Change Data Capture in all your data pipelines. Flexible configurations allow for seamless integration with batch sources as well so you don't need to leave any of your data behind.
Explore Top CDC Connectors
Snowflake Data Cloud
Snowflake is a cloud-based data warehouse that offers highly scalable and distributed SQL querying over large datasets. Using OLAP (Online Analytical Processing), Snowflake offers the ability to rapidly answer multi-dimensional analytic database queries with potentially large reporting views by breaking each query up between many worker nodes and reassembling the finalized answer.
MongoDB
MongoDB is a NoSQL document-oriented database that stores data in JSON-like documents with dynamic schema, making it very flexible and easy to work with.
SQL Server
Microsoft SQL Server is a Relational Database Management System developed by Microsoft. There are at least a dozen editions of SQL server which are for large enterprises and small clients.
PostgreSQL
Postgres is a free and open-source relational database management system (RDBMS) emphasizing extensibility and SQL compliance. It was originally named POSTGRES, but in 1996, the project was renamed to PostgreSQL to reflect its support for SQL. PostgreSQL features transactions with Atomicity, Consistency, Isolation, Durability (ACID) properties, automatically updatable views, materialized views, triggers, foreign keys, and stored procedures. It is designed to handle a range of workloads, from single machines to data warehouses or Web services with many concurrent users. It was the default database for macOS Server and is also available for Linux, FreeBSD, OpenBSD, and Windows.
MySQL
MySQL is a popular open-source relational database management system (RBDMS) used by enterprises of all sizes. This connector integrates data of various types from your MySQL database via MySQL Server.
Oracle Database
Oracle Database is a proprietary multi-model database management system produced and marketed by Oracle Corporation. It is a database commonly used for running online transaction processing, data warehousing and mixed database workloads including NoSQL workloads.
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