Complete Guide to Change Data Capture
Struggling to keep analytics, AI models, and apps fed with real-time data changes from databases without crashing your source systems? Latency and complexity in traditional pipelines cost enterprises an average of $15 million yearly in delayed decisions. This complete guide delivers the exact steps, tools, and best practices to implement low-latency CDC successfully, from setup to production.
Most companies today struggle with stale data. You run a report on Monday morning, but the numbers are from Friday night. In a fast-paced market, that gap is a major problem. You need to know what is happening right now, not what happened three days ago.
This is where Change Data Capture (CDC) comes in. It moves data from your source databases to your analytics platforms or other applications as soon as an event happens. Instead of waiting for a nightly batch job, you get a continuous stream of updates. It keeps your systems in sync and your decision-making sharp.
What Is Change Data Capture (CDC)?
Change Data Capture (CDC) is a method for identifying and tracking changes in a database. Instead of copying an entire database every time you need to move data, CDC looks only for the specific events that modified the data: inserts, updates, and deletes.
Think of it as a news feed for your database. When a customer updates their address or a new order comes in, CDC flags that specific change and sends it downstream immediately. This approach is much faster and lighter than traditional replication methods.
"Change data capture (CDC) is a set of software design patterns. It allows users to detect and manage incremental changes at the data source." - Informatica (Informatica)
Key Benefits of Using CDC
The main reason teams switch to CDC is efficiency. Moving huge chunks of data that haven't changed is a waste of bandwidth and computing power. CDC solves this by focusing strictly on the deltas—the difference between the old state and the new state.
Why this matters for your business:
- Greater efficiency: You only synchronize changed data, which is far more efficient than full replication.
- Lower impact: It captures updates without slowing down your production database.
- Faster load times: Because you transfer less data, your target systems update much quicker.
- Real-time accuracy: Your analytics teams work with current data, not yesterday's snapshot.
How Change Data Capture Works
At a high level, CDC works by monitoring the source system for activity. Once it detects a change, it extracts the relevant details and transforms them into a format your destination system can understand. Finally, it loads that data into a data warehouse, data lake, or another application.
While the goal is always the same—moving data fast—the way tools achieve this varies. There are three primary methods for capturing these changes, each with different pros and cons regarding speed and system impact.
Log-Based CDC
This is the gold standard for modern data pipelines. Every database keeps a transaction log (like the WAL in PostgreSQL or binlog in MySQL) to record every event for crash recovery.
Log-based CDC reads these logs directly. It does not run queries against your tables, so it has almost zero impact on database performance. It captures every single change, including deletes, ensuring your target data is a perfect mirror of the source.
Trigger-Based CDC
Before log-based tools became common, developers used database triggers. You write a script that runs automatically every time a row is inserted, updated, or deleted. This script copies the change to a separate "shadow" table.
The problem is overhead. Triggers run inside the database transaction. If you have a high-volume application, these triggers can severely slow down your primary application, causing latency for your actual users.
Query-Based CDC
This method involves regularly polling the database with SQL queries. You might run a query like SELECT * FROM orders WHERE updated_at > last_check_time.
It is simple to set up but has significant flaws. It puts a heavy load on your database because it constantly scans tables. Worse, it usually cannot detect when a record is deleted, leading to data discrepancies where your analytics show rows that no longer exist.
CDC vs. ETL and ELT Pipelines
Traditionally, companies used batch ETL (Extract, Transform, Load) processes. You would wait until midnight, extract the day's data, transform it, and load it. This created "batch windows" where data was unavailable or slow.
CDC changes the "Extract" phase fundamentally:
- Batch ETL: Extracts large snapshots at scheduled intervals.
- CDC: Extracts individual events continuously in real-time.
CDC effectively turns batch ETL into streaming ELT. It feeds the pipeline constantly, eliminating the need for massive nightly bulk loads. This means your "T" (transformation) can happen on fresh data immediately after it arrives in the warehouse.
Essential Use Cases for CDC in Enterprises
CDC is not just for analytics; it powers operational workflows across the enterprise. When systems need to talk to each other without delay, CDC is usually the connector.
Common enterprise applications include:
- Zero-downtime migrations: Moving to the cloud while keeping the old system live.
- Multi-system synchronization: Keeping your CRM, ERP, and search indexes (like Elasticsearch) aligned.
- Real-time fraud detection: analyzing financial transactions the moment they occur.
Healthcare: Instantly updated dashboards and databases for accurate patient care and costs. (Domo)
Choosing the Right CDC Tool
Selecting a CDC tool comes down to reliability and latency. You need a solution that won't break when your data volume spikes or your schema changes.
Look for these three features:
- Native Connectors: Generic connectors often fail at scale. You want tools built specifically for your database (e.g., native Postgres or MongoDB connectors).
- Schema Evolution Support: If a developer adds a column, the pipeline should adapt automatically, not crash.
- Low Latency: The time between an event occurring and it appearing in the destination should be measured in seconds or milliseconds, not minutes.
Step-by-Step Guide to Implementing CDC
Implementing CDC is a process that moves from the source outward. It requires coordination between database administrators and data engineers to ensure security and stability.
Here is the general workflow:
- Configure the Source: Enable the necessary logging features on your database.
- Connect the Tool: Authenticate your CDC provider with the source.
- Map the Data: Select which tables and columns you want to replicate.
- Start the Stream: Begin the initial snapshot (backfill) and switch to streaming.
Preparing Your Source Database
You cannot just point a tool at a database and expect it to work. You must enable the transaction logs first. For PostgreSQL, this means setting the wal_level to logical. For MySQL, you need to enable binary logging (binlogs).
You also need to create a dedicated user for the CDC tool. This user needs strictly defined permissions—usually REPLICATION privileges and SELECT access on the specific tables you intend to track.
Setting Up Capture and Delivery
Once the database is ready, you configure the connection. This involves entering your host, port, and credentials into your CDC platform.
Next, define your destination. This could be a data warehouse like Snowflake or a stream like Kafka. You will map your source tables to the destination schema. A good tool will handle type conversion automatically, ensuring a timestamp in MySQL looks like a timestamp in BigQuery.
Testing and Going Live
Before flipping the switch for production, run a validation test. Compare the row counts in your source table against the destination. Check edge cases, such as updating a row twice in quick succession.
Once verified, start the initial sync. The tool will copy existing data (historical backfill) and then seamlessly switch to reading the logs for new changes. Monitor the lag closely during this transition.
Best Practices for CDC Deployments
Deploying CDC is a shift from batch thinking to stream thinking. To keep your pipelines healthy, you need to design for continuity and resilience.
Follow these core principles:
- Start small: Don't replicate your entire database at once. Start with a few high-value tables.
- Isolate workloads: If possible, run CDC against a read-replica database to ensure absolutely zero impact on the primary writer node.
- Automate recovery: Ensure your tool can automatically restart and resume from the last checkpoint if the network fails.
Minimize Impact on Source Systems
The number one rule of CDC is "do no harm." Your analytics requirements should never degrade the performance of the application your customers are using.
This is why log-based CDC is preferred. It reads files from the disk rather than querying the database engine. If you must use query-based methods, schedule them during off-peak hours, though this defeats the purpose of real-time data.
"Monitoring and extracting changes as they occur with CDC simplifies the replication process, is incredibly efficient, and consumes fewer compute resources in the database so there is minimal, if any, performance impact." - Matillion (Matillion)
Manage Schema Evolution
Databases change. Developers add columns, rename tables, or change data types. If your CDC pipeline expects a static structure, these changes will break it.
You need a strategy for schema drift. Advanced CDC tools detect these DDL (Data Definition Language) changes and apply them to the destination automatically. This prevents your data engineering team from being woken up at 3 AM because a deployment broke the pipeline.
Monitor Latency and Reliability
In a batch world, you check if the job finished. In a streaming world, you check latency (how far behind is the data?) and throughput (how many rows per second?).
Set up alerts for replication lag. If latency spikes from 2 seconds to 20 minutes, something is wrong. It could be a network issue, or a massive bulk update on the source system that is clogging the pipe. You need visibility into these metrics to trust your data.
Common Mistakes to Avoid with CDC
Even with good tools, implementation errors can derail a project. One common mistake is ignoring deletes. If you use a query-based approach, records deleted from the source often remain in your warehouse, corrupting your analysis.
Another pitfall is underestimating data volume. Initial loads are heavy, and transaction logs can grow very fast if the CDC tool stops reading them. If the tool goes down, the database keeps the logs until they are acknowledged. If you aren't careful, this can fill up your database disk and crash the server.
Why Popsink Excels in Low-Latency CDC
For enterprises where data freshness is critical, generic tools often fall short. Popsink is built specifically for low-latency, mission-critical environments.
Popsink differentiates itself with native connectors that are hardened for production stability. Unlike tools that wrap open-source scripts, Popsink's architecture ensures high availability and scale. Whether you are a startup or a Fortune 100 company, Popsink delivers the fresh data needed for AI and advanced analytics without the maintenance headaches of fragile pipelines.
Frequently Asked Questions
What are popular open-source CDC tools?
Debezium and Kafka Connect are leading open-source CDC tools. Debezium captures changes from MySQL, PostgreSQL, and MongoDB via logs, integrating seamlessly with Apache Kafka for streaming to destinations.
How does CDC handle data conflicts during replication?
CDC minimizes conflicts by applying changes in commit order from transaction logs. Tools like Debezium can be set up for exactly-once semantics, ensuring updates and deletes propagate accurately without duplicates or losses.
Can CDC work with NoSQL databases?
Yes, CDC supports NoSQL databases like MongoDB via oplog tailing and Cassandra through commit logs. It captures inserts, updates, and deletes in real-time, enabling synchronization to analytics platforms.
What is the typical latency achieved with log-based CDC?
Log-based CDC tools like Popsink achieve sub-second latency, often 1-5 seconds end-to-end, by directly parsing logs without querying the database.
How do you secure CDC pipelines?
Secure CDC with TLS encryption for data in transit, role-based access for database users limited to logs, and VPC peering for connections. Regularly rotate credentials and audit access logs for compliance.
