What is CDC in Databricks?

In Databricks, CDC stands for Change Data Capture.

Change Data Capture (CDC) is the process that captures the changes in records made to a data storage like Database, Data Warehouse, etc. These changes usually refer to operations like data deletion, addition and updating.

While Databricks itself is not typically the source system generating CDC feeds (that's usually transactional databases like PostgreSQL, MySQL, SQL Server, etc.), it is a primary platform for processing and utilizing CDC data. Databricks, powered by Delta Lake, is highly effective at handling the stream of changes captured from source systems to build and maintain up-to-date data lakes and data warehouses.

Why is CDC Important in Databricks?

Processing CDC data within Databricks allows organizations to keep their analytical data stores synchronized with operational systems in near real-time or in batches. This is crucial for:

• Building Data Warehouses: Accurately reflecting operational changes in a centralized analytical store.
• Maintaining Data Lakes: Ensuring the raw or refined data lake layers are up-to-date.
• Auditing and Compliance: Tracking historical changes to data.
• Real-time Analytics: Powering dashboards and reports with fresh data.

How Databricks Handles CDC

Databricks, leveraging its Lakehouse architecture and Delta Lake capabilities, provides robust methods for processing CDC streams. Instead of simply overwriting data, CDC streams record what changed, when, and sometimes how. Databricks can apply these changes to target Delta tables efficiently.

Common patterns for processing CDC in Databricks include:

1. Ingestion: Capturing CDC events from various sources (e.g., Kafka, cloud storage, database replication tools) into raw Delta tables.
2. Processing and Merging: Applying the captured changes (inserts, updates, deletes) to a target Delta table representing the current state of the data. Delta Lake's MERGE INTO command is particularly well-suited for this, allowing atomic upserts and deletes based on the CDC feed.
3. Serving: Making the updated data available for querying, reporting, and machine learning.

Example: Applying CDC with Delta Lake

Imagine a CDC stream from a customer database containing records flagged as 'INSERT', 'UPDATE', or 'DELETE'. Using Databricks and Delta Lake, you can process this stream:

• Load the raw CDC events into a staging Delta table.
• Use a MERGE INTO statement on your main customers Delta table, applying the changes from the staging table based on a unique key (e.g., customer_id) and the operation type ('INSERT', 'UPDATE', 'DELETE').

This process ensures your main customers table is a correct, merged view of the data, reflecting all the operations from the source system without needing full table scans or complex logic for each change type.

Benefits of Using Databricks for CDC Processing

• Simplified Architecture: Delta Lake's ACID properties make applying complex changes reliable.
• Performance: Optimized processing engines handle large volumes of change data efficiently.
• Scalability: Databricks scales to process high-throughput CDC streams.
• Reliability: Atomic transactions prevent data corruption during merge operations.
• Cost-Effective: Processes data on scalable cloud storage instead of traditional data warehouses.

In summary, while CDC is a general data integration concept, within the context of Databricks, it primarily refers to the practice of ingesting and processing change data streams from external sources to maintain accurate, up-to-date analytical datasets on the platform, leveraging the power of Delta Lake.