Let's Talk Snowflake Streams and Change Data Capture

In this article, I want to talk about all things CDC, or Change Data Capture, what it is about, how this can be implemented in Snowflake and why Developers, Engineers and users of Snowflake need to use these.

Features change from time to time with new features being added regularly, it is recommended that you review the documentation for the latest on what specific features are included with any of the Editions.

Open Table of Contents

Understanding Change Data Capture (CDC)
Introduction to Snowflake Streams
How Snowflake Streams Work
Types of Snowflake Streams
Implementing Snowflake Streams
Practical Use Cases
Best Practices for Using Snowflake Streams
Conclusion

Understanding Change Data Capture (CDC)

Change Data Capture (CDC) is a method used to detect and capture changes—such as inserts, updates, and deletes—in a database. By capturing these changes, CDC ensures that downstream systems or applications have access to the most recent data without the need for full data reloads. This approach enhances efficiency, reduces latency, and supports real-time analytics and reporting.

Introduction to Snowflake Streams

Snowflake Streams provide CDC capabilities within the Snowflake ecosystem. A Stream is an object that records data manipulation language (DML) changes made to a specified source object, such as a table or view. It captures metadata about each change, allowing users to query and act upon the altered data effectively.

How Snowflake Streams Work

When a Stream is created on a source object, it establishes a reference point, known as an offset, marking the current state of that object. Subsequent DML operations on the source object are tracked by the Stream, which records metadata about each change. The Stream itself does not store the actual data but maintains information about the type of operation performed and the state of the data before and after the change.

To facilitate this tracking, Snowflake Streams introduce hidden metadata columns to the source object:

METADATA$ACTION: Indicates the DML operation (e.g., INSERT, DELETE) recorded.
METADATA$ISUPDATE: Shows whether the operation was part of an UPDATE statement.
METADATA$ROW_ID: Provides a unique identifier for the row affected by the DML operation.

These metadata columns enable users to identify and process changes efficiently.

Types of Snowflake Streams

There are three types of Snowflake Streams to cater to various use cases:

Standard Streams: Track all DML changes, including inserts, updates, and deletes.
Append-Only Streams: Record only insert operations, ignoring updates and deletes. These are useful when only new data additions are of interest.
Insert-Only Streams: Specifically designed for external tables, capturing only insert operations without tracking deletions.

Implementing Snowflake Streams

Implementing Snowflake Streams involves several steps:

Creating a Stream: Use the CREATE STREAM statement to establish a Stream on a source object. For example:

CREATE OR REPLACE STREAM my_stream ON TABLE my_table;

Querying the Stream: Retrieve changes captured by the Stream using a SELECT statement:

SELECT * FROM my_stream;

Consuming the Stream: To process the captured changes, incorporate the Stream’s data into your data pipeline, ensuring that the Stream’s offset advances only when its contents are consumed in a DML transaction.

Practical Use Cases

Snowflake Streams are instrumental in various scenarios:

Real-Time Data Integration: Synchronise data across systems by capturing and applying changes as they occur.
Incremental Data Loading: Efficiently load only the changed data into data warehouses or lakes, reducing processing time and resource utilisation.
Audit Logging: Maintain detailed records of data modifications for compliance and auditing purposes.

Best Practices for Using Snowflake Streams

To maximise the effectiveness of Snowflake Streams:

Regular Consumption: Regularly consume Stream data to prevent it from becoming stale, as Streams can become stale if their offset moves outside the data retention period of the source table.
Monitor Stream Staleness: Utilise the STALE_AFTER metadata field to predict when a Stream might become stale and take proactive measures.
Optimise Performance: Manage the number of Streams and their consumption patterns to avoid performance degradation.

Conclusion

Snowflake Streams provide a powerful mechanism for Change Data Capture, enabling developers and data engineers to track and respond to data changes efficiently. By understanding their functionality, implementing them thoughtfully, and adhering to best practices, organisations can enhance their data integration processes, support real-time analytics, and maintain synchronised datasets across their systems.