Snowflake Streams Data Processing
12 Jan 2025

The Who, Where, What, How, and When of Snowflake Streams

Snowflake Streams enable tracking and processing of changes in data, offering a powerful tool for implementing Change Data Capture (CDC) workflows. This guide explores the who, where, what, how, and when of Snowflake Streams to help you make the most of this feature.

Who Uses Snowflake Streams?

Snowflake Streams are utilized by:

  • Data Engineers: To build real-time or near-real-time data pipelines.
  • ETL Developers: For implementing incremental data processing workflows.
  • Data Analysts: To track and analyze changes in datasets.
  • Data Scientists: To capture dynamic data updates for model training or predictions.
  • Developers: For implementing event-driven architectures.

Where Are Snowflake Streams Used?

Snowflake Streams are applied in:

  • Data Pipelines: For processing incremental changes in source data.
  • ETL Workflows: To handle inserts, updates, and deletes in target systems.
  • Real-Time Analytics: To provide up-to-date insights on changing datasets.
  • Audit Logging: To track changes to critical business tables.
  • Event-Driven Systems: To trigger downstream processes based on data changes.

What Are Snowflake Streams?

Snowflake Streams are objects that capture changes (inserts, updates, deletes) to a table. These changes can then be processed downstream to maintain data consistency or generate insights.

Key Features

  • Change Tracking: Records row-level changes (inserted, updated, deleted) to a table.
  • Non-Intrusive: Tracks changes without modifying the underlying table.
  • Incremental Processing: Allows for efficient data pipeline execution by only processing new changes.
  • Support for Multiple Types: Tracks changes for standard and materialized views, as well as tables.

Stream Types

  1. Standard Streams: Captures changes from a table or view.
  2. Append-Only Streams: Tracks only inserted rows, ignoring updates and deletes.

How Do Snowflake Streams Work?

1. Creating a Stream

Define a stream on a table or view using the CREATE STREAM statement.

Standard Stream Example

CREATE OR REPLACE STREAM sales_stream ON TABLE sales;

Append-Only Stream Example

CREATE OR REPLACE STREAM new_sales_stream ON TABLE sales APPEND_ONLY = TRUE;

2. Querying Streams

Use the SELECT statement to query a stream and view captured changes:

SELECT * FROM sales_stream;

The result includes metadata such as:

  • Metadata$Action: Type of change (INSERT, UPDATE, DELETE).
  • Metadata$IsUpdate: Indicates if the row was updated.

3. Processing Streams

Use streams in conjunction with MERGE or INSERT statements to process changes.

Example: Applying Changes to a Target Table

MERGE INTO target_table t
USING sales_stream s
ON t.id = s.id
WHEN MATCHED THEN
  UPDATE SET t.amount = s.amount
WHEN NOT MATCHED THEN
  INSERT (id, amount) VALUES (s.id, s.amount);

4. Managing Streams

  • View Stream Metadata: 
    SHOW STREAMS;
  • Drop a Stream: 
    DROP STREAM sales_stream;

When Should You Use Snowflake Streams?

1. Incremental Data Processing

  • When: You need to process only new changes instead of full data loads.

2. Change Data Capture (CDC)

  • When: Tracking inserts, updates, and deletes for data replication or synchronization.

3. Real-Time Analytics

  • When: Continuously updating dashboards or reports with fresh data.

4. Event-Driven Workflows

  • When: Triggering downstream processes based on data changes.

Best Practices for Snowflake Streams

  1. Optimize Stream Usage:
    • Query streams regularly to avoid excessive accumulation of changes.
  2. Use Append-Only Streams When Possible:
    • Simplifies processing when updates and deletes are not needed.
  3. Combine with Tasks:
    • Automate stream processing with Snowflake Tasks for scheduled execution.
  4. Monitor Stream Performance:
    • Use metadata views to track stream usage and troubleshoot issues.
  5. Design for Efficiency:
    • Use MERGE operations wisely to minimize resource usage during processing.

Conclusion

Snowflake Streams provide a robust solution for tracking and processing data changes, enabling real-time analytics, incremental processing, and event-driven workflows. By understanding their capabilities and best practices, you can build efficient and scalable data pipelines tailored to your needs.

Start using Snowflake Streams today to unlock the full potential of your data operations.

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