Snowflake Dynamic Tables: Change Data Capture Simplified

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Snowflake Dynamic Tables: Change Data Capture Simplified

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Source: https://www.linkedin.com/pulse/snowflake-dynamic-tables-change-data-capture-arabinda-mohapatra-j4bcf?trackingId=xP9nN9ecSTuF1gxQ9dl6jQ%3D%3D

Snowflake Dynamic Tables: Change Data Capture Simplified

Arabinda Mohapatra

Arabinda Mohapatra

Running Kafka streams after dark, diving into genetic code by daylight, and wrestling with Databricks and Tableflow in every spare moment—sleep is optional

# Abstract

Change Data Capture (CDC) in Snowflake is the powerful capability to identify and process only the changed data in your tables. This enables efficient, incremental data pipelines that are faster, more cost-effective, and simpler to maintain than full-load processes.

  1. Benefit of CDC in Snowflake

  • The primary benefit of using Change Data Capture (CDC) in Snowflake is efficiency. Instead of periodically reloading entire datasets—a time-consuming and resource-intensive process—CDC allows you to pinpoint only the new, updated, or deleted rows since the last check.
  • This incremental approach drastically reduces compute costs and processing time, enabling near real-time data updates for analytics, dashboards, and downstream applications. With features like Streams and the newer Dynamic Tables, Snowflake automates much of the CDC logic, allowing you to build robust, low-latency data pipelines with minimal operational overhead.

2. Snowflake Dynamic Tables

  • Snowflake Dynamic Tables automatically materialize the results of a SQL query. They simplify incremental data loading by internally tracking changes in the source data and refreshing only new or modified rows, eliminating the need for manual change identification and merge operations.
  • Dynamic tables support Time Travel, Masking, Tagging, Replication etc. just like a standard Snowflake table.

use cases

  • Slowly changing dimensions (SCDs): Simplify SCD creation by incrementally processing change data streams to handle out-of-order updates.
  • Joins and aggregations: Enable fast queries by incrementally pre-computing complex joins and aggregations.
  • Batch to streaming transitions: Seamlessly switch from batch to streaming processing and control data freshness with a single command.

when to use Dynamic Tables

  1. Declarative Pipelines: To materialize data outcomes without writing custom code or manually managing data dependencies.
  2. Automatic Orchestration: To have Snowflake automatically handle all refresh scheduling and execution based on your data freshness targets.
  3. Simplified Transformations: To easily chain multiple tables together to build a multi-step data transformation pipeline.
  4. Limited Fine-Grained Control: When you do not need precise, minute-by-minute control over refresh schedules and are comfortable with target-level freshness settings

3. How to Create Snowflake Dynamic Tables?

Below is the syntax for creating Dynamic Tables in Snowflake.

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1. <name>:

Name of the dynamic table.

2. TARGET_LAG:

Specifies the lag between the dynamic table and the base table on which the dynamic table is built.

The value of TARGET_LAG can be specified in two different ways.

  • ‘<num> { seconds | minutes | hours | days }’ : Specifies the maximum amount of time that the dynamic table’s content should lag behind updates to the base tables. Example: 1 minute, 7 hours, 2 days etc.
  • DOWNSTREAM: Specifies that a different dynamic table is built based on the current dynamic table, and the current dynamic table refreshes on demand when the downstream dynamic table needs to refresh.

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3. <warehouse_name>:

Specifies the name of the warehouse that provides the compute resources for refreshing the dynamic table.

4. <query>:

Specifies the query on which the dynamic table is built.


4. How do Snowflake Dynamic Tables work?

  • Raw Data Foundation



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  • Create Dynamic Table

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  • Simulate Real-World Changes



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The automated refresh process identifies the changes in the results of the query defined and does an incremental refresh of data in the Dynamic table. Note that this is NOT a full data refresh



Advantages of Snowflake Dynamic Tables

1. Automate incremental data processing with simple SQL CREATE TABLE statements.

2. Eliminate the manual orchestration of tasks, simplifying data pipeline creation.

3. They automatically handle dependencies and scheduling between tables.

4. Improve cost and performance by only processing new or changed data.

5. Provide built-in data freshness controls with a target lag setting.

6. Unify streaming and batch processing into a single, declarative framework.

7. Reduce pipeline complexity, moving from directed acyclic graphs to SQL declarations.

8. Ensure data consistency by providing correct and up-to-date results.

9. Scale compute resources automatically to meet your specified latency targets.

10. Represent a major shift towards a fully managed, declarative pipeline experience on Snowflake.


Reference:

https://www.snowflake.com/en/developers/guides/getting-started-with-dynamic-tables/


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