How to Implement Change Data Capture (CDC) in Snowflake in 4 Steps

The need for accurate and timely data has never been more critical for businesses. Whether it’s supporting real-time analytics, enabling machine learning models, or providing up-to-date insights for decision-making, having fresh data can make all the difference.

Snowflake, a cloud-based data platform, offers change data capture (CDC) capabilities that allow businesses to efficiently capture and process data changes, ensuring that data-driven decisions are based on the most recent information. In this guide, we will explore how to implement CDC in Snowflake using Snowflake Streams in a few straightforward steps.

What is Snowflake?

Snowflake is a cloud-based data warehousing solution that allows organizations to easily store, manage, and analyze large volumes of data. Unlike traditional data warehouses, Snowflake is built for the cloud, offering scalability, flexibility, and high performance without the overhead of managing hardware or software. It supports multi-cluster, shared data architecture and provides native support for semi-structured data formats like JSON, Parquet, and ORC. With its pay-as-you-go pricing model, Snowflake allows businesses to optimize costs while scaling resources according to demand.

Snowflake's platform is highly popular for a wide range of use cases, from data lakes and data sharing to real-time analytics. Its robust ecosystem and integration capabilities make it an excellent choice for organizations looking to modernize their data infrastructure.

For more insights on how Snowflake can be utilized across various business scenarios, check out our article on the 9 Most Common Snowflake Use Cases.

What is change data capture (CDC)?

Change data capture (CDC) is a set of techniques used to identify and track changes made to data in a source system, such as a database, and then propagate these changes to a downstream system, such as a data warehouse or analytics platform. CDC plays a crucial role in keeping data synchronized across systems in real-time or near real-time, enabling businesses to maintain an accurate and up-to-date view of their data.

CDC is particularly valuable in environments where data is constantly evolving and needs to be ingested into analytical platforms to derive insights without delays. By capturing data changes—such as inserts, updates, and deletions—CDC ensures that the data in the target system reflects the most current state of the source.

What is Snowflake Stream?

Snowflake Stream is a powerful feature in Snowflake that enables change data capture by allowing users to track changes to a table in real-time or near real-time. A stream object in Snowflake keeps a continuous log of changes (inserts, updates, and deletes) that have occurred on a specified table, referred to as the source table. This allows users to create an efficient pipeline for data ingestion, processing, and analytics without the need for complex manual processes or additional ETL (extract, transform, load) tools.

When a stream is created on a table, Snowflake maintains an offset of changes, allowing users to query the stream to obtain only the rows that have changed since the last time the stream was queried. This makes it easier to build incremental data pipelines, as the stream can be configured to automatically capture the new changes, which can then be propagated to other tables or data stores.

Key features of Snowflake Stream include:

• Real-time change tracking: Streams can capture data changes as they happen, enabling near real-time updates to downstream systems.
• Integration with Snowflake tasks: Streams can be combined with tasks, another Snowflake feature, to automate the process of processing and loading changes.
• Simplified data pipelines: Streams remove the need for complex ETL processes by directly capturing and propagating changes.

Snowflake Streams play a crucial role in implementing CDC within Snowflake by providing a native, scalable, and efficient mechanism to track data changes. This feature allows businesses to quickly and easily implement CDC workflows that support various data integration and analytics use cases.

7 Benefits of using Snowflake to stream CDC data

Implementing Snowflake CDC using Streams provides numerous advantages that help organizations streamline their data management processes and optimize analytics workflows. Here are several key benefits:

Enhanced data sharing and collaboration

Snowflake CDC with Streams enables seamless data sharing across different teams, departments, or even external partners. By capturing changes in near real-time, Snowflake ensures that all stakeholders have access to the most up-to-date information. This capability is especially useful for organizations that need to maintain data consistency across multiple regions or business units.

For example, a global retail company can use Snowflake CDC to synchronize sales data from multiple stores into a central data warehouse, ensuring that regional managers, analysts, and decision-makers always have access to the latest sales figures, inventory levels, and customer feedback. This real-time data sharing can enhance collaboration across the organization, allowing for faster and more informed decision-making.

Simplified integration with existing systems

Integrating Snowflake Streams with existing systems and applications is straightforward thanks to Snowflake's support for standard SQL and its ability to seamlessly connect with various data sources and third-party tools. This reduces the complexity and cost associated with building and maintaining custom ETL pipelines.

By using Snowflake Streams, organizations can simplify their data integration workflows. Instead of developing complex scripts or deploying multiple tools to track data changes, Snowflake provides a native solution that is easy to set up and manage. This helps reduce the total cost of ownership (TCO) for data integration and allows IT teams to focus on more strategic tasks.

Accelerated data ingestion and processing

Snowflake Streams provide a direct way to capture data changes and ingest them into the data warehouse in near real-time, which is critical for applications that require timely insights, such as fraud detection, customer behavior analysis, and predictive analytics.

For example, a financial services company could use Snowflake CDC to monitor transactions for signs of fraudulent activity. By streaming transaction data changes directly into an analytics platform, the company can quickly identify and respond to potential fraud incidents, protecting both their business and their customers. This accelerated data ingestion ensures that data is always fresh and ready for analysis, enabling organizations to act swiftly and make data-driven decisions with confidence.

Improved data accuracy and consistency

One of the challenges of traditional data integration methods is ensuring data accuracy and consistency across multiple systems. With Snowflake CDC, data changes are captured as they occur and are automatically synchronized across all target systems, reducing the risk of discrepancies or data loss.

By using Snowflake Streams, organizations can minimize the time window during which data is out of sync between the source and the destination, thereby maintaining data consistency. This is particularly important in industries like healthcare and finance, where data accuracy is crucial for regulatory compliance and operational integrity.

Scalable and cost-effective data management

Snowflake’s architecture is designed to handle large volumes of data efficiently, and its CDC capabilities allow for scalable data management without the overhead of manual intervention. Snowflake Streams automatically scale with your data, capturing and processing changes in parallel to ensure minimal impact on performance.

Additionally, Snowflake’s usage-based pricing model allows businesses to pay only for the resources they consume. When combined with the efficiency of CDC, this makes Snowflake a cost-effective solution for organizations that need to manage large-scale data changes without incurring excessive costs.

Real-time analytics and insights

Snowflake Streams enable real-time analytics by capturing data changes as they happen and immediately making those changes available for analysis. This real-time data flow is crucial for businesses that need to make decisions quickly based on the latest data.

For example, e-commerce companies can use Snowflake CDC to continuously update their recommendation engines with the latest customer interactions, such as clicks, views, and purchases. This enables them to offer more relevant product recommendations to customers in real time, increasing the likelihood of conversions and enhancing the overall customer experience.

Enhanced data governance and compliance

Snowflake CDC with Streams also supports improved data governance and compliance. By providing a comprehensive log of all data changes, Snowflake enables organizations to maintain a detailed audit trail, which is crucial for compliance with data protection regulations such as GDPR, HIPAA, and CCPA.

Organizations can use Snowflake’s auditing capabilities to track data changes over time, ensuring that all modifications are properly documented and easily traceable. This transparency helps organizations meet regulatory requirements and build trust with customers and stakeholders by demonstrating a commitment to data privacy and security.

How to set up Snowflake CDC with streams in 4 steps

Implementing CDC in Snowflake using Streams involves several steps. Below is a detailed step-by-step guide to help you set up CDC in Snowflake:

1. Create a stream

To begin, you will need to create a stream on the source table that you want to monitor for changes. This stream will capture all data modifications (inserts, updates, deletes) on the table.

CREATE OR REPLACE STREAM my_stream ON TABLE my_table;

This SQL command creates a stream named my_stream on the table my_table. The stream will automatically capture any changes made to my_table from this point forward.

2. Define a target table

Next, define the target table where the captured changes will be stored or processed. This table will be the destination for the CDC data that is captured by the stream.

CREATE OR REPLACE TABLE target_table AS

SELECT * FROM my_table;

This command creates a new table named target_table that will hold the data captured by the stream.

3. Implement data transfer

To implement the data transfer, you need to write a query that selects the changes from the stream and inserts them into the target table. This step involves using a Snowflake task, which automates the data transfer process.

CREATE OR REPLACE TASK cdc_task

WAREHOUSE = my_warehouse

SCHEDULE = 'USING CRON * * * * *'

AS

INSERT INTO target_table

SELECT * FROM my_stream WHERE METADATA$ACTION = 'INSERT';

This task is scheduled to run every minute (using a cron schedule), and it inserts the new rows captured by the stream into the target table.

4. Monitor stream activity

It’s essential to monitor the stream activity to ensure that the data changes are being captured correctly and efficiently. You can use the following SQL command to check the status of the stream and review its history:

SHOW STREAMS;

By monitoring the stream, you can identify any issues or delays in capturing data changes and take appropriate actions to resolve them.

Get more out of Snowflake with CData Sync

By implementing Snowflake CDC with Streams, you can maintain an up-to-date view of your data across systems, driving more accurate analytics and business insights. CData Sync can help you further enhance your CDC initiatives. Sync helps you easily and automatically replicate your business data to Snowflake in a timely fashion, maximizing the value of your Snowflake-specific CDC workflows.