A Quick Overview of Snowflake CDC

December 12, 2023

Reading Time: 3 minutes

Data in an operating system changes rather often in the modern environment. The data in traditional data warehouses, however, is out of date. Current information keeping in systems that are in use.Before being periodically transferred to the Data Warehouse. With its transformation into a prominent cloud-based SaaS (Software-as-a-service) data platform, Snowflake is a contemporary data warehouse. For effective processing, Snowflake CDC facilitates the quick detection of new modifications or data in a table.

Table of Contents

What is Snowflake?

A cutting-edge SaaS cloud data warehousing system known as Snowflake. It is based on infrastructure from Google Cloud, Microsoft Azure, or Amazon Web Service, which offers an infinite platform for data archiving and retrieval. A unique SQL Database Engine with a cloud-specific design powers Snowflake Data Warehouse.

Snowflake CDC is ideal for businesses that don’t want to dedicate resources to in-house server setup, maintenance, or support as it doesn’t require any hardware or software to install, configure, or administer. Businesses can easily trade and share data securely in real-time using any ETL solution thanks to Snowflake’s sharing and security capabilities. The design of Snowflake enables Big Data flexibility. Snowflake stands apart from other data warehouses on the market because of its scalability and user-friendliness.

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Essential Elements of a Snowflake

The following are the factors that contribute to Snowflake’s enormous popularity.

Caching Paradigm: To swiftly provide results from the cache, Snowflake CDC uses a caching paradigm. To prevent the report from generating again, it makes use of Persistent Query results.
Standard and Extended SQL Support: Snowflake can query data using the majority of SQL’s DDL and DML procedures. Furthermore, it facilitates lateral views, stored processes, complex DML transactions, and more.
Scalability: It is commonly known that Snowflake’s architecture allows its “Compute” and “Storage” parts to scale independently of one another. Customers may thus only pay for the services they utilize.
Security: Snowflake offers several enhanced authentication methods. This covers SSO using Federated Authentication and Two-Factor Authentication.
Support for Semi-Structured Data: Snowflake’s design allows for the storing of Semi-Structured and Structured data in the same space by supporting the VARIANT schema on the Read data type.

Change Data Capture (CDC): What is it?

Change Data Capture (CDC) is the best way to record data movement in databases almost instantly. The term “CDC” describes a group of software design patterns that are used in databases to identify and monitor changes in data. It sets off the data-related event, resulting in the execution of a certain action for each Change Data Capture. To execute effective data analytics, all businesses need to have access to real-time data streams. CDC offers near-real-time data movement by processing data as soon as new database events occur.

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In high-velocity data contexts, Snowflake CDC enables dependable, low-latency, and scalable data replication by capturing and streaming events in real time. By using incremental data loading, it does away with the need for bulk data loading. In this approach, databases or data warehouses stay active so they can carry out particular tasks as soon as a Change Data Capture event takes place. Additionally, businesses may use CDC to transmit new data updates in almost real-time to team members and BI (Business Intelligence) tools, keeping them informed.

Snowflake Streams: What is it?

The data in your systems changes quite a bit in today’s data-driven market, and loading the entire data set into Snowflake each time would be a difficult effort. You’ll lose money and time as a result of it. Snowflake CDC is useful in this situation. With just a few instructions, you can successfully implement CDC in Snowflake, thanks to the notion of Streams.

All DML changes made to rows in a source table are essentially tracked by a Snowflake CDC Stream object, which also keeps the information associated with each modification. The modified data get later uses this information in a table that connects two transactional points in time.

As the current version of the table relative to an initial point in time, Snowflake Streams takes an initial picture of every row in the source table. After that, each time you add, modify, or remove data from your source table, Streams turns on Snowflake CDC. Every time a DML modification commits, then Streams will have more columns. Therefore, you can simply use the MERGE statement to merge only the changes from source to target by collecting the CDC Events.

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Conclusion

The outdated methods of applying CDC have been completely superseded by Snowflake Change Data Capture. Snowflake CDCs make it well worth adopting even if Snowflake is already a market-leading cloud-based data platform recognized for its speed and adaptable warehousing solutions.

In situations when millions of records exchange every day and you just want to update the modified ones, Snowflake Change Data Capture comes in extremely handy.