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A Comprehensive Guide to Snowflake Change Data Capture

In today’s fast-paced data analytics industry, integration and analysis that require real-time access to reliable information are vital for businesses to flourish. Snowflake Change Data Capture (CDC) is a revolutionary technology that allows businesses to collect and replicate changes to data in real time. In this complete guide, we dive into the details of Snowflake CDC, exploring its advantages, and key features. Learn how Snowflake CDC revolutionizes data integration with real-time insight, simplified processes, better data quality, and scaling.

What is Snowflake?


Snowflake is a contemporary Cloud Data Warehousing solution offered as a SaaS service. Based using Amazon Web Service, Microsoft Azure, and Google Cloud infrastructure, which provides an unbounded platform for the storage and retrieval of information. Snowflake Data Warehouse uses a custom SQL Database Engine with a cloud-specific structure.


Snowflake doesn’t require any equipment and software you need to set up and configure or manage and is therefore suitable for companies that don’t need to dedicate resources to internal server setup maintenance or support.

How does Snowflake CDC work?


The preceding article explains that Change Data Capture tracks change through table streams in Snowflake. For a stream object to capture DML changes, like inserts, updates, and deletes, regularly, it has to know the date and time at which the stream records were last accessed. The answer to this problem is to utilize the term “offset.” An offset is a number that indicates the date in the time since the stream was read during an operation.


The offset is described as a bookmark that is moved or removed. The offset for a stream is placed between two table versions; therefore, using a stream query returns changes triggered by transactions that occurred after the offset but within the time frame for the question.


The table stream creates an outline of the changes that have occurred at the level of the row, and it stores this information at two different points in the time of the object that originated. The data is not stored in streams but uses metadata in conjunction and table-versioning. The offset allows one to consume and query the change records in a transactional way.

What is Change Data Capture (CDC)?


Change Data Capture (CDC) is a great solution for capturing the near actual data movement within Databases. CDC is the term used to describe the accumulation of patterns for design in software that are utilized to monitor and identify changes to data within the Database.


It triggers data-associated events, leading to the specific process to be carried out in any change data capture. Every company requires real-time access to data streams to ensure effective Data Analytics. CDC provides close-to-real-time data movements by processing data immediately after new events in the Database occur.


Events are recorded and streamed live with CDC and help attain reliable, low-latency, and large-scale data replication in high-speed data environments. It can eliminate the need for large-scale data loading through the implementation of incremental loading of data.


In this way, Data Warehouses or Databases remain operational to perform specific actions when the Change Data Capture event occurs. In addition, companies can transmit updated data for BI (Business intelligence) software and team members in nearly time through CDC to keep their data up to date.

Snowflake: Key Features


A few of Snowflake’s most well-known and admired characteristics are described and outlined below.


Standard and extended SQL support: Despite sharing a distinct architecture and cloud-native, Snowflake can support most SQL Data Definition Language (DDL) and Data Manipulation Language (DML) operations. It helps the most common SQL statements, such as INSERT UPDATE, DELETE, and additionally, aggregate functions such as transactions, stored procedures, and DML in loading and unloading data. The teams’ expertise using SQL databases could be ported to Snowflake, lowering the entry barrier.


Security Governance, Data Security: Snowflake has various security and governance guidelines to safeguard and secure information. Users can choose the geographic place where the data is stored to ensure conformity with standards like GDPR. Snowflake also offers support for different authentication mechanisms, including:



  •     Multi-factor authentication (MFA)


  •     Federated authentication/single sign-on (SSO)


  •     OAuth


  •     and many more


In Snowflake, every interaction between the clients and server is protected by Transport Layer Security (TLS). A finely-tuned control of data is also available in Snowflake through object-level access control to ensure that users only get access to data they require and nothing more.


Ease of Connectivity/Availability of Tools: Snowflake has a web-based Graphical User Interface (GUI) for managing accounts, monitoring resources, and querying data. Additionally, it comes with a CLI client, dubbed Snow SQL, that can be used to send commands to Snowflake using a programming or scripted style. A wide array of drivers and connectors for client devices allow connectivity to transfer and receive information from other tools.


Failover and Replication of Databases:  Databases within Snowflake can be synced, replicated, or duplicated over several Snowflake accounts across different regions. Databases can be configured to failover to particular Snowflake accounts to provide business continuity and increase disaster recovery.

Why Use Streams in Snowflakes?


The stream of Snowflake, or table stream, is an object that tracks DML changes to an object source. It uses the metadata associated with the changes to allow actions to be taken regarding the modified information. A stream could provide a small number of changes using the offset derived from its current location to the latest edition of the table. If a stream is queryable, it will provide the historical data, in the shape and names of the original object, along with other columns that give more details about the kind of changes.


As part of Snowflake, streams assist in capturing data changes within the source table and the source table itself. Stream creation in Snowflake is inexpensive because data is not stored in the stream objects.

Bottom Line


To sum up, Snowflake Change Data Capture (CDC) is a revolutionary technology that allows data integration in real-time and analysis. With its benefits, features, and real-world application scenarios, Snowflake CDC offers businesses instant access to information, simpler processes, better quality data, and scalability.


With the help of Snowflake CDC, organizations can make decisions based on data, improve operational reporting, and create business intelligence. Use Snowflake CDC to propel your business to more effectiveness and success based on data.


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