CDC to Snowflake
Let’s take a moment to discuss why Change Data Capture or CDC to Snowflake is quickly becoming the preferred method of loading real-time data from transactional databases to Snowflake, without impacting source systems.
Snowflake is changing expectations for speed and flexibility of a data warehouse. Snowflake provides a cloud-based data warehouse that enables organizations to store and analyze data using public cloud-based hardware and software on AWS and Microsoft Azure.
However these benefits of speed and flexibility can be quickly throttled by legacy approaches to moving data into Snowflake. For most companies, their most valuable data – transactional and operational data – is stored on-prem in traditional relational databases or legacy data warehouses. While old-school migrations or batch ETL uploads achieve the objective of moving the data to a target such as Snowflake, these out-of-date, high-latency approaches cannot support the continuous data pipelines and real-time operational decision-making that Snowflake is built for.
Enter CDC to Snowflake, made possible by Striim. The Striim platform enables Snowflake users to quickly and easily leverage low-impact, real-time change data capture, or CDC to Snowflake, moving and processing only the changed data from their existing databases. Moving change data continuously, as new database transactions or events occur, makes it possible for Snowflake users to maintain the real-time data pipelines necessary to feed Snowflake’s fast and flexible storage and analytics solutions.
For the initial load of data to Snowflake, Striim enables zero-downtime, zero-data-loss migration from databases and data warehouses to Snowflake. As an enterprise-grade solution, Striim also features built-in, real-time monitoring to validate that the database transactions have loaded successfully to Snowflake, minimizing risk by ensuring data consistency.
Striim can not only load data, and continuously feed data, to Snowflake. Striim is unique in its ability to provide in-flight processing such as filtering, transformations into the desired schema, and data masking. In-memory stream processing minimizes ETL workloads, improves performance, reduces complexity and facilitates compliance.
Striim offers low-impact, log-based CDC to Snowflake from the following data sources: Oracle Microsoft SQL Server, MySQL, PostgreSQL, MongoDB, HPE NonStop SQL/MX, HPE NonStop SQL/MP, HPE NonStop Enscribe, and MariaDB. New sources are being added on a regular basis. All of these sources can be accessed via Striim’s easy-to-use CDC Wizards and drag-and-drop UI, speeding delivery of CDC to Snowflake solutions.
For more information on Striim’s CDC to Snowflake offering, please visit our Snowflake solutions page at: striim2020.local.com/partners/real-time-data-to-snowflake/
If you’d like a brief demo of CDC to Snowflake, please schedule a demo.