Striim Announces a Fully-Managed Real-Time Enterprise Data Integration Service for Snowflake

By Globe Newswire On Apr 4, 2023

No-code managed service, Striim for Snowflake, automates building and managing real-time data pipelines from enterprise-grade databases to Snowflake

Striim, Inc. announced Striim for Snowflake, a fully managed, streaming SaaS solution that is optimized for the Snowflake Data Cloud. Striim for Snowflake uses change data capture (CDC) technologies to load, integrate, and synchronize data from enterprise-grade databases such as Oracle, Microsoft SQL Server, and PostgreSQL into the Snowflake Data Cloud. Customers can easily build new automated data pipelines to stream transactional data from hundreds and thousands of tables to Snowflake with sub-second end-to-end latency to enable real-time analytics and 24/7 operations.

“In order to make better business decisions and unlock instant insights, enterprises need to unite and transform mission-critical data from disparate data sources into the Snowflake Data Cloud,” said Tarik Dwiek, Head of Technology Alliances at Snowflake. “With Striim’s real-time data integration capabilities, our joint customers benefit by being able to bring that data directly into the Snowflake Data Cloud.”

Most organizations utilize database replication to cloud data warehouses to enable data science and analytics teams to optimize decision-making and business workflows. Legacy replication platforms are complex and costly while cloud-based ETL platforms can introduce significant latency and performance overhead to mission-critical analytics that will not be tolerable at enterprise scale.

Striim for Snowflake builds on Striim’s data integration and streaming capabilities to simplify building and operating data pipelines to bring real-time streaming data to the Snowflake Data Cloud. Using the newly-designed user interface, customers can configure and observe the ongoing and historical health and performance of their data pipelines, reconfigure their data pipelines to add or remove tables on the fly, and easily repair their pipelines in case of any unexpected interruption.

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“We are very excited about launching this managed service that allows our joint customers to set up their data pipelines within a few clicks. The user experience in Striim for Snowflake is simple and intuitive since it automates the entire pipeline configuration and optimization. Snowflake teams can get enterprise data from multiple data sources within a few seconds from critical operational data sources. Fresh data is essential for enterprises to make the right business decisions at the right time,” said Alok Pareek, Co-founder and Executive Vice President of Engineering and Products at Striim. “Our customers are increasingly using the Snowflake Data Cloud for their data analytics needs. We have designed Striim for Snowflake for high performance, lower cost, operational ease, simplicity, and resiliency so that our customers can quickly and easily extract business value from their data. We have automated schema management, snapshot creation, CDC coordination, and failure handling in the data pipelines to deliver a delightful user experience. The combined speed of Striim’s data ingestion and Snowflake’s analytical capabilities gives customers a clear and distinct competitive advantage.”

Striim for Snowflake provides a high level of automation. With a few clicks, customers can set up their data pipelines, and Striim takes care of the rest. Striim uses patented technologies and leverages Snowflake’s high-speed streaming ingest API to optimize pipeline throughput and reduce end-to-end latency. Striim continuously monitors and reports pipeline health and performance. When it detects tables that cannot be synced to Snowflake, it can automatically quarantine the errant tables and keep the rest of the pipeline operational, thus preventing hours of pipeline downtime. Striim for Snowflake natively stores and reports health performance data so customers can quickly analyze and optimize pipeline performance based on real-time, near-term, and historical data.

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