PaaS offering for continuous, real-time data movement

19th November 2018
Source: Striim
Posted By : Alex Lynn
PaaS offering for continuous, real-time data movement

The fully managed database service for PostgreSQL and MySQL databases from Google in the cloud is the Google Cloud SQL. However, a cloud database is only as good as its data. Striim ensures Google Cloud SQL is continuously fed with real-time, relevant data from both on-premises and cloud systems, while maintaining transactional consistency.

Alok Pareek, Founder and EVP of Products for Striim, stated: "With Striim, our customers can set up continuous real-time data flows that deliver data in the right format into PostgreSQL and MySQL running on Google Cloud. Striim moves data continuously with Exactly Once processing (E1P) semantics and in a transactionally consistent manner.

“These are key requirements when integrating data from mission-critical applications. By rapidly setting up automated, real-time data pipelines, Cloud SQL customers can replicate data into their MySQL and PostgreSQL database services for real-time business applications, as well as for operational data stores and data marts in their analytical solutions."

Striim ingests data from a broad range of enterprise databases (including Oracle, SQL Server, HPE NonStop, MySQL, PostgreSQL, Amazon RDS for Oracle, Amazon RDS for MySQL) via log-based Change Data Capture (CDC), as well as from log files, messaging systems, sensors, and Hadoop solutions. While the data is in-motion, Striim performs in-line transformations before delivering to Google Cloud SQL with sub-second latency, providing full context for any downstream operations such as reporting, analytical, and transactional processing.

For Google Cloud SQL customers, Striim offers several features and benefits that can maximise integration speed and reliability:

  • Uses low-impact change data capture (CDC) to minimise impact on source databases.
  • Offers a secure, reliable, and scalable service for continuous real-time data collection, preparation, and delivery to Google Cloud SQL.
  • Provides in-flight data filtering, transformation, aggregation, masking, and enrichment.
  • Reduces on-premises ETL workloads, as well as data latency.
  • Enables synchronisation of partial data sets for reporting use cases.

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