GridGain Unified Real-Time Data Platform Version 8.9 Addresses Today’s More Complex Real-Time Data Processing and Analytics Use Cases
Latest version strengthens data integration and data hub capabilities, enables more comprehensive real-time analysis and decisioning
FOSTER CITY, Calif., Oct. 12, 2023 (GLOBE NEWSWIRE) -- GridGain®, provider of a leading unified real-time data platform, today announced the release of GridGain Platform v8.9, strengthening its data integration and data hub capabilities. This latest version helps enterprises make their increasingly disparate, diverse, and distributed data more accessible for real-time processing and analytics, all at ultra-low latencies.
GridGain Platform v8.9 includes several new or enhanced integrations, including deeper support for Apache Parquet, Apache Iceberg, CSV, and JSON. These integrations enable enterprises to deliver highly performant real-time analysis across complex data workloads by making enterprise data in data lakes and semi-structured data in non-relational/NoSQL databases more easily accessible for processing. The ability to handle these complex workloads in a single platform helps enterprises simplify and optimize their data architectures to drive the extreme speed, massive scale, and high availability they require.
“GridGain has provided leading enterprises with the capabilities of a unified real-time data platform for years,” said Lalit Ahuja, Chief Product and Customer Officer at GridGain. “With each release of our platform, we strengthen our ability to exceed the high expectations of our enterprise customers in supporting the increasing complexity of modern data use cases such as traditional and generative AI, fraud detection, smart decisioning, operational analytics, and customer 360°. With version 8.9, GridGain continues to raise the bar for durable, ultra-fast data processing and analytics at scale.”
New in GridGain Platform v8.9
- Expanded ecosystem: Includes deeper integrations for Apache Parquet, Apache Iceberg, CSV and JSON to easily support more complex datastores, including enterprise data lakes and NoSQL/semi-structured document databases.
- Enhanced data management: Enables more storage- and read-efficient management of massive data tables, with support for high performance, ACID-compliant queries and diverse document data types, helping developers to build new and more complex applications faster.
- Support for more data types: Takes its distributed, colocated, memory-centric computing capabilities to NoSQL and data lake technologies, enabling faster analytics.
These new and enhanced integrations – together with GridGain’s unique ability to seamlessly combine streaming data in-motion and historical data at-rest with compute functionality – enable enterprises to handle complex analytical and transactional data workloads at unmatched speed and scale.
GridGain Platform v8.9 is available now. Visit the GridGain website to download.
- GridGain blog: What’s New in GridGain 8.9
- GridGain eBook: The Ultimate Guide to Unified Real-Time Data Platforms
- Gartner report: Gartner Market Guide for Event Stream Processing
Connect with GridGain
GridGain is a unified real-time data platform. It enables a simplified and optimized data architecture for enterprises that require extreme speed, massive scale, and high availability from their data ecosystem. GridGain’s distributed memory-first architecture and colocated compute deliver data processing and analytics at millisecond latencies, with configurable disk-based persistence for added durability. Horizontally scalable clusters can be deployed both on-premises and natively in public or private clouds, empowering companies to handle even the most demanding workloads in multi, hybrid, and inter-cloud environments. GridGain is trusted by companies like Citi, Barclays, American Airlines, AutoZone, and UPS to accelerate their existing applications, speed operational analytics and fraud detection, train machine learning models for AI, and provide fast-access data hubs. To learn more, please visit www.gridgain.com.
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