GridGain Systems, provider of enterprise-grade In-Memory Data Fabric solutions based on Apache® Ignite™, announced the availability of GridGain Professional Edition 1.6, an in-memory computing platform enabling high-performance transactions that run 1,000x faster than disk-based approaches.
The latest version adds native support for Apache® Cassandra™, a new ODBC driver, deadlock-free transactions, and the availability of a new hosted web management console. These features enable easy integration with data analytics tools, provide enhanced performance, offer access to a new web-based configuration and management tool for GridGain and Apache Ignite deployments, and add significant database performance improvement for Cassandra users.
With the addition of native support for Apache Cassandra, GridGain extends its native support for accelerating and scaling most popular SQL and NoSQL databases, as well as Apache® Hadoop®. Apache Cassandra is optimized to run simple, pre-defined queries on data stored on disk. It does not, however, include an in-memory computing option and does not support transactions. When combined with GridGain, Apache Cassandra users can:
- See a 1,000x query speed improvement because data is uploaded from disk into RAM
- Leverage ANSI SQL-99 compliance to run ad hoc and structured queries with complete ODBC and JDBC support
- Use full ACID compliant transactions to read and write data to their Cassandra database
- Benefit from built-in support for Apache® Spark™, Apache Hadoop, and streaming applications
Based on Apache Ignite, the GridGain In-Memory Data Fabric enables massive scale for data-intensive applications and a 1,000x improvement in transaction performance versus disk-based approaches without replacing the existing underlying databases. It provides high-speed transactions with ACID guarantees, real-time streaming, and fast analytics in a single, comprehensive data access and processing layer. GridGain powers existing or new applications in a distributed, massively parallel architecture on affordable, industry-standard hardware, which can be easily scaled by adding more nodes to the compute grid. The GridGain In-Memory Data Fabric requires minimal or no modifications to the application or database layers for architectures built on RDBMS, NoSQL or Apache Hadoop databases.
Key Features of This Release
- Easier Integration with Apache Cassandra – GridGain now offers out-of-the-box integration with Apache Cassandra, making it easier for customers to use Cassandra as persistent storage for any version of GridGain or Apache Ignite.
- New ODBC Driver – The new ODBC driver enables customers to connect to GridGain or Apache Ignite from any language platform. Customers can also now analyze data stored in GridGain or Apache Ignite using Tableau or any other standards-based analytics tool.
- New Deadlock Management – The new deadlock-free transactions capability enables deployment across multiple large teams without worrying about deadlocks. This feature includes automatic deadlock detection for optimistic and pessimistic transactions, ensuring uninterrupted performance even in high contention situations.
- New Hosted GridGain Web Console – A new hosted web management console for GridGain and Apache Ignite makes managing the environment easier with features that simplify configuration, SQL querying, management, and monitoring.
The GridGain In-Memory Data Fabric is now the go-to technology for companies needing to process and analyze massive amounts of data for real-time transactional, analytical or hybrid transactional/analytical use cases,” said Abe Kleinfeld, President and CEO of GridGain. “With the latest release, we have made it easier for IT to integrate GridGain into any environment by offering features such as new deadlock management, an ODBC driver and a hosted web console, which enable more users to be more productive. We are especially excited about the new native support for Apache Cassandra which provides Cassandra users with new capabilities such as ad hoc queries and full ACID transactions while achieving in-memory speeds.”
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