GigaSpaces recently released a unified analytics service as part of its in-memory computing platform.
The service, AnalyticsXtreme, accelerates access to data lakes and data warehouses to enable faster and smarter analytics. The new service is designed to simplify the development of analytics applications and enables them to leverage both streaming (or real-time data) and historic data.
A data lake is a storage repository that holds a lot of raw data in its native format. Data lakes are built for performing batch analytics on primarily historic data. By bringing in support for data lakes, GigaSpaces said it creates more options for its customers. It also claims to have accelerated access to data lakes by 100 times.
The new AnalyticsXtreme service helps to enable both interactive queries and machine learning (ML) models to be able to run simultaneously on both real-time streaming data and historical data stored in data lakes.
GigaSpaces added support for Hadoop, Amazon S3, and Azure Blob Storage. And it added support for data warehouses such as Snowflake. Integration with these external sources can be spun up automatically without making changes to data structures or changes to logic in the ML applications. This, the company says, reduces the complexities of big data architectures.
The service also has support for a number of data platforms and provides a single view of this data. It can access data across real-time and historic platforms including SQL, Spark dataset/dataframe, and business intelligence tools like Tableau and Looker.
Karen Krivaa, VP of Marketing, GigaSpaces
It used to be quite complicated for customers to develop these types of applications. We’ve really unified it into a single interface and we’ve actually been able to accelerate the access to the historical data, which is known to be too slow for real-time [analytics].
