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.
“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],” said Karen Krivaa, vice president of marketing at GigaSpaces.
A data lake is a storage repository that holds a lot of raw data in its native format. According to Krivaa, data lakes are built for performing batch analytics on primarily historic data. By bringing in support for data lakes, GigaSpaces 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. What’s unique, says Krivaa, is that it gives a user access to an external storage layer without requiring a separate data load procedure or data duplication — all in a unified service.
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.
The terms big data, machine learning, and artificial intelligence (AI) are all becoming fun buzzwords that many businesses are “adding” to their platforms in some way or another. So how does GigaSpaces separate itself from the noise of its competition?
Krivaa says that it stands out with its in-memory platform, InsightEdge, and now its AnalyticsXtreme service simply by offering more in one place. “We’ve combined a lot of different functionalities into a unified platform, whereas competitors can do some of them but not all of them,” she said. “We could really cover much more use cases than our competitors.”
What she is referring to is that GigaSpaces can ingest data from any source — which includes structured, unstructured, and semi-structured data. When the platform runs analytics on that data, it can store that data so it doesn’t move out of the platform. Krivaa noted that moving data makes it more complicated and costly to manage and slows analytics.
GigaSpaces “seamlessly manages the data on the [storage] tier that the customer decides,” she said. As an example, Krivaa said that the “most important data should be stored in the RAM. The next important data would be saved in the persistent memory, which is almost as fast as RAM, but less expensive. Less important data can be saved in SSD.” She added that older data is saved in a data lake, which is the least expensive way but also the slowest.
This added choice and unified approach lends itself to simpler operations and data governance by applying an automatic lifecycle policy that handles data movement.
All of which, as Krivaa noted, enables use cases where companies need a wide breadth of data and need options for storage, but still need to quickly complete analytics queries.