GigaSpaces' XAP 9.0 Supports New Ways to Tackle Big Data


Archived Published: 29 May 2012 ID: G00235683

Analyst(s):

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Summary

GigaSpaces Technologies' XAP 9.0 offers a versatile way to analyze huge datasets in real time. But the company must convince the market of the value of its in-memory cloud application platform in big-data scenarios.

News Analysis

Event

On 21 May 2012, GigaSpaces Technologies, an application infrastructure vendor based in Israel, announced the launch of eXtreme Application Platform (XAP) release 9.0. XAP 9.0 adds support for high-performance real-time analytics for applications dealing with very large datasets.

Analysis

GigaSpaces' background is in in-memory data grid (IMDG) technology, but it has expanded its offering to cover cloud-enabled application platforms (CEAPs), and has recently moved into cloud management with the Cloudify product. GigaSpaces’ 300 or so clients use XAP to support business-critical applications in verticals such as financial services, telecom, online entertainment, travel and cloud services. XAP 9.0 is the latest rendition of GigaSpaces' well established IMDG/CEAP product line.

Traditionally XAP has been used mainly to implement transaction-oriented applications that require low-latency access to in-memory data. But XAP, like other IMDGs, has also been used extensively for "big data" analytics scenarios — typically complementing data stores like HBase (Apache Hadoop), Cassandra and MongoDB — to collect, filter, aggregate and process low-latency/high-volume data streams (such as financial data, information feeds, social networks and sensor data) that are then stored in database management systems (DBMSs) for further, batch-type processing.

IMDGs are suited to big data because:

  • They can support hundreds of thousands of in-memory data updates per second, so they can deal with the “velocity” aspect of big data.

  • As NoSQL data stores, they can also support the “variability” in big data.

  • They can cluster hundreds of nodes, to manage the “volume” aspect of big data. However, IMDGs are rarely used to permanently hold very large datasets, for which users still prefer "on-disk" DBMSs for cost, data persistency and other reasons.

GigaSpaces has introduced specific new big-data-oriented features in XAP 9.0, such as data compression, distributed processing of ordered events and integration with big-data-oriented DBMSs, as well as a new HTML5-based Web monitoring and management console and performance and availability improvements. These big-data-oriented features support existing and well-known blueprints, and also make it possible for users to combine transactional and analytical/big-data capabilities in the same applications. They do this without requiring high-latency (typically batch) data duplication to optimally support online transaction processing and online analytical processing applications, as in traditional architectures.

Challenges for GigaSpaces include:

  • Proving the new use case for XAP with a significant number of public references

  • Educating the market in the use of IMDGs in big-data scenarios

  • Positioning its technology against event processing platforms (which are often used in the same type of applications)

  • Aligning its strategy with those of analytical tools vendors

  • Winning support from system integrators and independent software vendors

Recommendations

  • GigaSpaces users: Consider XAP 9.0's new big-data capabilities as extensions of the product you are already familiar with, primarily targeting analytical applications.

  • Users looking for a versatile IMDG: Consider XAP as a platform that could cover a wide range of use cases, including: distributed caching, in-memory systems of record, distributed in-memory application platforms and big-data analytics.

  • Users working on big-data projects: Consider XAP 9.0 (or other IMDGs) as a low-latency, highly scalable in-memory front end to “on-disk” DBMSs to support real-time data validation, filtering, aggregation and processing.

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