GigaSpaces Technologies, a pioneer of data scalability and next-generation application platforms for mission-critical applications, announces the launch of XAP9.0. At the core of the latest release of the GigaSpaces platform is its ability to quickly launch a high-performance real-time analytics system for applications processing massive data sets, quickly and easily.
“In today’s business world there is an increased – even dire – need for real-time insights, especially for large-scale location-aware mobile, social, and financial/risk analysis applications. This is clearly evidenced by the launch real-time analytics platforms by Facebook, Google, and Twitter this past year,” says Adi Paz, GigaSpaces EVP of Marketing & Business Development. “What is common to all real-time analytics systems is the need for streaming data processing, alongside batch processing, such as Hadoop provides. However, building a system for Big Data apps is complicated – it must maintain reliability, scalability, and performance even at immensely large scales of data. XAP 9.0 was specifically designed to resolve these challenges.”
Major social networking platforms like Facebook and Twitter have developed their own architectures for handling the need for real-time analytics on huge amounts of data. However, not every company has the need or resources to build their own Twitter-like solution. This is where XAP 9.0 comes in. With the GigaSpaces real-time analytics solution for Big Data, there is no need to reinvent the wheel – GigaSpaces has taken the same Twitter/Facebook-like blueprint, and made it simple enough for developers to implement a Big Data analytics system in a matter of days, or even less. GigaSpaces XAP has a long record of providing elastic real-time processing, and in version 9.0 these capabilities have been honed to specifically meet today’s challenges to real-time analytics for Big Data.
What are the issues that make real-time analytics for Big Data such a challenge? These are the “3 Vs” of Big Data:
- Volume: The system must be able to deal with extremely large amounts of data.
- Variety: The data is diverse and comes from many sources. The system must be able to accommodate new and different schemas and languages without any need to change the system or downtime to set it up.
- Velocity: The speed of data processing must keep up with the speed of incoming data, while maintaining real-time latency levels.
GigaSpaces XAP 9.0 includes the exact feature set to meet these challenges:
- Real-time, scalable streaming data processing:
- Patent-pending parallel processing (FIFO Groups).
- Fine-grained data compression.
- Reduced memory footprint, with no compromise on query capabilities.
- Local view, which enables keeping local data continuously updated
- Ability of all the above to now work across multiple sites, dynamically, through XAP’s WAN Dynamic Topology API.
- Integration with Big Data back-end databases, such as Hbase, Cassandra, and MongoDB to ensure consistent flow of data without affecting performance.
- Big Data analytics on the cloud.
- Built-in cloud-enablement features provide:
- Consistent automation of deployment, scaling, and failover of the entire Big Data application stack on any private or public cloud.
- Built-in support for managing Big Data services, such as Cassandra, MongoDB, Solr, and more.
- Support for bare metal environments for extreme I/O.
- Elastic scaling to reduce the total cost of ownership of running Big Data apps.
“What sets apart the GigaSpaces Big Data solution for real-time analytics is the combination of simplicity and reliable high performance,” says Paz. “Real-time analytics is actually a very complex area, and the ability to get a ready-made, easy-to-implement solution cannot be overestimated. At the same time, simplicity is not enough for business that require accuracy, speed and reliability as basic requirements. XAP 9.0 provides all of the above.”