In-Memory Computing Platform

Scaling your business with extreme transactions and fast insights at the point of action


XAP 12 has Landed!
Check out the latest release of the In-Memory Computing Platform from GigaSpaces. XAP 12 is the first step in delivering the GigaSpaces Open In-Memory Compute Platform. 
Presentation from LA Apache Spark Users Group Meetup 
VP of Product and Strategy took the stage to discuss Hybrid Transactional/Analytical Processing with Spark & In-Memory Data Fabrics. Download full presentation here
IoT Time Podcast with GigaSpaces
GigaSpaces talks data storage, security and using your power for good instead of evil with IoT Evolution's Ken Briodagh.  Listen now
Extreme Transaction Processing
XAP is an In-Memory Data Grid that scales your digital business performance by orders of magnitude. Our open platform provides microsecond-scale transaction processing, data scalability and powerful event-driven workflows.

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Fast Data Insights
InsightEdge connects insight to action at the speed of business. Our high performance, enterprise-ready Spark distribution is designed for fast data workloads and low latency real-time analytics.

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Featured Customers


In-Memory Computing Product Line




Why XAP?

Linear Scalability
Horizontally partitioned data grid for scalable and ultra-fast data access, ensuring immediate consistency across a multitude of data models.
Optimized for Fast Data Analytics
Provides a unified in-memory store for heterogeneous NoSQL data sources using our rich query language, map/reduce capabilities and efficient aggregations.
Event-Driven Processing
Supports advanced parallel processing patterns as well as full messaging semantics (p2p or pub-sub) while ensuring consistency through transactions.
Mission Critical High Availability
Automatic self-healing and failure recovery with fault tolerance and resiliency against network partitions eliminating any single point of failure.
Multi-Data Center Support
Synchronize your data across multiple data centers using our optimized replication protocol to achieve DR readiness, maintain data locality and geolocation affinity.
Tiered Data Store
Provides a hybrid storage model for maintaining data on multiple tiers in addition to RAM (e.g. SSD) to reduce costs and expand grid capacity.

Why InsightEdge?

Low Latency, Fast Processing
Develop, deploy and test multiple Spark jobs without having to reload data from HDFS.
Shared RDDs Across Spark Jobs
Share RDDs and DataFrames across Spark jobs and clusters for workloads that require state sharing.
Hybrid Fast Data Workloads
Easily load data from your transactional database and query it through RDD/DataFrame API in Spark.
Storage-Side Query Filtering
Avoid CPU, Disk I/O and JVM bottlenecks in complex workloads by deferring execution to the underlying storage.
High Availability
Eliminate streaming downtime by having a redundant copy of Spark executor data readily available in case of a crash.
Off-Heap Storage
Utilize an enterprise-grade in-memory storage as an off-heap solution for low latency streaming workloads.



Interested in learning more? Get in touch with us.

In-Memory Computing Platform