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  • Presentation from LA Apache Spark Users Group Meetup

    VP of Product and Strategy discusses Hybrid Transactional/Analytical Processing with Spark & In-Memory Data Fabrics. Download full presentation here.
  • 451 Group Webinar

    Watch our Oct 5 Webinar with 451 Group Analyst Jason Stamper on Fast Data Analytics: From Insight to Action Using In-Memory Computing. Watch now!
  • 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.  Click to listen.

Why InsightEdge?

Simplified Data Science and Machine Learning

Run machine learning models directly against your transactional data while creating and ingesting data sets without ETL complexity.

In-Grid Processing and Filtering

Speed up Spark actions by as much as 30x with our fast data grid execution. This will eliminate compute-bound bottlenecks and JVM performance issues.

Shared Spark DataFrames and RDDs

You can share Spark DataFrames and RDDs across many jobs enabling efficient pipelining.

GeoSpatial Processing

Implement powerful location-based analytics by ingesting and querying GeoSpatial data as native RDDs and DataFrames.

High Availability

Eliminate streaming downtime by having a redundant copy of Spark executor data readily available in case of a crash.

Fast Off-Heap Storage

Utilize an enterprise-grade in-memory storage as an off-heap solution for low latency streaming workloads.

InsightEdge Architecture



We provide an implementation of all Spark API’s (Spark Core, SQL,Streaming, MLLib, and GraphX) on top of an in-memory data grid. This data grid features high-performance, extreme transaction processing and leverages RAM and SSD/Flash storage for low latency workloads. InsightEdge tiers the storage and processing of Spark workloads between Spark workers and underlying data grid containers.

check out our demo to see insightedge in action

Use Cases



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