Run machine learning models directly against your transactional data while creating and ingesting data sets without ETL complexity.
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.
You can share Spark DataFrames and RDDs across many jobs enabling efficient pipelining.
Implement powerful location-based analytics by ingesting and querying GeoSpatial data as native RDDs and DataFrames.
Eliminate streaming downtime by having a redundant copy of Spark executor data readily available in case of a crash.
Utilize an enterprise-grade in-memory storage as an off-heap solution for low latency streaming workloads.
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.
Stream live traffic, flight and passenger data to optimize airport, crew, and passenger scheduling
Ingest and query supply chain field data to analyze in real-time for anomaly detection.
Correlate web, mobile, and call center data in real-time to improve customer experience through personalization.
Run and optimize machine learning workloads against field data to find optimal supply routes.
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