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.


check out our demo to see insightedge in action

Use Cases


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