Develop, deploy and test multiple Spark jobs without having to reload data from HDFS.
Share RDDs and DataFrames across Spark jobs and clusters for workloads that require state sharing.
Easily load data from your transactional database and query it through RDD/DataFrame API in Spark.
Avoid CPU, Disk I/O and JVM bottlenecks in complex workloads by deferring execution to the underlying storage.
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.
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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