Batch extract-transform-load (ETL) processes face strain for a number of reasons. Increases in data volume can render a once-performant ETL process unstable or introduce unacceptable lag, increasing batch frequency (e.g., from daily to hourly) can introduce overhead on the underlying database or application, and schema evolution can lead to costly maintenance and custom coding needs over time.
Equalum leverages the scalability of open source big data frameworks like Spark and Kafka to dramatically improve the performance of existing batch ETL processes – enabling organizations to increase data volumes while improving performance and minimizing system impact.
Speed and Scalability
Combines unique scalability capabilities with the power of Spark and Kafka to provide extremely fast data ingestion between any number of sources and targets.
Eliminates complex ETL programming and scripting with a zero-coding approach and large number of predefined functions for transformations and manipulations.
Provides the best-in-class security, monitoring, fault tolerance, and availability.
Plug-and-play integration with all major databases and systems; breakthrough use of CDC creates minimal system strain.
A Fortune 100 manufacturing enterprise saw a 15x performance improvement in batch ETL processes – and a 2/3 reduction in development time – after migrating from a legacy commercial ETL solution to Equalum.
EXECUTIVE SUMMARYOlder data ingestion technology originally built for on-premises batch processing cannot...
Coverage Initiation: Equalum aims to equalize the odds for real-time data integration...