MemSQL’s scalability and in-memory performance make it the frequent relational database of choice for data-rich enterprises. But replicating data from other sources – operational data stores, legacy applications, IoT control systems, and files – into MemSQL is challenging. Homegrown scripts are bug-prone, require custom integration for every file type, and fail under high data volumes. And traditional ETL solutions, in addition to only supporting batch updates, are not optimized to load data into MemSQL efficiently – leading to slow performance and high usage of compute resources.
Equalum offers the most powerful MemSQL integration on the market, with proprietary performance tuning and a load process that decouples loading from processing – enabling re-streams and efficient downstream transformation. Equalum’s solution leverages fully-managed Spark and Kafka, along with best-in-class change data capture (CDC) on the market, to replicate data from any database or application into MemSQL the instant it’s created.
Proprietary, optimized load process into MemSQL, ensuring high performance and data reliability for both column store and row store.
Seamless integration with MemSQL; breakthrough use of CDC and decoupling of loading from processing creates minimal system strain.
Leveraging a Spark and Kafka foundation; Equalum supports streaming between any number of sources and targets in real-time.
A Fortune 500 oil and natural gas exploration company struggled with previous data ingestion processes with 10 minutes latency at best, creating big operational and business risks. Developing additional data ingestion processes was very time consuming with hundreds being added every quarter.
Equalum was brought in to stream drilling rigs data, approximately 20,000 events/second with latency of 1 second from Oracle and other sources to a data analytics environment – powering real-time analytics for drill site optimization.
Software Engineer Specialist, Oil & Gas Company