MemSQL, the San Francisco, CA based Big Data analytics outfit, has today rolled out a new data loading tool that increases ‘data ingest from popular data stores like Amazon Web Services S3 and the Hadoop Distributed File System (HDFS)’.
“The MemSQL Loader is another innovation of simplifying MemSQL implementations with production data workflows,” explains Chief Technology Officer and co-founder of MemSQL, Nikita Shamgunov.
“After working with customers during their MemSQL deployments, we found a simple way to eliminate steps in data pipelines, saving them time and reducing complexity,” he further added. “By streaming directly from popular data stores like Amazon S3 and HDFS, we offer customers an easy way to get started, and an efficient way to integrate the real-time transactions and analytics of MemSQL into existing environments.”
An announce made earlier today reveals, that in contrast to typical data loading which often requires multiple steps, the MemSQL Loader enables direct streaming from the originating datastore in a single transfer. Available as open source, the MemSQL Loader enables ‘multiple parallel input streams’, thus gaining time through lack of repetitive operations and increasing performance.
A Y Combinator company, MemSQL has been assisting companies in merging real-time and historical Big Data analytics with its distributed in-memory database and has garnered funding from Accel Partners, Khosla Ventures, First Round Capital, and Data Collective.
For further reading into the nuances of MemSQL Loader, the technical blog post can be found here.
(Image credit: MemSQL)
