MemSQL Helps Enterprises Compare Real-Time Data to Historical Trends
New features enable easy exploration at real-time speeds on large data sets with fast ingestion and analytical query capabilities.
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MemSQL, a real-time analytics solutions provider, has released MemSQL 2.1, which includes new features and enhancements to enable customers to access, explore, and increase the value of data, regardless of size or file format. As organizations amass increasing amounts of data, maximizing the value of each data type is key. To meet these demands, MemSQL updated its analytics platform to enable customers to unleash the full potential of Big Data and receive real-time results on analytical queries across both real-time and historical data sets.
"For years, organizations have been bogged down loading data in batches sacrificing real-time analytical capabilities. With MemSQL, it’s now possible to consume real-time events and simultaneously compare that against historical operational data sets. Moreover, the ability to load data stored as flat files on disk into memory has created an ideal experimentation platform by increasing query performance," said Eric Frenkiel, CEO.
"From ingestion to exploration, MemSQL makes deployment simple, increasing reliability and, most important, delivering increased value from the data to customers."
As the speed at which data is analyzed increases, so does the quality of insight. To increase competitive advantage, organizations must access, explore, and attain the greatest value possible from their data. With a real-time database, users can access and explore data instantly, greatly appreciating the value of the data itself. MemSQL allows companies to manage the velocity of big data transactions without sacrificing the ability to interact with and analyze all data in the system.
New and enhanced features and functionalities include:
- Multithreaded load of CSV files: CSV is one of the most commonly used file formats for exchanging data between applications. Users can setup ondemand or scheduled uploads of CSV files to MemSQL, allowing them to harness the power of a inmemory distributed database for subsecond query response times on hundreds of millions of rows of data.
- Distributed joins: The ability to perform distributed joins on tables with primary key to foreign key relationships is critical to accelerating many types of analyses. MemSQL 2.1 unlocks this functionality to accelerate analytics use cases by allowing users to choose the column(s) on which they shard, making MemSQL smart and flexible on schema design.
- Linux-based package manager: With MemSQL 2.1, users have the ability to download, install, and upgrade MemSQL via a package manager on major Linux distributions. Users can now easily install, upgrade, and manage MemSQL using common Linux workflows
- Node management: The ability to manage nodes efficiently is key in database cluster management. With MemSQL 2.1, failed nodes are more easily tracked in the MemSQL Watch dashboard and can be reattached to the cluster without reprovisioning. The entire experience of backing up, restoring, shutting down, and managing failures is optimized for DBAs. MemSQL built these features based on extensive collaboration with DBAs to ensure its solution is the easiest platform to manage.
MemSQL’s real-time analytics platform is built on the world's fastest, most scalable in-memory database, capable of simultaneously handling real-time transactions and analytic workloads. The company’s distributed version of its database, which combines in-memory speed and a massively scalable relational DBMS, enables customers to take full advantage of real-time and historical data to make informed decisions, better engage customers, and identify competitive advantages.
For more information about MemSQL, visit www.memsql.com.