5 Trends Impacting the Future of Machine Data Intelligence IT organizations looking to apply real-time analytics against machine data have historically needed to make a choice between using an open source NoSQL database or an instance of Hadoop that is not especially fast or licensing a proprietary platform that could get prohibitively expensive. Aiming to […]
5 Trends Impacting the Future of Machine Data Intelligence
IT organizations looking to apply real-time analytics against machine data have historically needed to make a choice between using an open source NoSQL database or an instance of Hadoop that is not especially fast or licensing a proprietary platform that could get prohibitively expensive.
Aiming to create a middle ground between those two extremes, Crate.io today announced the general availability of an open source database, dubbed CrateDB, that is based on a distributed SQL query engine optimized for processing machine data in real time.
Crate.io CEO Christian Lutz says the CrateDB database enables IT organizations to capture machine data in a way that can be queried using standard SQL. That approach creates a significantly less expensive approach for enabling the development of real-time analytics applications based on machine data being generated throughout the enterprise.
“Most of the time, organizations are using us to replace Splunk,” says Lutz.
Lutz says CrateDB is unique because it combines a distributed SQL query engine based on a columnar database and embedded search technology to support a broad range of data types and use cases, including machine learning and predictive analytics, on time series, full text, JSON and geospatial applications.
In addition, Lutz notes that CrateDB can be deployed in a container environment, which Lutz says makes it easier to scale CrateDB using container orchestration platforms based on Docker, Mesos or Kubernetes.
One of the first primary use cases for Big Data inside any enterprise is analyzing machine data created by IT infrastructure to discover anomalies and root causes of performance issues. But with the rise of Internet of Things (IoT) applications, it’s clear that the amount of machine data most IT organizations will soon be asked to analyze will be increasing exponentially. Naturally, figuring out how best to go about collecting and analyzing that data in real time without breaking the IT budget is about to become substantially more challenging in the months and years ahead.
Michael Vizard is a seasoned IT journalist, with nearly 30 years of experience writing and editing about enterprise IT issues. He is a contributor to publications including Programmableweb, IT Business Edge, CIOinsight and UBM Tech. He formerly was editorial director for Ziff-Davis Enterprise, where he launched the company’s custom content division, and has also served as editor in chief for CRN and InfoWorld. He also has held editorial positions at PC Week, Computerworld and Digital Review.
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