Crate.io announced the availability of CrateDB 1.0, an open source SQL database that allows real-time analytics for machine data applications. CrateDB makes machine data applications that were formerly only possible using NoSQL solutions available to mainstream SQL developers.
According to Jason Stamper, Analyst, Data Platforms and Analytics at 451 Research, “The growth of machine data and the opportunities that businesses have to capitalise on it are outstripping the ability of their data management infrastructure to act on it. CrateDB’s power lies in its ability to enable users to collect and analyse vast amounts of data in real-time, using SQL commands they already know.”
Downloaded over one million times since its introduction last two years, CrateDB combines the familiarity of SQL with the versatility of search and the ease of scalability of containers. It offers an alternative to current analytic datastores including Splunk.
CrateDB’s unique capabilities are enabled by the following innovations:
Distributed SQL query engine for faster JOINs, aggregations, and ad-hoc queries
Columnar field caches and a fully distributed query planner enable CrateDB to perform complex queries in real -ime and overcome many of the performance and flexibility limitations of first-generation distributed SQL databases.
SQL with integrated search for data and query versatility
CrateDB is a unique integration of SQL and search technology, which allows a wide range of analytics, including machine learning and predictive analytics, on time series, full text, JSON, geospatial, and other structured and unstructured data without a need to use different database engines to do so.
Container architecture and automatic data sharding for simple scaling
Database scalability is important for managing variations in machine data volume, but this is usually difficult to do. CrateDB can operate as a cluster of containers, which allows it to be scaled easily with Docker, Kubernetes, or Mesos container platforms. CrateDB also automatically shards and redistributes data across the cluster as it changes size to optimise performance and high availability.
Christian Lutz, CEO or Create.io said, “When we founded Crate.io, we set out to reinvent SQL for the machine data era. Today, 75 percent of our customers use CrateDB to manage machine and IoT data because of its superior ease of use, performance, and versatility. The general availability of the product and our expansion to San Francisco mark a new phase in our growth, and we look forward to driving further innovation of the platform both internally and by extension through the open source community.”
Customer Testimonials for CrateDB
Juergen Sutterlueti, Head of Energy Segment, Gantner Instruments mentioned, “The mission-criticality of our industrial sensor and data acquisition devices cannot be overstated. Our well known key customers in the automotive, energy, aerospace and civil engineering segment rely on our ability to take synchronised and decentral measurements from hundreds of thousands of sensors, feed them into a database and extract that data for instant visibility of power, temperature, pressure, speed and torque. Based on the real-time aggregated meta-data they make their decisions. CrateDB is the only database that gives us the speed, scalability and ease of use that our teams, customers and applications require.”
Sekhar Sarukkai, Co-Founder, Chief Scientist and Vice President of Engineering, Skyhigh Networks also said, “More than 40 percent of the Fortune 500 customers depend on Skyhigh to help address their cloud security needs. CrateDB is an important part of our data stack giving us the performance and horizontal scalability to meet our rapidly growing business needs.”
Paul Hofmann, Chief Technology Officer, Space-Time Insight described, “At Space-Time Insight we create Industrial Internet of Things solutions to help our customers extend asset life, predict maintenance needs, and optimize logistics and operations. We make extensive use of machine learning and streaming analytics, and CrateDB is particularly well-suited for the geospatial and temporal data we work with, including support for distributed joins. It allows us to write and query sensor data at more than 200,000 rows per second, and query terabytes of data.”