CrateDB 2.0 Adds Clustering Upgrades and SQL Enhancements to Its Database Solution for IoT and Machine Data

On May 16, Crate.io announced the availability of CrateDB 2.0, an open source SQL database that specializes in IoT and machine data. The innovation of CrateDB consists in leveraging SQL to aggregate and perform real-time analytics on IoT and machine data instead of the NoSQL databases commonly used in the industry for related use cases. CrateDB’s ability to accommodate the ingestion of high velocity streams of data and to perform queries on rapidly changing datasets, with impressive levels of scalability and latency, allows developers to combine their familiarity with SQL alongside a solution specially designed for the unique needs of IoT and machine data applications. CrateDB 2.0 features clustering upgrades that deliver improved query performance by means of faster aggregations and new index structures. In addition, CrateDB 2.0 contains a bevy of SQL enhancements that give developers a greater range of options regarding joins, sub-selects and the renaming and re-indexing of tables. The Enterprise Edition of CrateDB 2.0 offers performance monitoring, enhanced security as well as the ability for end users to create user-defined functions. CrateDB 2.0’s clustering upgrades, SQL enhancements and enterprise-grade security and performance monitoring mark a new milestone in the platform’s evolution that testifies to its readiness to embrace enterprise-grade workloads that include sensor data, GPS data and the industrial internet more generally. Subsequent to news of its general availability in December 2016, Crate.io’s release of open source and enterprise-grade versions of CrateDB underscores the early traction the platform has received, with over 1.3 million downloads and 50 customers using Crate.io in production. With the IoT and machine data space gearing up for a rampant proliferation of devices and corresponding datasets in forthcoming years, expect Crate.io to continue building on its recent momentum, particularly as organizations look for scalable databases that allow organizations to leverage widely available skillsets in SQL.

The graphic below illustrates the platform’s Enterprise Edition user interface for monitoring the performance of clusters gives users real-time visibility into cluster performance with respect to the ingestion and transformation of IoT and machine data:

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CrateDB Combines SQL-based Queries And Extreme Scalability For Machine Data Analytics

Crate.io today announces the general availability of CrateDB, an open source SQL-database platform that specializes in storing and analyzing machine data and related applications. CrateDB features a distributed SQL query engine that empowers users to run complex queries in real-time without the diminution of performance specific to “first generation SQL databases”, as noted in a press release. The platform also boasts columnar field caches and enhanced versatility with respect to SQL-based queries on machine data. For example, CrateDB delivers the capability to create outer joins as well as run queries on structured and unstructured data, perform time series analysis and leverage advanced database search functionality. In addition, CrateDB features extreme scalability marked by automated sharding and data redistribution that optimizes data performance and availability in correspondence with the volume of data stored within the platform. Importantly, CrateDB allows organizations to take advantage of SQL-oriented skills and tools to expedite its integration and adoption. As such, the platform represents a SQL-based alternative to NoSQL machine data solutions such as Splunk and Cassandra that empowers organizations to collect and analyze massive volumes of machine data in real-time in conjunction with the platform’s enhanced querying versatility and scalability. Available under an Apache 2.0 license, CrateDB marks the emergence of another key player in the machine data analytics space that promises to disrupt the landscape of machine data analytics platforms, particularly given the nexus of its advanced SQL-based querying functionality and extreme scalability. Organizations with resources versed primarily in SQL will lean toward CrateDB given the richness of its distributed SQL querying engine and ability to query data in real-time without resorting to an ancillary data warehousing option to append to their machine data analytics infrastructure.