Imply 3.3 Extends Performance and Reduces TCO for Real-Time Intelligence
Addition of SQL JOIN operations to the world’s fastest real-time data platform delivers cost-effective self-service analytics to day-to-day decision-makers
Imply, the real-time intelligence company, announced Imply 3.3, featuring enhancements that help customers optimize their analytics spend and improve time to insight on their freshest data while extending Imply’s leading real-time analytics performance to a broader set of queries. An Imply 3.3 download or cloud trial is available on the Imply website.
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Enterprise leaders in a range of industries use Imply to deliver self-service real-time analytics to their business users, make business intelligence interactive and exploratory, and create data-driven applications for their customers. They’ve wanted the ability to query multiple data sets directly using standard SQL operations while not sacrificing performance, and now they can.
Imply 3.3 takes advantage of new SQL JOIN support in Apache Druid 0.18. The addition of JOIN operations broadens Druid’s performance advantage over data warehouses and data lake query engines by leveraging Druid’s architectural advantages such as advanced indexing and horizontal query distribution. Druid’s innate query speed advantage over data lake query engines was demonstrated last year by researchers at the University of Minho (Portugal). Druid displayed a 10X to 59X advantage over Presto and was 110X to 190X faster than Apache Hive.
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Support for JOIN operations reduces cloud data storage volumes and compute costs, and enables broad adoption of self-service analytics. Previously, multiple data sets would have to be “flattened” into a single table which included redundant data and made updates expensive. Now multiple data sets can be used “as is,” simplifying data pipelines and creating substantial savings by reducing storage costs, data ingestion costs and maintenance costs.
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