To meet critical requirements, the Confluence Analytics Experience Team chose to deploy Imply Enterprise Hybrid, a complete, real-time database built from Apache Druid® that runs in Atlassian’s VPC with Imply’s management control plane.
A stack for real-time analytics applications.
Earlier this year, we explained how we’re moving to monthly releases that fall into two different categories: Short Term Support releases and Long Term Support releases. Each of the two categories is geared towards the needs of a specific set of Imply users.
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PivotTable (or CrossTab) views are a staple of data analytics software since their popularization by Microsoft Excel. The basics are very simple: you filter the data and drag dimensions into rows and columns to create a two dimensional grid of “cross-tabulated” values for every intersection.
To better serve the needs of both types of users, we’re changing our release strategy. As of November 2020, Imply releases will follow one of two tracks: monthly Short Term Support (STS) releases and an annual Long Term Support (LTS) release.
Recently, I joined Imply. I’ll further discuss this on a different post, but what really amazed me during these first few days, is how much this company and the team believe in eating our own dog food.
Apache Druid 0.20.0 contains over 140 updates from 36 contributors, including new features, major performance enhancements (6x-11x on some queries!), bug fixes, and major documentation improvements.
Welcome to Imply 4.0! This release includes significant (6-11X) query performance improvements, plus management and usability enhancements.
Snowflake's IPO today is a testament to the importance of data analytics and the cloud in business today. This post describes how Imply complements cloud data warehouses for a growing set of workloads called interactive data applications, which require sub-second query response at high user concurrency.
The Apache Druid community released Druid 0.19 on July 21st, 2020. This release contains over 200 new features, performance enhancements, bug fixes, and major documentation improvements from 47 contributors.
One of the most important considerations when selecting an analytics platform is its suitability to conduct the required analyses over specific types of data within a given performance threshold. A helpful way to think about this is to utilize the concept of temperature-tiered analytics to align analytics needs with data architectures.
