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Imply Pivot offers multi step aggregations, which is valuable for timeseries data where measures are not evenly distributed in time.

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.

We are excited to announce the general availability of Imply 3.3, which greatly expands the expressivity of SQL by adding the ability to do JOINs and interact with lookups in novel ways. This release includes over 200 new features, performance enhancements, bug fixes, and major documentation improvements.

Today, Imply announced a new round of growth funding raised from Andreessen Horowitz and Geodesic Capital. First I want to thank these firms for their support and for sharing our vision as we continue to grow. Thank you, too, to all of our customers for joining us on this journey. A milestone like this is a good time to reflect on the past and the future.

In the 0.16 console, we have added a new layer of SQL awareness to the query view to help move the view away from its roots as a text-only interface, to a point and click one.

We are delighted to announce that Imply 3.0 is now available! It contains many usability features such as a visual data loader, on-premises cluster and alerts functionality.

Imply 2.8 comes with the first Apache release of Druid and a host of features aimed at performance improvements and ease of use.

We're excited to announce Imply 2.2, with tons of new features for Pivot.

We are extremely excited to announce Imply 2.1, a feature-packed release, available now for download. Native SQL comes to Druid and dashboarding is now possible in Pivot.