The focus of this release, which includes includes Druid 0.12.1 is on improving the ability to analyze and understand variations in the data.
The main features of this release are:
One use case that users express again and again is the desire to explore and manipulate the data but also to have historical context to guide that exploration. With Imply 2.6, the ability to compare with different time
frames is now available.
You can now select a reference time period to compare against and all of your measures will be augmented with delta indicators visualizing their change. In the image above, the light gray line is the historical which provides useful context to the blue line, which is the time period being analyzed.
The time compare augmented visualizations can be composed into dashboards allowing you to get a quick overview of your relative performance.
Furthermore, it is possible to sort on the data by the delta change column which, in one click, would bring the the biggest movers in a given measure to the top of the view allowing you to see which values changed the most.
For more details have a look at the feature video
Sometimes it is useful to look at the data in another application (most often Microsoft Excel). Once you find the precise slice of data you were looking for it is important to be able to get the results that you need in the right format.
With this release, the data comprising the results can be delivered in CSV, TSV, Excel, and JSON formats. Moreover, it is possible to pick the number of rows that you want to get back, up to and including all the
rows for a dimension.
For more details have a look at the feature video
We have added the ability to express an internal regrouping within a measures aggregation. This enables advanced use-cases such as computing daily active users or computing 5 minutely 95th percentiles that are commonly used for burstable billing in the netflow world.
Imply 2.6 is available today Stay tuned for even more features for Druid and Imply.
Why You Shouldn’t Have to Delete Your VPC Flow Logs
When a security incident happens, investigators almost always start with the same questions: Which systems communicated? Where did the traffic originate? What changed before the incident? Was data exfiltrated?...
Learn MoreSplunk Smartstore vs Lumi Loglake: Two Very Different Ways to Search Logs in Object Storage
One copies data back before it can be searched. The other queries it where it lives. Lumi Loglake lets Splunk teams query logs directly in object storage, including AWS S3, Delta Lake, Apache Iceberg, using...
Learn MoreSupercharging Schema-On-Read: Logs in Object Storage Don’t Need a Data Catalog
Machine data architectures are rapidly changing. As telemetry volumes continue to grow and as costs rise, organizations are increasingly moving logs and other machine data into object stores such as AWS S3....
Learn More
Vadim Ogievetsky