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

In this blog, we’ll review the simple steps to export data from ClickHouse in a format that is easy to ingest into Polaris.

Druid is a magical data box that can answer any question about your data. It does this by reading the data from lots of little pieces of paper called segments. The segments are very small and easy to store, but when you want to do something with them, Druid combines the segments together like a jigsaw puzzle.

A simple set of instructions to deploy Apache Druid on minikube using minio for local deep storage on your laptop.

Software Engineer Nicholas Lippis explains how his team developed a license-key generation and management service that our employees can use to generate secure keys for Imply customers. A lambda (serverless) architecture came to be the design breakthrough that helped balance cost with functionality

Imply is a real-time data platform for self-service analytics. It is very well suited for high performance analytics against event-driven data. One of the common use cases is to store, analyze, and visualize different types of networking data (NetFlow v5/v9, sFlow, IPFIX, etc.).

In Apache Druid, Compaction basically helps with managing the segments for a given datasource. Using compaction, we can either merge smaller segments or split large segments to optimize segment size. One of the first options to consider would be to determine, if the segments could be generated optimally. If that isn’t possible, compaction would be required.

In this post, I am going to talk a bit about Apache Druid and a recently documented configuration option that enables true NULL values to be stored and queried for better SQL compatibility: druid.generic.useDefaultValueForNull=false, and in the process do a deep dive into how it relates to a small sliver of the query processing system as we explore the performance of this feature.

When Hadoop is pushing data into Druid, Hadoop indexer performance is key and becomes challenging at scale. There are a quite a few things to consider when running large scale Hadoop indexing.