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Using Imply Pivot with Druid to Deduplicate Timeseries Data
Imply Pivot offers multi step aggregations, which is valuable for timeseries data where measures are not evenly distributed in time.
Migrating Data from ClickHouse to Imply Polaris
In this blog, we’ll review the simple steps to export data from ClickHouse in a format that is easy to ingest into Polaris.
For April 1st: a New Description of Apache Druid from Our Youngest Technical Architect
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.
Clustered Apache Druid® on your Laptop – Easy!
A simple set of instructions to deploy Apache Druid on minikube using minio for local deep storage on your laptop.
Design on a dime: How we built a license-key generator using AWS serverless architecture
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
Tutorial: Add BGP Analytics to your Imply netflow analysis
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.).
Apache Druid Best Practices – Determining Worker Capacity (slots) for Automatic Compaction
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.
Behold the void: Measuring the performance impact of numeric column NULL checks in Apache Druid
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.