Modeling
Learn the best practices for data modeling in Druid from partioning to schema design patterns and nested data.
The Promise (and Limitations) of Range Partitions
Learn how to improve read times with range partitions.
An Introduction to Window Functions
Learn all about window functions
Multi-dimensional range partioning in Druid
Druid always partitions data by the timestamp dimension to benefit time-based analytical queries. A secondary partitioning is available to further break down the time chunks into manageable partition sizes.
Joins in Apache Druid
This blog explores Druid’s multiple options for joins, including ingestion-time and query-time joins, catering to different use cases and data scenarios.
Exploring Unnest in Druid
This article shows how Druid supports multi-value strings through multi-value dimensions (MVDs), which automatically flattens during a group-by.
Learn how to achieve sub-second responses with Apache Druid
A review of Druid’s query processing engine with an eye on performance. Provides many data modeling and query tips that improve response times.
The Significance of Schema Auto-Discovery in Apache Druid
This article provides a technical overview of the schema auto-discovery feature in Apache Druid through a practical IoT telemetry use case.
How the Time Series Extension Can Enable IoT use Cases in Imply Polaris
Temporal and real-time data have always been the cornerstones of Apache Druid, making it a natural fit for IoT applications that collect and analyze real-time sensor data. However, there was a subset of functionality that was available in other time-series databases that was missing from Druid. With the recently released time series extension from Imply, that gap has been closed, and Imply now has the ability to perform advanced time series analysis.
How to Execute Window Functions on Sketches
Learn how to use SQL window functions in Apache Druid.
Working with changing data
This lesson covers key considerations when working with data sources that mutate over time, including creating tables from streaming data and using schema auto-discovery.
Working with Nested JSON in Druid
This lesson covers key considerations for how to use nested data in Apache Druid.
How to Use Window Functions and Theta Sketches for User Behavior Analytics
Learn how Theta Sketches and window functions can power user behavior analytics
Pruning in Druid
Fourth and last video in the Partitioning Series that describes and demos how different partitioning strategies provide segment pruning at query time.
Range Partitioning in Druid
Third video in the Partitioning Series that explains how single dimension and range partitioning work.
Hash Partitioning in Druid
Second video in the Partitioning Series that describes the details of how hash partitioning works.
Intro & Dynamic Partitioning in Druid
First in the Partitioning Series of videos that explains the concepts of time partitioning and secondary partitioning.
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