For people getting started with Apache Druid, this lesson takes a developer through all the key planning phases for a Druid project, including purpose, queries, data sources, visualization, infrastructure, and documentation.

This lesson outlines various approaches to generating synthetic data for nonproduction uses, including data requirements and programmatic generation.

It’s easy to ingest batch data into Druid, turning files into full-indexed, high-performance tables using SQL commands. This lesson provides an overview of ingesting data from batch sources with in-database transformation capabilities and overall workflow automation.

This lesson outlines the key considerations when ingesting data from streaming pipelines including Apache Kafka and Amazon Kinesis. It includes information on Data Sources, Schema Definition, Ingestion Spec, and Monitoring.

Learn how to use SQL window functions in Apache Druid.

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.

This lesson covers key considerations for how to use nested data in Apache Druid.

Learn how to speed up your API with this trick.

Learn how to optimize Apache Druid for peak performance and concurrency.

This lesson walks through design and implementation of Druid infrastructure, including Initial Cluster Creation, Infrastructure Provider Choice, Adding and Removing Capacity, Landscape Design, and Performance Tuning

This lesson covers how to consider and implement monitoring for internal Druid operations and cluster infrastructure.

This lesson described how to make a Druid cluster secure, including authoriztion, authentication, network security, and best practices.

This lesson provides details on designing and implementing Druid to ensure always-on operations and high data durability.