Data modeling with Druid

This module addresses best practices and considerations for data modeling in Druid. It covers why you can’t cut and paste a data model from an RDBMS, denormalization/flattening; schema design; column inclusion; field type options and selection; use of aggregation, complex data types like hyperunique and datasketches and multi-value arrays

Introduction to Druid University

An introduction to Druid University.

What is Apache Druid?

An overview of the Apache Druid real-time analytics database

How does Apache Druid work?

Covers the key features and functionality of Apache Druid.

What can you use Apache Druid for?

Discusses use case for Apache Druid.

Druid architecture

A walk through the architecture of Apache Druid.

Druid file format

The details and benefits of the Druid columnar file format.

Data modeling with Druid

Best practices and considerations for data modeling in Druid.

Druid and Kafka

Build an ingestion spec for data streaming from Apache Kafka.

Druid native batch

Build an ingestion spec for Druid native batch ingestion.

Druid SQL

Using the Druid SQL API.

Imply Pivot analytics UI

A brief walkthrough of Imply Pivot analytics UI.

Conclusion

A short Druid University summary and next steps video.