Apache Druid

The time between an event arriving and a person acting on it is what Apache Druid sets out to shorten. It is a real-time analytics database designed for workflows where query speed and ingestion speed both matter, a pairing that general purpose warehouses tend to treat as two separate problems.

Druid does well behind user-facing interfaces, ad-hoc operational queries and workloads with high concurrency, and the project positions it as an open source alternative to a data warehouse for exactly those cases. Design documentation explains the segments, processes and architecture behind that behavior.

Interaction happens through APIs and a console that ships with the database.

• Query interfaces: HTTP and JDBC endpoints for loading, managing and querying data.
• Ingestion wizard: a point-and-click flow in the web console for streaming and batch ingestion, with monitoring for one-off tasks and supervisors.
• Cluster view: datasources, segments, ingestion tasks and services in one place, backed by SQL system tables whose underlying query you can read.
• Query workbench: somewhere to prototype DruidSQL and native queries before wiring up an external tool.
• Ecosystem: many third-party tools connect to Druid, and a separate druid-operator repository handles Kubernetes deployments.

Getting started means a local or Docker quickstart, both documented as tutorials. The documentation site carries manuals for the current release and for previous ones, and the project is developed at the Apache Software Foundation under the Apache License 2.0.