Apache Druid
Real-time analytics database for sub-second queries
Apache Druid is profiled here as a Data Warehouse tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
Apache Druid is an open-source real-time analytics database, created at Metamarkets by Eric Tschetter and Fangjin Yang, designed for fast aggregation queries over large event streams. It ingests data from streaming sources like Kafka and from batch files, then serves slice-and-dice queries with sub-second latency across high-cardinality data. Druid powers user-facing analytics and operational dashboards where many users run interactive queries at once, and its distributed design scales ingestion and querying independently.
Key Capabilities:
Sub-second aggregation queries over large, high-cardinality datasets
Streaming ingestion from Kafka and Kinesis with exactly-once handling
Batch ingestion from files and object storage
A columnar format with bitmap indexes tuned for analytics
A distributed architecture that scales ingestion and querying separately
Native support for time-series and event data
Alternative tools
- Tinybird
Managed ClickHouse for building real-time analytics APIs
- Amazon Redshift
Cloud data warehouse for large-scale analytics on AWS
- Databricks
Lakehouse platform unifying data engineering and AI
- BigQuery
Serverless, petabyte-scale cloud data warehouse
- DuckDB
In-process analytical database for fast local queries
- ClickHouse
Open-source columnar database for real-time analytics