Apache Druid

Apache Druid is a high-performance real-time analytics database designed for interactive OLAP queries on large, high-cardinality datasets. It supports both streaming and batch ingestion and is optimized for low-latency queries under high concurrency.

Key Features

• Sub-second interactive query engine optimized for high-dimensional, high-cardinality data
• Native streaming ingestion designed for query-on-arrival use cases
• Columnar storage with time indexing, dictionary encoding, bitmap indexes, and compression
• SQL API plus native query APIs over HTTP, including JDBC connectivity
• Built-in web console for ingestion setup, query exploration, and cluster visibility
• Elastic, loosely coupled architecture separating ingestion, query, and coordination services
• Tiering and quality-of-service controls to prioritize mixed workloads

Use Cases

• Powering real-time analytics dashboards and embedded analytics in user-facing applications
• Ad-hoc operational analytics on event, clickstream, and observability-style data
• High-concurrency OLAP analytics on time-series and event data from streaming platforms

Limitations and Considerations

• Operates as a distributed system with multiple service types, which can increase operational complexity compared to single-node databases
• Designed primarily for analytics workloads; it is not a general-purpose OLTP database

Apache Druid is well-suited for organizations that need fast, consistent analytical queries on continuously arriving data. Its storage format and distributed architecture make it effective for high-scale, high-concurrency real-time analytics applications.