Apache Druid
Apache Druid is a high-performance, real-time analytics database designed to deliver sub-second queries on both streaming and batch data, even at massive scale and under heavy load. It is tailored for organizations and developers who need to analyze high-cardinality and high-dimensional datasets with billions to trillions of rows, without the need for pre-defining or caching queries.
Key Features
- Sub-Second Queries: Execute OLAP queries in milliseconds on large datasets using a scatter/gather approach with data preloaded into memory or local storage.
- High Concurrency: Supports 100s to 100,000s of queries per second with a cost-efficient architecture that minimizes infrastructure needs.
- Real-Time and Historical Insights: Seamlessly integrates with streaming platforms like Apache Kafka and Amazon Kinesis for query-on-arrival at millions of events per second.
- Interactive Query Engine: Avoids data movement and network latency for faster query execution.
- Elastic Architecture: Features loosely coupled components for easy scaling, combined with a deep storage layer.
- True Stream Ingestion: Offers connector-free integration with streaming platforms for low-latency, high-scalability data ingestion.
- SQL Support: Provides a familiar SQL API for end-to-end data operations including ingestion, transformation, and querying.
Use Cases
Apache Druid is ideal for building real-time analytics applications, powering dashboards, and supporting business intelligence (BI) tools. It is widely used by leading companies for massive-scale data analysis, making it a proven solution for industries requiring instant insights from streaming and historical data.
