Apache Druid

About Apache Druid

A column-oriented, distributed data store designed for sub-second OLAP queries on event data. Druid is used for powering interactive analytical applications, real-time dashboards, and exploratory analytics on high-cardinality data.

Key Features

• 1Real-time OLAP database designed for sub-second queries on event streams
• 2Ingests from Kafka, Kinesis, S3, and HDFS with streaming and batch modes
• 3Column-oriented storage with bitmap indexing for high-cardinality dimensions
• 4Approximate query support (HyperLogLog, quantiles) for extreme scale
• 5Tiered storage moves cold data to object storage automatically

How Python Data Engineers Use Apache Druid

Python data engineers use `pydruid` or Druid's HTTP API to query real-time analytics data — computing slice-and-dice metrics over millions of events that were ingested from Kafka seconds earlier. Druid powers dashboards that need to answer 'how many users did X in the last 5 minutes?' queries that would be too slow on traditional data warehouses.