For other databases, stream ingestion is an afterthought. Druid was built for it.

  • Native connectivity
  • Massive scalability
  • Query on arrival
  • Guaranteed consistency

Handle billions
of daily events

Never worry about
data consistency

Query in actual

Native Connectivity

Connectors? Don’t need ‘em.

Druid connects natively to Apache Kafka and Amazon Kinesis with indexing processes that actually use Kafka’s own partition and offset mechanisms and Kinesis’ own shard and sequence mechanism. There’s no connectors to update and maintain. And there’s no batch processing masquerading as “streaming” like other databases do.

Learn how to do it

Read how Confluent connects

Massive Scalability

Millions of events? Druid does billions.

Druid can handle billions of events per day with an architecture that takes streaming topics and turns them into tables which are partitioned over multiple servers (thousands of them if you need it). Combined with batch ingestion, applications can compare real-time and historical data.

Learn how to auto scale Kafka streams

Read how Target ingests thousands of sources

Query on Arrival

Insight the moment events happen

With Druid, you don’t wait for events to be persisted to storage like you do with other databases. Druid ingests streaming data into segments, each held in memory while written to deep storage, so they are available instantly to your application.

Learn the details

Read how Netflix gets real-time insight

Guaranteed Consistency

No workarounds, no surprises

Druid has a true event-by-event ingestion engine, meaning guaranteed exactly once ingestion without the roll your own solutions that other databases require. Plus, if there is a failure during ingestion, Druid automatically recovers with no data loss – even events that arrive during the outage.

Read the docs

Learn how to simplify with exactly once ingestion

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