Apache Kafka and Apache Druid

The perfect match


Let's talk about Kafka & Druid!

Analyze millions of events per second in your application

With Kafka, you can get millions of events in a flash. But how can you interactively analyze all those events as they happen with hundreds or thousands of concurrent users? Typical databases can’t handle the high concurrency and volume of real-time events, even the ones that promise “unlimited scalability.” 

Developers from NetflixTwitterConfluentSalesforce, and many others choose Druid as their real-time analytics database, often pairing it with Confluent/Kafka in what we call the K2D Stack: Kafka to Druid. 

Confluent Kafka and Imply Druid Analytics Stack for Real Time Analytics Applications
K2D Stack: Kafka serves as the event streaming platform, Druid serves as the real-time analytics database

With Imply Polaris, you can have Druid up and running in minutes, with built-in Confluent Cloud and a slice-and-dice visualizer – perfect for a quick proof-of-concept.


Druid is unique in the streaming analytics world

Developers love Druid because it natively connects to Kafka and can handle millions of events per second.

In addition to high speed, high volume streaming capability, Druid ingests and queries batch data as well and offers a query engine optimized for long-running queries. Getting both streaming and batch in one place, simplifying your application architecture.

Key Challenges

Query events the moment they hit the cluster – no waiting for the events to persist to storage

True event-by-event ingestion – not batch ingestion masquerading as streaming 

Exactly-once semantics (no duplicates) – without workarounds or writing special queries

“We built an observability platform powered by Kafka and Druid. This solution ingests over 3.5 million events per second and handles hundreds of queries on top of that. And this gives us real-time insights into the operations of thousands of these Kafka clusters within Confluent Cloud”
Jay Kreps Confluent
Jay Kreps  |  CEO, Confluent

Read More about Apache Kafka
and Apache Druid


Case Study:
Confluent Health+


Confluent Health+ provides Confluent Cloud customers with the visibility needed to ensure the health of their data-in-motion infrastructure and to minimize business disruption. Health+ offers intelligent alerts, cloud-based monitoring and visualizations, and a streamlined support experience.

“Leveraging Druid as part of our stack means we don’t shy away from high-cardinality data which means we can find the needle in the haystack. As a result, our teams can detect problems before they emerge and quickly troubleshoot issues to improve the overall customer experience.

The flexibility we have with Druid also means we can expose the same data we use internally also to our customers, giving them detailed insights into how their own applications are behaving.”

Xavier Leaute and Zohreh Karimi, Lead Engineers at Confluent

Learn more about Apache Druid

An Introduction to Apache Druid

Druid’s flexible, efficient, and resilient design make it the best choice for modern analytics applications. Here’s a quick but technical overview of what makes Druid different.

An Introduction to Analytics Applications

The world of analytics is shifting from traditional BI solutions designed by data engineers to custom applications created by developers. Learn what this is all about and what it takes to build a successful analytics application.

More ways to support your Druid journey


Grow your knowledge and developer power with Apache Druid Basics Accreditation

The Imply Accreditation Program for Apache Druid Basics begins by demonstrating how to set up a local environment, followed by instruction on architecture, the importance of processes and how they interact with each other. Delegates then learn how to ingest data into Druid and finally how to query it.

This expert-led training is completely free, and you’ll earn a certificate that you can display on your LinkedIn profile after passing the exam!

Apache Druid Basics Accreditation Program

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