Achieve Sub-Second Analytics on Apache Kafka with Confluent and Imply

Analytic pipelines running purely on batch processing systems can suffer from hours of data lag, resulting in accuracy issues with analysis and overall decision-making. Join us for a demo to learn how easy it is to integrate your Apache Kafka streams in Apache Druid (incubating) to provide real-time insights into the data.

In this online talk, you’ll hear about ingesting your Kafka streams into Imply’s scalable analytic engine and gaining real-time insights via a modern user interface.

Watch now to learn about:

• The benefits of combining a real-time streaming platform with a comprehensive analytics stack
• Building an analytics pipeline by integrating Confluent Platform and Imply
• How KSQL, streaming SQL for Kafka, can easily transform and filter streams of data in real time
• Querying and visualizing streaming data in Imply
• Practical ways to implement Confluent Platform and Imply to address common use cases such as analyzing network flows, collecting and monitoring IoT data and visualizing clickstream data

Confluent Platform, developed by the creators of Kafka, enables the ingest and processing of massive amounts of real-time event data. Imply, the complete analytics stack built on Druid, can ingest, store, query and visualize streaming data from Confluent Platform, enabling end-to-end real-time analytics. Together, Confluent and Imply can provide low latency data delivery, data transform, and data querying capabilities to power a range of use cases.