Analytics pipelines running purely on batch processing systems can suffer from hours of data lag. Initial attempts to solve this problem often lead to inflexible solutions, where the queries must be known ahead of time, or fragile solutions where the integrity of the data cannot be assured. Combining Kafka, and Druid can guarantee system availability, maintain data integrity, and support fast and flexible queries.
Learn how to build an end-to-end streaming analytic stack by combining a message bus (Apache Kafka), a stream processor, and a query engine (Apache Druid).
Netflix shares their experience of using Druid and how it has helped provide the best streaming experience to their users through a series of lightning talks.
Learn more about how Druid can power exploratory workflows that go beyond dashboarding and reporting.
Imply is an operational data analytics platform that is designed from the ground up for event-driven data.
Learn about how open source streaming technologies such as Apache Kafka and Apache Druid can be combined to analyze network traffic data.
A look into what is needed to build a true OLAP streaming big-data platform.
A brief interview with Imply's CTO and cofounder, Gian Merlino.
Imply's cofounder and CTO, Gian Merlino, presents about Druid and operational analytic databases.
Learn about how Apache Kafka and Apache Druid can be combined to form an end-to-end streaming analytics stack.
A quick walk-though of Imply quickstart. Learn how to set up Imply and load some example data.