Build analytics apps on lakes and streams with Apache Druid
The good news: you are generating millions of events in a flash. But now you need to include that new streaming data alongside data lake sources and enable analytics immediately–not waiting for streams to land in your object store. Your application needs to do double-duty: provide real-time operational insights and ad-hoc analysis of historical data.
In this session:
- Learn the basics of Apache Druid for analytics applications
- See how Druid can extend your application to include streaming data in addition to your data lake insights
- Hear how companies like Target, Netflix, Twitter, Confluent, and Cisco use Apache Druid for real-time and historical analysis
Ready to Get Started? Here Are Some Resources to Help
What Is a Data Lakehouse?
The data lakehouse is a new architecture that combines the best parts of data lakes and data warehouses. Learn more about the data lakehouse and its key advantages.read more
Simplifying Data Mesh for Self-Service Analytics on an Open Data Lakehouse
The adoption of data mesh as a decentralized data management approach has become popular in recent years, helping teams overcome challenges associated with centralized data architecture.read more