Streaming Data + Context: Intro to Stream Analytics - an Imply Lightboard
When building analytics applications, choosing the right database for the data you’re using is important. While nearly all databases are designed for batch processing, only a few can handle stream (hint: one of those is Apache Druid).
Watch Imply’s own Director of Technology Darin Briskman explore the world of stream analytics, stream processors, and real-time databases, including:
-The difference between stream processors and real-time analytics databases
-Enhancing and enriching stream data
-Combining stream ingestion and batch ingestion to understand real-time data in context
Learn more about Druid and Stream Analytics:
Website: https://imply.io/
Druid Architecture & Concepts: https://imply.io/druid-architecture-c...
When Streaming Analytics…Isn’t: https://imply.io/blog/when-streaming-...
Living the Stream: https://imply.io/blog/living-the-stream/
Connect
Subscribe: / implydata
Imply GitHub: https://github.com/implydata
Apache Druid GitHub: https://github.com/apache/druid
Twitter: / implydata
LinkedIn: / imply
About Imply
Developers are in the driver’s seat when it comes to analytics, building applications that serve real-time insights on terabytes to petabytes of streaming and batch data at hundreds to thousands of queries per second.
With Imply, developers have a database that is uniquely built for these analytics applications, delivering sub-second queries at scale and under load. The result? No spinning wheel and no limit to the analytics in their applications.
Check us out at https://imply.io/