Real-time Stream Processing
As more and more data is being streamed, there is a growing need for the organizations to process the data instantly or near real time; this course will cover stream processing concepts and technologies that will allow participants to query, analyze, and process data as it flows.
The course aims to equip participants with knowledge and hands-on skills needed for a streaming data architecture and will also cover popular use cases driven by streaming data with the aim of helping participants learn to identify opportunities at work that could leverage stream processing.
Learn from a world-class industry expert
Courage Noko has over 12 years experience in Data engineering. He started at the New York Times where he was a Hadoop administrator/developer maintaining Hadoop clusters as well as writing MapReduce pipelines in Java and Pig. I also designed and implemented the first real time recommendation engine for nytimes.com.
He has led a team at Spotify to build a scalable real-time streaming platform that allows engineers to write real time recommendation engines, receive over 100s of millions of live events per seconds from streaming devices and run real time performance diagnostics. Over time, he has worked on several streaming frameworks: Apache Beam/Dataflow, Spark, Flink. And Storm.
Lately, he has taken interest in real-time analytics, leading teams to build and deploy platforms that support OLAP databases such as Druid, ClickHouse and Pinot.
Prerequisites:
Knowledge of basic data processing (filtering, map, GroupBy, Sum, count).
This course is for:
Data Scientists, Data Engineers and ML Engineers
What you will be able to do after this course:
Write streaming pipelines using different streaming platforms and their features.Additional information about the course
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