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Real-time Data Processing for Data Engineers

by Daniel Stern

In today's world, valuable data can often be produced in great quantity and at great speed. This course will teach you to process data in real time and keep up with high-volume data throughput.

What you'll learn

Data is often the most valuable asset an organization has, but how do you process data in real time, at scale, when data can be coming in at huge volumes from disparate sources in varying formats?

In this course, Real-time Data Processing for Data Engineers, you’ll gain the ability to build and contribute to professional data pipelines capable of processing big data at scale.

First, you’ll explore the fundamentals of data processing at scale, including what data pipelines are, how they are implemented, and how they vary from batch data processing.

Next, you’ll discover key data pipeline tooling such as Apache Kafka and Spark, and take a look at their strengths and where they fit into data pipelines.

Finally, you’ll learn how to implement data pipelines, first by reviewing an example scenario, and then via a coding example where you will implement a working data pipeline in minutes.

When you’re finished with this course, you’ll have the skills and knowledge of real-time data processing needed to effectively process real-time data at scale.

About the author

Daniel Stern is a coder, web developer and programming enthusiast from Toronto, Ontario, where he works as a freelance developer and designer. Daniel has been working with web technologies since the days of the dial-up, and is especially keen on JavaScript, CSS, Angular, React and TypeScript. Over the course of his work as an open-source developer, he's created many community-standards web tools including Angular Audio and Range.CSS. His tool, Range.CSS, was featured in a guest article on CSS-T... more

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