Master Big Data Realtime Streaming



What you’ll learn

  • Learn designing an end-to-end Real-time Streaming pipeline for Big Data using latest technologies.

  • Understand the different components in Big Data streaming pipeline.

  • Use Kafka as the connecting tool between ETL components in the real-time streaming pipeline.

  • Use Apache Flink, Spark Streaming and Kafka Streams to perform different transformations and aggregations.

  • Use Druid and Pinot as OLAP technologies in the streaming pipeline.

  • Use Superset to visualize the real-time incoming data stream to explore and visualize the transformed data.

  • Hands-on Practicals helping you build all the components and forming a complete end-to-end pipeline.

  • Learn multiple technologies used in Real-time Streaming pipelines, and you can use the one that better suits your use-case.


  • An exposure to Big Data world will help you better appreciate Real-time Streaming pipelines, but is completely optional.

  • Basic knowledge of Java and Scala will be helpful, but not mandatory.

Getting real-time insights from huge volumes of data is very important for a majority of companies today.

Big data Real-time streaming is used by some of the biggest companies in the world like e-commerce companies, Video streaming companies, Banks, Ride-hailing companies, etc.

Knowing about the concepts of realtime streaming and the various realtime streaming technologies will be a great addition to your skillset and will enable you to build some of the most cutting-edge solutions that exist today.

We have created this Hands-On Course so that you get a good understanding about how realtime streaming systems can be built

This course will ensure that you get a hands-on experience with Apache Kafka, Apache Flink, Spark Streaming, Kafka Streams, Apache Pinot, Apache Druid, and Apache Superset.

This course covers the following topics

  • An Introduction to Kafka with hands-on Kafka setup
  • Understanding basic transformations and aggregations which can be done in a real time system
  • Learn how transformations and aggregations can be done using Apache Flink with hands-on coding exercises
  • Learn how transformations and aggregations can be done using Spark streaming with hands-on coding exercises
  • Learn how Kafka streams can be used to perform transformations and aggregations with hands-on coding exercises
  • Ingest data into Apache Pinot which is an OLAP technology
  • Ingest data into Apache Druid which is also an OLAP technology
  • Using Apache Superset to create some insightful dashboards
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If you are interested in learning how all these technologies can be connected together to build an end to end real-time streaming system, then this course is for you.

Who this course is for:

  • Students who want to learn building real-time streaming pipelines from SCRATCH to its Live Project Implementation.
  • Students who want to learn latest technologies that are used in Big Data Engineering.
  • Developers who want to learn different well-known tools to build streaming pipelines.
  • Students who want to pursue and grow career in Data Engineering.

I am software architect by profession. I have worked primarily in building e-commerce technologies and also designed systems in Big Data Analytics domain. I am an active technical interviewer, and have taken 500+ coding and software design interviews. I love to share technical knowledge and help budding engineers with their career growth.

Senior Software Engineer, Part Time Tech Blogger

I am a Software Engineer, a curious techie, and a part-time tech blogger.

I have a good amount of experience in dealing with highly scalable distributed applications in production.

The applications I work on, deal with millions of records coming in every minute and also respond to millions of HTTP requests every minute.

I Love to teach and maintain a personal blog where I write articles on a variety of technical topics.

As an Instructor in Udemy I want to ensure that I am able to teach the concepts that I know in an easy and understandable manner.

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