Big Data for Architects

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This course will help you explore the world of Big Data technologies and frameworks. You will develop skills that will help you to pick the right Big Data technology and framework for your job and build the confidence to design robust Big Data pipelines.

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74 on-demand videos & exercises
Level: Intermediate
8hrs 19mins
Access on mobile, web and TV

What to know about this course

Do you want a guide that will help you to pick the right Big Data technology for your project? Or do you want to get a solid understanding of the Big Data architecture and pipelines? This course will help you out. After highlighting the course structure and learning objectives, the course will take you through the steps needed for setting up the environment. Next, you will understand the Big Data logical architecture, study the evolution of Big Data technologies, and explore Big Data pipelines. Moving along, you will become familiar with ingestion frameworks, such as Kafka, Flume, Nifi, and Sqoop. Next, you will learn about key storage frameworks, such as HDFS, HBase, Kudu, and Cassandra. Finally, you will go through the various data formats and uncover key data processing and data analysis frameworks. By the end of this course, you will have a good understanding of the Big Data architecture and technologies and will have developed the skills to build real-world Big Data pipelines. All the resources and support files for this course are available at

Who's this course for?

If you are a software engineer, who is looking to build Big Data pipelines or planning to appear for certifications such as CCA175 or CCA159, this video course is for you. A basic understanding of Big Data is needed to get started with this course.

What you'll learn

  • Create a Google account and a Dataproc cluster.
  • Understand the Big Data architecture and pipelines.
  • Study factors to consider while comparing ingestion frameworks.
  • Gain a solid understanding of storage frameworks.
  • Distinguish between text and binary data format.
  • Find the key differences between the Spark, Tez, and Flink frameworks.
  • Build a scalable Extract, Transform, Load (ETL) pipeline with Kafka Connect.

Key Features

  • Get a holistic picture of the Big Data ecosystem.
  • Become an expert in choosing Big Data technology as per the requirements.
  • Get ready to build end-to-end Big Data batch and streaming pipelines.

Course Curriculum

About the Author

Data Circuit

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