| Summary: | Build data engineering solutions with Spark programming in Python About This Video Build your own data engineering solutions using Spark structured API in Python Gain an in-depth understanding of the Apache Hadoop architecture, ecosystem, and practices Learn to apply Spark programming basics In Detail If you are looking to expand your knowledge in data engineering or want to level up your portfolio by adding Spark programming to your skillset, then you are in the right place. This course will help you understand Spark programming and apply that knowledge to build data engineering solutions. This course is example-driven and follows a working session-like approach. We will be taking a live coding approach and explaining all the concepts needed along the way. In this course, we will start with a quick introduction to Apache Spark, then set up our environment by installing and using Apache Spark. Next, we will learn about Spark execution model and architecture, and about Spark programming model and developer experience. Next, we will cover Spark structured API foundation and then move towards Spark data sources and sinks. Then we will cover Spark Dataframe and dataset transformations. We will also cover aggregations in Apache Spark and finally, we will cover Spark Dataframe joins. By the end of this course, you will be able to build data engineering solutions using Spark structured API in Python. Audience This course is designed for software engineers willing to develop a data engineering pipeline and application using Apache Spark; for data architects and data engineers who are responsible for designing and building the organization's data-centric infrastructure, for managers and architects who do not directly work with Spark implementation but work with the people who implement Apache Spark at the ground level. This course does not require any prior knowledge of Apache Spark or Hadoop; only programming knowledge using Python programming language is required
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