This Introduction to Spark Programming training class introduces the Apache Spark distributed computing engine and is suitable for developers, data analysts, architects, technical managers, and anyone who needs to use Spark in a hands-on manner.
The course provides a solid technical introduction to the Spark architecture and how Spark works. It covers the basic building blocks of Spark (e.g., RDDs and the distributed compute engine), as well as higher-level constructs that provide a simpler and more capable interface (e.g., Spark SQL and DataFrames). It also covers more advanced capabilities such as the use of Spark Streaming to process streaming data and provides an overview of Spark GraphX (graph processing) and Spark MLlib (machine learning). Finally, the course explores possible performance issues and strategies for optimization.
The course is very hands-on, with many labs. Participants will interact with Spark through the Spark shell (for interactive, ad hoc processing) as well as through programs using the Spark API. Labs currently support Scala; support of Python/Java is available.
The Apache Spark distributed computing engine is rapidly becoming a primary tool in the processing and analyzing of large-scale data sets. It has many advantages over existing engines, such as Hadoop, including runtime speeds that are 10-100x faster, as well as a much simpler programming model. After taking this course, you will be ready to work with Spark in an informed and productive manner.
Each student in our Live Online and our Onsite classes receives a comprehensive set of materials, including course notes and all the class examples.
Experience in the following is required for this Spark class: