In this module, we will dive into Scala programming, covering its unique syntax, control flow, and key data structures like Map and List. You’ll get plenty of practice to ensure you’re comfortable with the language before jumping into Spark.

In this module, we will break down the structure and usage of RDDs, the building blocks of Spark applications. You’ll work on real-world examples, including building histograms and analyzing social network data.

In this module, we will introduce SparkSQL, DataFrames, and Datasets, which provide a higher-level abstraction for working with structured data. You’ll also compare and contrast the use of RDDs with these APIs.

In this module, we will tackle advanced Spark programming examples, such as finding popular movies and superheroes in a social graph. We will also cover optimization techniques using broadcast variables and accumulators.

In this module, we will move from running Spark on a local desktop to deploying and scaling it on a real cluster using Amazon Elastic MapReduce (EMR). You’ll learn how to submit jobs, manage dependencies, and troubleshoot issues.

In this module, we will explore Spark MLlib for machine learning. You’ll work through examples like generating movie recommendations and performing linear regression on large datasets.

In this module, we will focus on real-time data processing using Spark Streaming. You’ll build streaming applications and learn to handle data in motion, both with DStreams and the newer Structured Streaming API.

In this module, we will cover Spark’s GraphX library for graph-parallel processing, using it to explore social network data and understand how connected entities are through complex algorithms like breadth-first search.

In this final module, we will provide additional learning resources and give you tips on leveraging your new skills to advance your career in the field of big data.