The key takeaways from this section include:

• Big Data usually refers to datasets that grow so large that they become awkward to work with using traditional database management systems and analytical approaches
• Big data refers to data that is terabytes (TB) to petabytes (PB) in size
• MapReduce can be used to split big datasets up in smaller sets to be distributed over several machines to deal with Big Data Analytics
• Before starting to work, you need to install Docker and Kinematic on your environment
• Make sure to test your installation so you're sure everything is working
• When you start working with PySpark, you have to create a SparkContext()
• The creation or RDDs is essential when working with PySpark
• Examples of actions and transformations include collect(), count(), filter(), first(), take(), and reduce()
• Machine Learning on the scale of big data can be done with Spark using the ml library