Chapter 5: Big Data Analytics
Optimizations in Apache Spark play a crucial role while building big data solutions. Knowledge and experience in tuning Spark-based workloads help organizations save costs and time while running these workloads on the cloud. In this chapter, we will learn about various optimization techniques concerning Spark DataFrames and big data analytics in general. We will learn about the limitations of the collect() method and inferSchema when reading data. This will be followed by an overview of the best practices for working with CSV files, Parquet files, Pandas projects, and Koalas projects. Also, we will learn about some powerful optimization techniques, such as column predicate pushdown, column pruning, and partitioning strategies.
The topics covered in this chapter are as follows:
- Understanding the
collect()method - Understanding the use of
inferSchema - Learning to differentiate between CSV and Parquet
- Learning to differentiate between...
