Course Details

Previous Page

Spark Core

Target Audience
Expected Duration
Lesson Objectives
Course Number

Spark Core provides basic I/O functionalities, distributed task dispatching, and scheduling. Resilient Distributed Datasets (RDDs) are logical collections of data partitioned across machines. RDDs can be created by referencing datasets in external storage systems, or by applying transformations on existing RDDs. In this course, you will learn how to improve Spark's performance and work with Data Frames and Spark SQL.

Target Audience
Programmers and developers familiar with Apache Spark who wish to expand their skill sets


Expected Duration (hours)

Lesson Objectives

Spark Core

  • start the course
  • recall what is included in the Spark Stack
  • define lazy evaluation as it relates to Spark
  • recall that RDD is an interface comprised of a set of partitions, list of dependencies, and functions to compute
  • pre-partition an RDD for performance
  • store RDDS in serialized form
  • perform numeric operations on RDDs
  • create custom accumulators
  • use broadcast functionality for optimization
  • pipe to external applications
  • adjust garbage collection settings
  • perform batch import on a Spark cluster
  • determine memory consumption
  • tune data structures to reduce memory consumption
  • use Spark's different shuffle operations to minimize memory usage of reduce tasks
  • set the levels of parallelism for each operation
  • create DataFrames
  • interoperate with RDDs
  • describe the generic load and save functions
  • read and write Parquet files
  • use JSON Dataset as a DataFrame
  • read and write data in Hive tables
  • read and write data using JDBC
  • run the Thrift JDBC/OCBC server
  • show the different ways to tune up Spark for better performance
  • Course Number: