• Online, Self-Paced
Course Description

Spark is an analytics engine built on Hadoop that can be used for working with big data, data science and processing batch and streaming data. In this course you will explore the fundamentals of working with streams using Spark.

Learning Objectives

Streaming Data Architectures: An Introduction to Streaming Data

  • Course Overview
  • recognize the differences between batch and streaming data and the types of streaming data sources
  • list the steps in involved in processing streaming data, the transformation of streams, and the materialization of the results of the transformation
  • describe how the use of a message transport decouples a streaming application from the sources of streaming data
  • describe the techniques used in Spark 1.x to work with streaming data and how it contrasts with processing batch data
  • recall how structured streaming in Spark 2.x is able to ease the task of stream processing for the app developer
  • compare how streaming processing works in both Spark 1.x and 2.x
  • recognize how triggers can be set up to periodically process streaming data and describe the various output modes available to publish the results of stream processing
  • recognize the key aspects of working with structured streaming in Spark

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Data Administration
  • Systems Architecture