Course details

Streaming Data Architectures: An Introduction to Streaming Data

Streaming Data Architectures: An Introduction to Streaming Data


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Spark, an analytics engine built on Hadoop, can be used for working with big data, data science, and processing batch and streaming data. Explore the fundamentals of working with streams using Spark in this 9-video course. Key concepts covered here include the differences between batch and streaming data and the types of streaming data sources; processing streaming data, transformation of streams, and materialization of the results of the transformation; and how use of a message transport decouples a streaming application from the sources of streaming data. Next, learn about techniques used in Spark 1.x to work with streaming data and how it contrasts with processing batch data; how structured streaming in Spark 2.x is able to ease the task of stream processing for the app developer; and how streaming processing works in both Spark 1.x and 2.x. Finally, learn how triggers can be set up to periodically process streaming data and the various output modes available to publish the results of stream processing; and the key aspects of working with structured streaming in Spark.



Expected Duration (hours)
0.9

Lesson 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
  • Course Number:
    it_dssdardj_01_enus

    Expertise Level
    Intermediate