Big Data Processing with Apache Spark / Galeano, Manuel.

Format:

Video

Author/Creator:

Galeano, Manuel, author.

Narang, Nimish, author.

Language:

English

Subjects (All):

Big data.

Python (Computer program language).

Application program interfaces (Computer software).

Cloud computing.

Electronic data processing.

Spark (Electronic resource : Apache Software Foundation).

Genre:

Electronic videos.

Physical Description:

1 online resource (1 video file, approximately 3 hr., 30 min.)

Edition:

1st edition

Other Title:

Sub-title on title screen: Efficiently tackle large datasets and perform big data analysis with Spark and Python

Place of Publication:

Packt Publishing, 2019.

System Details:

video file

Summary:

Efficiently tackle large data sets and big data analysis challenges using Spark and Python About This Video This course will allow the learner to: Get up and running with Apache Spark and Python Integrate Spark with AWS for real-time analytics Apply processed data streams to machine learning APIs of Apache Spark In Detail Processing big data in real time is challenging due to scalability, information consistency, and fault-tolerance. Big Data Processing with Apache Spark teaches you how to use Spark to make your overall analytical workflow faster and more efficient. You'll explore all core concepts and tools within the Spark ecosystem, such as Spark Streaming, the Spark Streaming API, machine learning extension, and structured streaming. You'll begin by learning data processing fundamentals using Resilient Distributed Datasets (RDDs), SQL, Datasets, and Dataframes APIs. After grasping these fundamentals, you'll move on to using Spark Streaming APIs to consume data in real time from TCP sockets, and integrate Amazon Web Services (AWS) for stream consumption. By the end of this course, you'll not only have understood how to use machine learning extensions and structured streams but you'll also be able to apply Spark in your own upcoming big data projects.

Participant:

Presenter, Nimish Narang.

Notes:

Online resource; Title from title screen (viewed January 29, 2019)

Title from resource description page (Safari, viewed March 15, 2019).

OCLC:

1090069452