[MANNING] Spark in Motion [FCO] torrent

Author : Jason Kolter
Publisher : Manning
Course Duration : 2h 37m
Language : English
Exercises : 44
Torrent Contains : 37 Files
Course Source : https://www.manning.com/livevideo/spark-in-motion

Aabout the video

See it. Do it. Learn it! Spark in Motion teaches you to use Spark for big data analytics through high-quality video-based lessons and built-in exercises, so you can put what you learn into practice.

Spark in Motion teaches you how to use Spark for batch and streaming data analytics. In nearly 3 hours of hands-on video lessons, you'll get up and running with Spark, starting with the basic architecture of a Spark application. You'll explore data partitioning and accessing common application state, and then you'll deep-dive into using Spark SQL and dataframes for structured analytics. Finally, you'll use Spark Streaming to handle and process real-time data flowing into your application.

About the subject

When you're doing analytics on big data systems, it can be a challenge to efficiently query, stream, filter, and consolidate data sharded across a cluster. Built especially for efficiently operating over large distributed datasets, the Spark data processing engine takes some of the weight off your shoulders. Spark features an easy-to-use interface, near-limitless upgrade potential, and performance that will knock your socks off. Spark simplifies your data infrastructure so you can focus on creating top-notch analytics.

Prerequisites

Designed for a software engineer or architect, data scientist, or data analyst interested in getting started with Spark. No prior experience is needed.

Wwhat you will learn

• Exploring the Spark Ecosystem
• Deploying Spark on a cluster
• Analytics with SparkSQL
• Real-time applications with Spark Streaming

Aabout the instructor

Jason Kolter - is an instructor for the University of Washington certificate program in Big Data Technologies. Additionally he has worked in a wide range of technology companies, gaining extensive experience leading teams building production large-scale distributed analytics systems.

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