Introduction to Apache Spark

Video description

Get up to speed on Apache Spark for building big data applications in Python, Java, or Scala. Recently updated with nearly an hour of new footage on DataFrames in Spark 1.3, this video workshop shows you how to explore data and apply algorithms with MLlib, GraphX, and Spark SQL. You’ll learn Spark and its core APIs by doing hands-on technical exercises with presenter Paco Nathan, host of the popular Just Enough Math video workshop.

With this …

Introduction to Apache Spark

Video description

With this workshop, you will:

Get going with the newest features of Spark 1.3
Open a Spark shell
Develop Spark apps for typical use cases
Use some machine-learning algorithms
Explore data sets loaded from HDFS or another filesystem
Work with Spark SQL, Spark Streaming, and Spark’s machine-learning library, MLlib
Use Maven, SBT, IPython Notebook, and other tooling
Learn about Spark follow-up courses and certification

Paco Nathan has led innovative data teams building large-scale apps for several years. He’s an expert in distributed systems, machine learning, cloud computing, and functional programming.

Publisher resources

Download Example Code

Pre-Flight Check

Spark Deconstructed

A Brief History

Simple Spark Apps

Spark Essentials

Spark Examples

Unifying the Pieces - Spark SQL

Unifying the Pieces - Spark Streaming

Unifying the Pieces - MLlib and GraphX

Unified Workflows Demo

The Full SDLC

Developer Certification

Resources

Introduction - Why DataFrames?

ETL to Prepare the Data from Capital Bikeshare

Create a DataFrame, Explore using SQL

Data Preparation for Machine Learning Models

Build a Classifier Using Naive Bayes

Build a Classifier Using Decision Trees

Build a Classifier Using Random Forests

Use a DataFrame to Compare Models

Parquet as a Best Practice with DataFrames

How to Store a DataFrame with Parquet

How to Read a DataFrame Back in From Parquet

Use SQL to Estimate Route Durations

Data Preparation for GraphX - Model Route Costs

Use PageRank to Rank Popular Stations

Optimize Routes to Columbus Circle

Compare Results with Google Maps

Analyze a Popular Tourist Route

Examples of How to Use DataFrames in Python

Summary - The New DataFrames Features in Spark

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Self paced

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