Apache Spark with Java – Hands On!

Apache Spark with Java – Hands On!
Apache Spark with Java – Hands On!
English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 7 Hours | 7.94 GB

Learn how to slice and dice data using the next generation big data platform – Apache Spark!

Apache Spark is the next generation batch and stream processing engine. It’s been proven to be almost 100 times faster than Hadoop and much much easier to develop distributed big data applications with. It’s demand has sky rocketed in recent years and having this technology on your resume is truly a game changer. Over 3000 companies are using Spark in production right now and the list is growing very quickly! Some of the big names include: Oracle, Hortonworks, Cisco, Verizon, Visa, Microsoft, Amazon as well as most of the big world banks and financial institutions!

In this course you’ll learn everything you need to know about using Apache Spark in your organization while using their latest and greatest Java Datasets API. Below are some of the things you’ll learn:

  • How to develop Spark Java Applications using Spark SQL Dataframes
  • Understand how the Spark Standalone cluster works behind the scenes
  • How to use various transformations to slice and dice your data in Spark Java
  • How to marshall/unmarshall Java domain objects (pojos) while working with Spark Datasets
  • Master joins, filters, aggregations and ingest data of various sizes and file formats (txt, csv, Json etc.)
  • Analyze over 18 million real-world comments on Reddit to find the most trending words used
  • Develop programs using Spark Streaming for streaming stock market index files
  • Stream network sockets and messages queued on a Kafka cluster
  • Learn how to develop the most popular machine learning algorithms using Spark MLlib
  • Covers the most popular algorithms: Linear Regression, Logistic Regression and K-Means Clustering

You’ll be developing over 15 practical Spark Java applications crunching through real world data and slicing and dicing it in various ways using several data transformation techniques. This course is especially important for people who would like to be hired as a java developer or data engineer because Spark is a hugely sought after skill. We’ll even go over how to setup a live cluster and configure Spark Jobs to run on the cloud. You’ll also learn about the practical implications of performance tuning and scaling out a cluster to work with big data so you’ll definitely be learning a ton in this course.

What you’ll learn

  • Utilize the most powerful big data batch and stream processing engine to solve big data problems
  • Master the new Spark Java Datasets API to slice and dice big data in an efficient manner
  • Build, deploy and run Spark jobs on the cloud and bench mark performance on various hardware configurations
  • Optimize spark clusters to work on big data efficiently and understand performance tuning
  • Transform structured and semi-structured data using Spark SQL, Dataframes and Datasets
  • Implement popular Machine Learning algorithms in Spark such as Linear Regression, Logistic Regression, and K-Means Clustering
Table of Contents

1 Why Spark
2 Spark High Level Components
3 Creating a Spark Maven Project
4 Import Source Code into Eclipse
5 First Spark Application
6 Spark Standalone Cluster Architecture

Spark Java Dataset API Basics
7 Ingesting CSV and JSON Files
8 How to reduce logging in the console
9 Real World Dataframes Example
10 Union Dataframes and Other Set Transformations
11 Converting Between Datasets and Dataframes

Diving Deeper with Datasets, Dataframes, Transformations and the DAG
12 Map and Reduce Transformation Functions
13 Using Datasets with User Defined POJOs
14 Using Datasets with Unstructured Textual Data
15 Joining Dataframes and Using Various Filter Transformations
16 Aggregation Transformations + Join Assignment
17 More on Transformations, Actions and the DAG

Running Spark Jobs on the Cloud
18 Using Spark to Analyze Reddit Comments
19 Running the Reddit Spark Application on an EMR Cluster
20 Instructions for Configuring a Spark Stand-alone Cluster

Spark Streaming Applications
21 Streaming Network Socket Example
22 Stock Market Files Streaming Example
23 Using Kafka with Spark Streaming

Machine Learning with Spark MLlib
24 Machine Learning Resources
25 Overview of Linear Regression
26 Spark Java Linear Regression Example
27 Overview of Logistic Regression
28 Spark Java Logistic Regression (Classification Algorithm)
29 Overview of K-Means Clustering
30 Spark Java K-Means Clustering Example
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