tarball.tar.gz

DataFrameBasicsRun

SparkDataframeExampleDB

This course serves as a comprehensive introduction to the Spark Java API. Experienced Java developers will use object-oriented programming (OOP) principles to apply theory to Apache Spark and big data practice.

You’ll learn the basic components and architecture of Spark, a leading framework for building big data applications, before implementing them in Java. You’ll also explore data transformations like grouping, sorting, and joining. Further, you’ll learn to support SQL operations in the database and create a big data and batch template application with Java.

By the end of the course, you’ll be familiar with Apache Spark and know how to integrate big data with Java environments through the Spark Java API. You’ll wrap up by learning about monitoring and support functions for a live Spark Java environment.

Combining a leading big data framework with a leading programming language, this course will empower you to work efficiently with large volumes of data, and process at scale and speed.

Mastering Big Data with Apache Spark and Java

## Adding design into the mix

Documentation tends to be a reference, manual, and reminder of an application’s design to consult back and forth. It can also serve as a fundamental blueprint for a project being built, illustrating the landscape and interaction between its parts or components.

As we enrich our application in this lesson by adding a Database into the mix and applying a transformation to the data through the DataFrame API, it is helpful to illustrate how the big picture looks for our application’s changes and, more importantly, how Spark fits into it.

During the duration of this course, we’ll try to enforce this practice, especially when our main batch application increases in its complexity.

### The application's model

Because our application is not complex, the following diagram mixes both architectural and flow perspectives into one:




# Adding design into the mix

Documentation tends to be a reference, manual, and reminder of an application’s design to consult back and forth. It can also serve as a fundamental blueprint for a project being built, illustrating the landscape and interaction between its parts or components.

As we enrich our application in this lesson by adding a Database into the mix and applying a transformation to the data through the DataFrame API, it is helpful to illustrate how the big picture looks for our application’s changes and, more importantly, how Spark fits into it.

During the duration of this course, we’ll try to enforce this practice, especially when our main batch application increases in its complexity.

## The application's model

Because our application is not complex, the following diagram mixes both architectural and flow perspectives into one:




Let’s add a DB and produce more meaningful information using the DataFrame API.

Course Introduction

Spark Introduction and Basics

Getting Started with Spark

DataFrame Basic Operations

DataFrame Advanced Operations

Spark SQL and Other Functionalities

Building a Big Data Batch Application

Deployment and Cluster Execution

Monitoring and Performance Fundamentals

Conclusion

Apendix

Enriching the Basic DataFrame Program

Adding design into the mix

The application’s model