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

## Schemas

In previous lessons, we interchangeably used the terms “schema” and “structure” when learning how to manipulate a DataFrame, an abstraction with a well-defined representation composed of columns and rows.

But there is a clear and substantial difference to make now that we are using the SQL syntax in Spark in conjunction with the DataFrame API:


___
A **schema** in Spark defines the number and type of columns that a DataFrame comprises, and it should not be confused with an RMDBS schema, which most of the time refers to a grouping of tables as the logical construct for a data model.
___

Every time a DataFrame is created—from a file, from another DataFrame as a result of transformations applied, and so on—a schema is attached to it, or rather, it specifies its representational structure. This schema can also be created and manipulated programmatically, the latter of which we learned in previous lessons.

Let’s learn how to create a schema and a DataFrame from a collection of elements in our Java program by simply writing code.


### Working with schemas programmatically

For this lesson, the codebase looks like this:




# Schemas

In previous lessons, we interchangeably used the terms “schema” and “structure” when learning how to manipulate a DataFrame, an abstraction with a well-defined representation composed of columns and rows.

But there is a clear and substantial difference to make now that we are using the SQL syntax in Spark in conjunction with the DataFrame API:


___
A **schema** in Spark defines the number and type of columns that a DataFrame comprises, and it should not be confused with an RMDBS schema, which most of the time refers to a grouping of tables as the logical construct for a data model.
___

Every time a DataFrame is created—from a file, from another DataFrame as a result of transformations applied, and so on—a schema is attached to it, or rather, it specifies its representational structure. This schema can also be created and manipulated programmatically, the latter of which we learned in previous lessons.

Let’s learn how to create a schema and a DataFrame from a collection of elements in our Java program by simply writing code.


## Working with schemas programmatically

For this lesson, the codebase looks like this:




Let’s focus on expanding the concept and use cases for the schema of a DataFrame.

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

Schema Manipulation

Schemas

Working with schemas programmatically