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

In the Spark world, exporting information is simply the act of writing a DataFrame (or other abstractions) into a persistent DataSource, such as a database, or to a persistent storage abstraction, such as a file. In this lesson, we are going to learn how to do both.

There are many similarities between ingesting and exporting information, mostly in programmatic terms at the code level. This is no accident. The creators of the Spark API made the interfaces and methods somewhat similar by applying design patterns, thus providing the developer with an easy-to-remember API.


## Exporting to a database

We can pick up where we left off in the previous project because we have a database, Embedded DerbyDB, already bootstrapped to the application during its startup.

However, let’s inspect some new classes added to the project shown below:




In the Spark world, exporting information is simply the act of writing a DataFrame (or other abstractions) into a persistent DataSource, such as a database, or to a persistent storage abstraction, such as a file. In this lesson, we are going to learn how to do both.

There are many similarities between ingesting and exporting information, mostly in programmatic terms at the code level. This is no accident. The creators of the Spark API made the interfaces and methods somewhat similar by applying design patterns, thus providing the developer with an easy-to-remember API.


# Exporting to a database

We can pick up where we left off in the previous project because we have a database, Embedded DerbyDB, already bootstrapped to the application during its startup.

However, let’s inspect some new classes added to the project shown below:




Learn how to export DataFrames from Spark into different sources.

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

Exporting Information

Exporting to a database