Gain insights into Spark Java API, learn about data transformations and SQL operations, and discover how to integrate big data and Java for scalable, high-speed processing.

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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

## Ingestion of XML files

Extensible Markup Files, or XML files, are still broadly present in the realm of data formats.

These files are structured, extensible, self-describing (easy to read for us humans), and can be validated by using XSD files in conjunction with them.

> **Note:** For more information on the XML format, please refer to: https://www.w3.org/XML/

On the downside, they tend to be quite verbose, and sometimes, depending on the complexity of their structure, very hard to read. Nonetheless, this format is widely used, and Spark finds no impediments to parsing it for us.

The project for this lesson is quite similar to the previous lesson one:





# Ingestion of XML files

Extensible Markup Files, or XML files, are still broadly present in the realm of data formats.

These files are structured, extensible, self-describing (easy to read for us humans), and can be validated by using XSD files in conjunction with them.

> **Note:** For more information on the XML format, please refer to: https://www.w3.org/XML/

On the downside, they tend to be quite verbose, and sometimes, depending on the complexity of their structure, very hard to read. Nonetheless, this format is widely used, and Spark finds no impediments to parsing it for us.

The project for this lesson is quite similar to the previous lesson one:





Learn how to load different file formats with the Spark 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

How to Ingest Files: Part II

Ingestion of XML files