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

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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


## Logging and Spark

Whenever Spark is being used in a Java (or Spring) application, the logs produced by it are most likely a mix of the log lines included by the developers in the Java application, plus the logs produced by the Spark libraries themselves.


This separation occurs because Spark libraries work alongside logging libraries such as (and at least currently in version 3.X) `org.slf4j` and `log4j` logging frameworks (with the interface being the former, and the implementation the latter). We purposely excluded this library in the batch template Maven project within the `pom.xml` file to avoid compatibility issues with Spring Boot’s logging framework used:









# Logging and Spark

Whenever Spark is being used in a Java (or Spring) application, the logs produced by it are most likely a mix of the log lines included by the developers in the Java application, plus the logs produced by the Spark libraries themselves.


This separation occurs because Spark libraries work alongside logging libraries such as (and at least currently in version 3.X) `org.slf4j` and `log4j` logging frameworks (with the interface being the former, and the implementation the latter). We purposely excluded this library in the batch template Maven project within the `pom.xml` file to avoid compatibility issues with Spring Boot’s logging framework used:








Get introduced to Spark application logs and the key parts to understanding them.

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

Interpreting Spark Logs

Logging and Spark