In this session, you'll learn about big data.

Get an introduction to Scala, a modern programming language. Learn its syntax, functional programming features, and compatibility with Java and big data frameworks like Spark.

In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK.

This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow.

In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance.

Learn how to streamline your data pipelines using Apache Kafka. Explore its capabilities for real-time data streaming and integration with big data systems.

Get started with Apache Solr, a scalable search engine. Learn how to index, search, and analyze data for enterprise-level search applications.

Gain expertise in using Apache Spark for large-scale data processing and analytics.

Dive into Apache Spark basics. Learn how this distributed computing framework handles big data processing efficiently, with an introduction to its key concepts and operations.

Build on your knowledge of Apache Spark with advanced topics. Explore Spark's powerful APIs for handling complex data processing tasks in distributed environments.

Explore Spark MLlib, the machine learning library for Apache Spark. Learn how to build and deploy scalable machine learning models in distributed environments.

Learn how to use Apache Spark with Scala for big data analytics. Explore Spark's architecture, APIs, and practical applications in distributed data processing.