What you'll learn:
- Spark Scala industry standard coding practices - Logging, Exception Handling, Reading from Configuration File
- Unit Testing Spark Scala using JUnit , ScalaTest, FlatSpec & Assertion
- Building a data pipeline using Hive, Spark and PostgreSQL
- Spark Scala development with Intellij, Maven
- Cloudera QuickStart VM setup on GCP
This course bridges the gap between academic learning and real-world application, preparing you for an entry-level Big Data Spark Scala Developer role. You'll gain hands-on experience with industry best practices, essential tools, and frameworks used in Spark development.
What You’ll Learn:
Spark Scala Coding Best Practices – Write clean, efficient, and maintainable code
Logging – Implement logging using Log4j and SLF4J for debugging and monitoring
Exception Handling – Learn best practices to handle errors and ensure application stability
Configuration Management – Use Typesafe Config for managing application settings
Development Setup – Work with IntelliJ and Maven for efficient Spark development
Local Hadoop Hive Environment – Simulate a real-world big data setup on your machine
PostgreSQL Integration – Read and write data to a PostgreSQL database using Spark
Unit Testing – Test Spark Scala applications using JUnit, ScalaTest, FlatSpec & Assertions
Building Data Pipelines – Integrate Hadoop, Spark, and PostgreSQL for end-to-end workflows
Bonus – Set up Cloudera QuickStart VM on Google Cloud Platform (GCP) for hands-on practice
Prerequisites:
Basic programming knowledge
Familiarity with databases
Introductory knowledge of Big Data & Spark
This course provides practical, hands-on training to help you build and deploy real-world Spark Scala applications. By the end of this course, you’ll have the confidence and skills to build, test, and deploy Spark Scala applications in a real-world big data environment.
This course uses high-quality AI-generated text-to-speech narration to complement the powerful visuals and enhance your learning experience.
