Why this course?

Description

Delta Lake with Apache Spark using Scala – Hands-On Guide

Are you working with big data and struggling with data reliability, consistency, and performance? Do you want to master the technology that powers modern data lakes used by top companies worldwide?

Welcome to Delta Lake with Apache Spark using Scala, a hands-on, beginner-to-advanced course designed to help you understand, implement, and optimize Delta Lake for real-world big data projects.

Delta Lake is an open-source storage layer that brings ACID transactions, schema enforcement, and unified batch + streaming processing to Apache Spark and big data workloads. By the end of this course, you will be able to confidently build, manage, and optimize Delta Lake tables for enterprise-scale analytics.

What makes this course unique?

• Step-by-step, hands-on approach – learn by doing, not just theory.
• Covers both fundamentals and advanced concepts – from creating Delta tables to optimizing performance with file management and caching.
• Practical use cases & interview preparation – with dedicated FAQ lectures to strengthen your real-world knowledge.
• Up-to-date content – including Databricks free account setup (old & new), Spark cluster provisioning, and best practices.
• Built for Scala developers – get the real experience of working with Delta Lake using Apache Spark + Scala.

 

What’s inside the course?

Section 1: Introduction to Delta Lake & Spark

• Get started with Delta Lake, its key features, and the concept of Data Lakes.
• Learn the basics of Apache Spark, notebooks, and dataframes.
• Set up your Databricks free account and provision a Spark cluster.

Section 2: Hands-On with Delta Lake Tables

• Create, write, and read Delta tables.
• Perform schema validation and update schemas dynamically.
• Manage table metadata, updates, and deletions.
• Understand and use vacuuming, table history, and concurrency control.

Section 3: Delta Lake Performance Optimization

• Learn how to migrate workloads to Delta Lake.
• Optimize data storage with file management.
• Use Auto Optimize and caching techniques to boost performance.
• Explore isolation levels and concurrency handling in detail.

Section 4: Best Practices & Interview Prep

• Industry-proven best practices for working with Delta Lake.
• 15+ FAQ lectures covering interview-style questions on optimization, auto optimize, and advanced Delta Lake features.
• Practical tips to help you ace interviews and apply knowledge in real projects.

Section 5: Wrap Up & Bonus

• Important summary lecture consolidating key concepts.
• Bonus lecture with resources to continue your learning journey.

 

By the end of this course, you’ll be able to:

• Understand Delta Lake architecture and why it solves traditional data lake challenges.
• Implement ACID transactions and schema evolution with Delta Lake.
• Optimize Spark jobs with caching, auto optimize, and file management techniques.
• Manage and scale real-world data pipelines using Delta Lake.
• Confidently answer interview questions and apply best practices in your job or projects.

 

Why take this course?

 

This course is designed for:

• Beginners who want to get started with Delta Lake and Spark.
• Data Engineers, Developers, and Data Scientists who want to implement robust big data solutions.
• Students and professionals preparing for interviews in Big Data and Spark-based roles.
• Anyone who wants to gain hands-on skills in one of the fastest-growing big data technologies.