Delta Lake with Apache Spark using Scala

About Delta Lake with Apache Spark using Scala

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.