Data Engineering for Beginners: Learn SQL, Python & Spark | Udemy

Why Learn Data Engineering?

Data Engineering is one of the fastest-growing fields in the tech industry. Organizations of all sizes rely on Data Engineers to build and maintain the infrastructure that powers big data analytics, reporting, and machine learning. Data Engineers design, implement, and optimize data pipelines to efficiently process and manage data for business intelligence, real-time analytics, and AI applications.

With SQL, Python, and Apache Spark, Data Engineers can handle large-scale data processing efficiently. These skills are highly sought after in finance, healthcare, e-commerce, and every data-driven industry.

If you are looking for an industry-relevant and practical course that teaches you how to work with SQL, Python, Apache Spark (PySpark), and Databricks on Google Cloud Platform (GCP), this course is the perfect place to start.

What You Will Learn in This Course

This course is designed to take you from a beginner to an intermediate level in Data Engineering. You will gain hands-on experience working with SQL, Python, Apache Spark (PySpark), and Databricks by building real-world batch and streaming data pipelines.

SQL for Data Engineering (PostgreSQL)

• Install and configure PostgreSQL to practice SQL queries
• Learn fundamental SQL concepts such as SELECT, WHERE, JOIN, GROUP BY, HAVING, and ORDER BY
• Perform advanced SQL operations including window functions, ranking, cumulative aggregations, and complex joins
• Learn how to optimize SQL queries for performance and debugging

Python for Data Engineering

• Understand Python fundamentals for data processing
• Work with Python Collections to efficiently process structured data
• Use Pandas to manipulate, clean, and analyze data
• Build real-world Python projects, including a File Format Converter and a Database Loader
• Learn how to troubleshoot and debug Python applications
• Understand performance tuning strategies for Python-based data pipelines

Apache Spark (PySpark) for Big Data Processing

• Learn Spark SQL to process structured data at scale
• Work with PySpark DataFrame APIs to manipulate big data
• Create and manage Delta Tables and perform CRUD operations (INSERT, UPDATE, DELETE, MERGE)
• Perform advanced SQL transformations using window functions, ranking, and aggregations
• Learn how to optimize PySpark jobs using Spark Catalyst Optimizer and Explain Plans
• Debug, monitor, and optimize Spark jobs using Spark UI

Deploying Data Pipelines on Databricks (Google Cloud Platform – GCP)

• Set up and configure Databricks on Google Cloud Platform (GCP)
• Learn how to provision and manage Databricks clusters
• Develop PySpark applications on Databricks and execute jobs on multi-node clusters
• Understand the cost, scalability, and benefits of using Databricks for Data Engineering

Performance Tuning and Optimization in Data Engineering

• Learn query performance optimization techniques in SQL and PySpark
• Implement partitioning and columnar storage formats to improve efficiency
• Explore debugging techniques for troubleshooting SQL and PySpark applications
• Analyze Spark execution plans to improve job execution performance

Common Challenges in Learning Data Engineering and How This Course Helps

Many learners struggle with setting up a proper Data Engineering environment, finding structured learning material, and gaining hands-on experience with real-world projects.

This course eliminates these challenges by providing:

• A step-by-step guide to setting up PostgreSQL, Python, and Apache Spark
• Hands-on exercises that simulate real-world Data Engineering problems
• Practical projects that reinforce learning and build confidence
• Cloud-based Data Engineering with Databricks on Google Cloud, making it easier to work with large-scale data

Who Should Take This Course?

This course is designed for:

• Beginners who want to start a career in Data Engineering
• Aspiring Data Engineers who want to learn SQL, Python, Apache Spark (PySpark), and Databricks
• Software Developers and Data Analysts who want to transition into Data Engineering
• Data Science and Machine Learning Practitioners who need a deeper understanding of data pipelines
• Anyone interested in Big Data, ETL processes, and cloud-based Data Engineering

Why Take This Course?

Beginner-Friendly Approach

This course starts with the fundamentals and gradually builds up to advanced topics, making it accessible for beginners.

Hands-On Learning with Real-World Projects

You will work on real-world projects to reinforce your skills and gain practical experience in building Data Pipelines.

Cloud-Based Training on Databricks (GCP)

This course teaches cloud-based Data Engineering using Databricks on Google Cloud, a platform widely used by companies for Big Data processing and machine learning.

Comprehensive Curriculum Covering All Key Data Engineering Skills

This course covers SQL, Python, Apache Spark (PySpark), Databricks, ETL, Big Data Processing, and Performance Optimization—all essential skills for a Data Engineer.

Performance Tuning and Debugging

You will learn how to analyze Spark execution plans, optimize SQL queries, and debug PySpark jobs, which are crucial for real-world Data Engineering projects.

Lifetime Access and Updates

You get lifetime access to the course content, which is regularly updated to keep up with industry trends and new technologies.

Course Features

• Step-by-step instructions with detailed explanations
• Hands-on exercises to reinforce learning
• Real-world projects covering batch and streaming data pipelines
• Complete Databricks setup guide for Google Cloud
• Performance optimization techniques for SQL and PySpark
• Best practices for debugging and tuning Spark jobs

Enroll Today and Start Your Data Engineering Journey

If you are serious about learning Data Engineering and want to master SQL, Python, Apache Spark (PySpark), and Databricks on Google Cloud, this course will provide you with the essential skills and hands-on experience needed to succeed in this field.

Take the first step in your Data Engineering journey today—enroll now!