What you'll learn:
- Setup Environment to learn SQL and Python essentials for Data Engineering
- Database Essentials for Data Engineering using Postgres such as creating tables, indexes, running SQL Queries, using important pre-defined functions, etc.
- Data Engineering Programming Essentials using Python such as basic programming constructs, collections, Pandas, Database Programming, etc.
- Data Engineering using Spark Dataframe APIs (PySpark) using Databricks. Learn all important Spark Data Frame APIs such as select, filter, groupBy, orderBy, etc.
- Data Engineering using Spark SQL (PySpark and Spark SQL). Learn how to write high quality Spark SQL queries using SELECT, WHERE, GROUP BY, ORDER BY, ETC.
- Relevance of Spark Metastore and integration of Dataframes and Spark SQL
- Ability to build Data Engineering Pipelines using Spark leveraging Python as Programming Language
- Use of different file formats such as Parquet, JSON, CSV etc in building Data Engineering Pipelines
- Setup Hadoop and Spark Cluster on GCP using Dataproc
- Understanding Complete Spark Application Development Life Cycle to build Spark Applications using Pyspark. Review the applications using Spark UI.
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!
