Data Engineering for Beginners: Learn SQL, Python & Spark

<b> <!--StartFragment--><p><strong>Why Learn Data Engineering?</strong></p><p>Data Engineering is one of the fastest-growing fields in the tech industry. Organizations of all sizes rely on&nbsp;<strong>Data Engineers</strong>&nbsp;to build and maintain the infrastructure that powers&nbsp;<strong>big data analytics, reporting, and machine learning</strong>. Data Engineers design, implement, and optimize&nbsp;<strong>data pipelines</strong>&nbsp;to efficiently process and manage data for&nbsp;<strong>business intelligence, real-time analytics, and AI applications</strong>.</p><p>With&nbsp;<strong>SQL, Python, and Apache Spark</strong>, Data Engineers can handle large-scale data processing efficiently. These skills are highly sought after in&nbsp;<strong>finance, healthcare, e-commerce, and every data-driven industry</strong>.</p><p>If you are looking for an&nbsp;<strong>industry-relevant</strong>&nbsp;and&nbsp;<strong>practical</strong>&nbsp;course that teaches you how to work with&nbsp;<strong>SQL, Python, Apache Spark (PySpark), and Databricks on Google Cloud Platform (GCP)</strong>, this course is the perfect place to start.</p><p><strong>What You Will Learn in This Course</strong></p><p>This course is designed to take you from a&nbsp;<strong>beginner to an intermediate level</strong>&nbsp;in Data Engineering. You will gain&nbsp;<strong>hands-on experience</strong>&nbsp;working with&nbsp;<strong>SQL, Python, Apache Spark (PySpark), and Databricks</strong>&nbsp;by building real-world&nbsp;<strong>batch and streaming data pipelines</strong>.</p><p><strong>SQL for Data Engineering (PostgreSQL)</strong></p><ul><li><p>Install and configure&nbsp;<strong>PostgreSQL</strong>&nbsp;to practice SQL queries</p></li><li><p>Learn fundamental&nbsp;<strong>SQL concepts</strong>&nbsp;such as&nbsp;<strong>SELECT, WHERE, JOIN, GROUP BY, HAVING, and ORDER BY</strong></p></li><li><p>Perform&nbsp;<strong>advanced SQL operations</strong>&nbsp;including&nbsp;<strong>window functions, ranking, cumulative aggregations, and complex joins</strong></p></li><li><p>Learn how to&nbsp;<strong>optimize SQL queries</strong>&nbsp;for performance and debugging</p></li></ul><p><strong>Python for Data Engineering</strong></p><ul><li><p>Understand&nbsp;<strong>Python fundamentals</strong>&nbsp;for data processing</p></li><li><p>Work with&nbsp;<strong>Python Collections</strong>&nbsp;to efficiently process structured data</p></li><li><p>Use&nbsp;<strong>Pandas</strong>&nbsp;to manipulate, clean, and analyze data</p></li><li><p>Build&nbsp;<strong>real-world Python projects</strong>, including a&nbsp;<strong>File Format Converter</strong>&nbsp;and a&nbsp;<strong>Database Loader</strong></p></li><li><p>Learn how to&nbsp;<strong>troubleshoot and debug Python applications</strong></p></li><li><p>Understand&nbsp;<strong>performance tuning strategies</strong>&nbsp;for Python-based data pipelines</p></li></ul><p><strong>Apache Spark (PySpark) for Big Data Processing</strong></p><ul><li><p>Learn&nbsp;<strong>Spark SQL</strong>&nbsp;to process structured data at scale</p></li><li><p>Work with&nbsp;<strong>PySpark DataFrame APIs</strong>&nbsp;to manipulate big data</p></li><li><p>Create and manage&nbsp;<strong>Delta Tables</strong>&nbsp;and perform&nbsp;<strong>CRUD operations</strong>&nbsp;(INSERT, UPDATE, DELETE, MERGE)</p></li><li><p>Perform&nbsp;<strong>advanced SQL transformations</strong>&nbsp;using&nbsp;<strong>window functions, ranking, and aggregations</strong></p></li><li><p>Learn how to&nbsp;<strong>optimize PySpark jobs</strong>&nbsp;using&nbsp;<strong>Spark Catalyst Optimizer and Explain Plans</strong></p></li><li><p>Debug, monitor, and optimize&nbsp;<strong>Spark jobs using Spark UI</strong></p></li></ul><p><strong>Deploying Data Pipelines on Databricks (Google Cloud Platform - GCP)</strong></p><ul><li><p>Set up and configure&nbsp;<strong>Databricks on Google Cloud Platform (GCP)</strong></p></li><li><p>Learn how to&nbsp;<strong>provision and manage Databricks clusters</strong></p></li><li><p>Develop&nbsp;<strong>PySpark applications</strong>&nbsp;on Databricks and execute jobs on multi-node clusters</p></li><li><p>Understand the&nbsp;<strong>cost, scalability, and benefits of using Databricks for Data Engineering</strong></p></li></ul><p><strong>Performance Tuning and Optimization in Data Engineering</strong></p><ul><li><p>Learn&nbsp;<strong>query performance optimization techniques</strong>&nbsp;in&nbsp;<strong>SQL and PySpark</strong></p></li><li><p>Implement&nbsp;<strong>partitioning and columnar storage formats</strong>&nbsp;to improve efficiency</p></li><li><p>Explore&nbsp;<strong>debugging techniques</strong>&nbsp;for troubleshooting&nbsp;<strong>SQL and PySpark applications</strong></p></li><li><p>Analyze&nbsp;<strong>Spark execution plans</strong>&nbsp;to improve job execution performance</p></li></ul><p><strong>Common Challenges in Learning Data Engineering and How This Course Helps</strong></p><p>Many learners struggle with setting up a proper&nbsp;<strong>Data Engineering environment</strong>, finding structured learning material, and gaining&nbsp;<strong>hands-on experience</strong>&nbsp;with real-world projects.</p><p>This course&nbsp;<strong>eliminates these challenges</strong>&nbsp;by providing:</p><ul><li><p><strong>A step-by-step guide</strong>&nbsp;to setting up PostgreSQL, Python, and Apache Spark</p></li><li><p><strong>Hands-on exercises</strong>&nbsp;that simulate real-world Data Engineering problems</p></li><li><p><strong>Practical projects</strong>&nbsp;that reinforce learning and build confidence</p></li><li><p><strong>Cloud-based Data Engineering with Databricks on Google Cloud</strong>, making it easier to work with large-scale data</p></li></ul><p><strong>Who Should Take This Course?</strong></p><p>This course is designed for:</p><ul><li><p><strong>Beginners</strong>&nbsp;who want to start a career in Data Engineering</p></li><li><p><strong>Aspiring Data Engineers</strong>&nbsp;who want to learn&nbsp;<strong>SQL, Python, Apache Spark (PySpark), and Databricks</strong></p></li><li><p><strong>Software Developers and Data Analysts</strong>&nbsp;who want to transition into Data Engineering</p></li><li><p><strong>Data Science and Machine Learning Practitioners</strong>&nbsp;who need a deeper understanding of&nbsp;<strong>data pipelines</strong></p></li><li><p><strong>Anyone interested in Big Data, ETL processes, and cloud-based Data Engineering</strong></p></li></ul><p><strong>Why Take This Course?</strong></p><p><strong>Beginner-Friendly Approach</strong></p><p>This course starts with the&nbsp;<strong>fundamentals</strong>&nbsp;and gradually builds up to&nbsp;<strong>advanced topics</strong>, making it&nbsp;<strong>accessible for beginners</strong>.</p><p><strong>Hands-On Learning with Real-World Projects</strong></p><p>You will work on&nbsp;<strong>real-world projects</strong>&nbsp;to&nbsp;<strong>reinforce your skills</strong>&nbsp;and&nbsp;<strong>gain practical experience</strong>&nbsp;in building Data Pipelines.</p><p><strong>Cloud-Based Training on Databricks (GCP)</strong></p><p>This course teaches&nbsp;<strong>cloud-based Data Engineering</strong>&nbsp;using&nbsp;<strong>Databricks on Google Cloud</strong>, a platform widely used by companies for&nbsp;<strong>Big Data processing and machine learning</strong>.</p><p><strong>Comprehensive Curriculum Covering All Key Data Engineering Skills</strong></p><p>This course covers&nbsp;<strong>SQL, Python, Apache Spark (PySpark), Databricks, ETL, Big Data Processing, and Performance Optimization</strong>—all essential skills for a&nbsp;<strong>Data Engineer</strong>.</p><p><strong>Performance Tuning and Debugging</strong></p><p>You will learn how to&nbsp;<strong>analyze Spark execution plans, optimize SQL queries, and debug PySpark jobs</strong>, which are crucial for&nbsp;<strong>real-world Data Engineering projects</strong>.</p><p><strong>Lifetime Access and Updates</strong></p><p>You get&nbsp;<strong>lifetime access</strong>&nbsp;to the course content, which is regularly updated to&nbsp;<strong>keep up with industry trends and new technologies</strong>.</p><p><strong>Course Features</strong></p><ul><li><p><strong>Step-by-step instructions</strong>&nbsp;with detailed explanations</p></li><li><p><strong>Hands-on exercises</strong>&nbsp;to reinforce learning</p></li><li><p><strong>Real-world projects</strong>&nbsp;covering&nbsp;<strong>batch and streaming data pipelines</strong></p></li><li><p><strong>Complete Databricks setup guide</strong>&nbsp;for Google Cloud</p></li><li><p><strong>Performance optimization techniques</strong>&nbsp;for SQL and PySpark</p></li><li><p><strong>Best practices for debugging and tuning Spark jobs</strong></p></li></ul><p><strong>Enroll Today and Start Your Data Engineering Journey</strong></p><p>If you are serious about learning&nbsp;<strong>Data Engineering</strong>&nbsp;and want to&nbsp;<strong>master SQL, Python, Apache Spark (PySpark), and Databricks on Google Cloud</strong>, this course will provide you with the&nbsp;<strong>essential skills and hands-on experience</strong>&nbsp;needed to succeed in this field.</p><p><strong>Take the first step in your Data Engineering journey today—enroll now!</strong></p><!--EndFragment--></b>

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.