Data Engineering for Beginners: Learn SQL, Python & Spark

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 CourseThis 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 queriesLearn fundamental SQL concepts such as SELECT, WHERE, JOIN, GROUP BY, HAVING, and ORDER BYPerform advanced SQL operations including window functions, ranking, cumulative aggregations, and complex joinsLearn how to optimize SQL queries for performance and debuggingPython for Data EngineeringUnderstand Python fundamentals for data processingWork with Python Collections to efficiently process structured dataUse Pandas to manipulate, clean, and analyze dataBuild real-world Python projects, including a File Format Converter and a Database LoaderLearn how to troubleshoot and debug Python applicationsUnderstand performance tuning strategies for Python-based data pipelinesApache Spark (PySpark) for Big Data ProcessingLearn Spark SQL to process structured data at scaleWork with PySpark DataFrame APIs to manipulate big dataCreate and manage Delta Tables and perform CRUD operations (INSERT, UPDATE, DELETE, MERGE)Perform advanced SQL transformations using window functions, ranking, and aggregationsLearn how to optimize PySpark jobs using Spark Catalyst Optimizer and Explain PlansDebug, monitor, and optimize Spark jobs using Spark UIDeploying 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 clustersDevelop PySpark applications on Databricks and execute jobs on multi-node clustersUnderstand the cost, scalability, and benefits of using Databricks for Data EngineeringPerformance Tuning and Optimization in Data EngineeringLearn query performance optimization techniques in SQL and PySparkImplement partitioning and columnar storage formats to improve efficiencyExplore debugging techniques for troubleshooting SQL and PySpark applicationsAnalyze Spark execution plans to improve job execution performanceCommon Challenges in Learning Data Engineering and How This Course HelpsMany 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 SparkHands-on exercises that simulate real-world Data Engineering problemsPractical projects that reinforce learning and build confidenceCloud-based Data Engineering with Databricks on Google Cloud, making it easier to work with large-scale dataWho Should Take This Course. This course is designed for:Beginners who want to start a career in Data EngineeringAspiring Data Engineers who want to learn SQL, Python, Apache Spark (PySpark), and DatabricksSoftware Developers and Data Analysts who want to transition into Data EngineeringData Science and Machine Learning Practitioners who need a deeper understanding of data pipelinesAnyone interested in Big Data, ETL processes, and cloud-based Data EngineeringWhy Take This Course.