Data Engineering with Apache Spark in MS Azure

This course is designed for individuals with prior data experience, such as Data Analysts, Business Analysts, ETL Developers, and aspiring Data Engineers, who want to explore the "backend" side of data in an MS Azure environment using Apache Spark (PySpark). Learn how to generate, collect, and organize data, and if you're new to data handling, start with the Data Engineer Fundamentals course to master SQL and Python basics.

This course provides in-depth knowledge of data engineering, focusing on Microsoft Azure, Apache Spark (PySpark), data modeling, and SQL. You will learn how to design and manage efficient data processing pipelines, utilize Azure cloud services like Synapse, Databricks, and DevOps for data optimization, and apply best practices in data modeling. You will also master Apache Spark and PySpark, gaining expertise in distributed data processing and optimization techniques. The course explores key data architectures such as Data Warehouses, Data Lakes, and Data Marts, and covers advanced SQL techniques for efficient data management.
Throughout the course, you will gain hands-on experience by solving real-world data processing tasks, which will deepen your understanding and provide valuable material for your portfolio. This training will equip you with the practical skills needed to help companies efficiently manage, process, and analyze large data volumes, preparing you for the growing demand for data engineering expertise.

• Building and managing efficient data pipelines
• Using Azure services like Synapse, Databricks, and DevOps for data storage and optimization
• Mastering Apache Spark and PySpark for distributed data processing and optimization
• Exploring data architectures such as Data Warehouses, Data Lakes, and Data Marts
• Learning advanced SQL techniques for efficient data management
• Gaining hands-on experience through real-world projects

• The course will be held in English as well as all projects and questions will be submitted in English.
• E-learning materials, self-paced: Access interactive digital materials and guided coding videos to study at your own pace, with one year of rewatching available.
• Learn-by-doing approach, weekly schedule: Apply your knowledge through weekly practice exercises, requiring 8-12 hours of study each week.
• Constant mentoring, live sessions: Receive feedback on projects, ask questions anytime, and join live sessions for personalized support.
• Exam, certificate: Complete an exam and/or hand in your final project at the end of the course to earn a certificate for your CV and LinkedIn profile.