Engineering Data Ecosystems: Pipelines, ETL, Spark

This course is designed to provide you with a foundational understanding of how modern data ecosystems work. From data pipelines to ETL processes, and big data handling using Apache Spark, you’ll explore the essential tools, techniques, and technologies that drive decision-making in today’s data-driven world. Whether you’re an aspiring data engineer or someone interested in the mechanics of data handling, this course will lay the groundwork for your journey into the exciting field of data engineering. This course is ideal for aspiring data engineers, software developers, database administrators, and IT professionals looking to expand their skills in data handling and processing. Additionally, analysts and business professionals interested in data technologies will find the course beneficial for enhancing their understanding of the fundamental processes behind data ecosystems and big data. Participants should have a general interest in data and a basic understanding of programming concepts. Familiarity with database systems will be helpful, but prior experience with Spark is not required. An interest in big data and data analytics will enrich your learning experience throughout the course. By the end of this course, participants will be able to identify the components and importance of data ecosystems, understand the structure and function of data pipelines, and recognize the critical steps involved in ETL workflows. Additionally, you'll gain introductory knowledge of big data handling with Apache Spark and its applications in large-scale data processing.

• Engineering Data Ecosystems: Pipelines, ETL, Spark
• This course serves as an introductory course aimed at unraveling the complexities of data ecosystems. It's tailored for individuals at the onset of their data engineering journey, emphasizing the construction, management, and optimization of data pipelines, the essentials of ETL (Extract, Transform, Load) workflows, and an introduction to big data processing with Apache Spark.