Engineering Data Ecosystems: Pipelines, ETL, Spark
Master the fundamentals of modern data ecosystems: from data pipelines and ETL processes to big data handling with Apache Spark for effective data engineering.
Master the fundamentals of modern data ecosystems: from data pipelines and ETL processes to big data handling with Apache Spark for effective data engineering.
This foundational course explores the essential components of modern data ecosystems, focusing on data pipelines, ETL processes, and Apache Spark for big data handling. Designed for aspiring data engineers and IT professionals, it covers fundamental tools and technologies driving today's data-driven decision-making. Through comprehensive lessons on data pipeline construction, ETL workflow management, and Spark applications, participants gain practical knowledge in large-scale data processing.
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What you'll learn
Identify and describe the components and importance of data ecosystems
Understand the basic structure and function of data pipelines
Recognize the steps involved in ETL workflows
Gain introductory knowledge of big data processing
Master the fundamentals of Apache Spark applications
Implement scalable data solutions
Skills you'll gain
This course includes:
61 Minutes PreRecorded video
1 assignment, 2 discussion prompts
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FullTime access
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There is 1 module in this course
This comprehensive course introduces the fundamentals of data ecosystems and engineering. Students learn about the construction and management of data pipelines, the implementation of ETL (Extract, Transform, Load) workflows, and big data processing using Apache Spark. The curriculum covers essential aspects of data ecosystem design, tools and technologies for data pipeline management, and practical applications of Spark in large-scale data processing, providing a solid foundation for aspiring data engineers.
Engineering Data Ecosystems: Pipelines, ETL, Spark
Module 1 · 1 Hours to complete
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Instructor
Data Scientist and AI Research Scientist
Soheil Haddadi is a postdoctoral researcher specializing in Artificial Intelligence (AI) and machine learning, with a robust academic background in Control and Automation Systems Engineering. His expertise encompasses a wide range of areas, including data science, deep learning, natural language processing (NLP), and robotics. Soheil is committed to advancing the field of AI through innovative research and practical applications.He teaches several courses on Coursera, such as Fine-tuning Language Models for Business Tasks, GenAI for Data Scientists, and Data Engineering: Pipelines, ETL, Hadoop. These courses are designed to equip learners with the skills necessary to effectively utilize generative AI technologies and data engineering practices in various professional contexts.
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