Develop a Data Pipeline in Python: A Comprehensive Course Enhance your skills in data engineering by mastering the creation of data pipelines using Python. This 4-hour course provides a comprehensive understanding of the essential concepts and tools utilized by data engineers to ensure seamless data availability and efficient deployment of machine learning models within organizations. To expedite the process of bringing processes into production and producing high-quality code, it is crucial to comprehend the fundamentals of data pipeline construction. Throughout this course, we delve into the various data pipelines constructed by data engineers and explore the tools they employ to facilitate the integration of models into production and the consistent execution of repetitive tasks. Utilize PySpark to Construct a Data Transformation Pipeline This course offers a comprehensive overview of the key components of data engineering pipelines. In Chapter 1, you will gain insights into data platforms and learn the process of data ingestion. Chapter 2 takes you a step further by focusing on data cleaning and transformation. You will harness the power of PySpark to construct a robust data transformation pipeline. In Chapter 3, you will acquire the skills to deploy code securely, examining different forms of testing. Finally, Chapter 4 equips you with the knowledge to schedule complex dependencies between applications. By leveraging the fundamentals of Apache Airflow, you will learn how to trigger various components of an ETL pipeline on a predetermined schedule and execute tasks in a specific order. Master Workflow Management and Orchestration Upon completion of this course, you will possess a comprehensive understanding of building data pipelines in Python for data engineering purposes. Additionally, you will acquire the expertise to orchestrate and manage your workflows effectively using DAG schedules and Apache Airflow, enabling automated testing. Enroll now and unlock the potential to streamline your data engineering processes and optimize the deployment of machine learning models.