In this module, you will explore the Snowflake platform, gaining insights into its architecture and key concepts. Through hands-on practice in the Snowflake Web UI, you'll learn to create tables, manage warehouses, and use the Snowflake Python Connector to interact with tables. By the end of this week, you'll solidify your understanding of Snowflake's architecture and practical applications, emerging with the ability to effectively navigate and leverage the platform for data management and analysis.

In this module, you will practice the essential skills for seamlessly managing machine learning workflows using Databricks and MLFlow. First, you will create a Databricks workspace and configure a cluster, setting the stage for efficient data analysis. Next, you will load a sample dataset into the Databricks workspace using the power of PySpark, enabling data manipulation and exploration. Finally, you will install MLFlow either locally or within the Databricks environment, gaining the ability to orchestrate the entire machine learning lifecycle. By the end of this week, you will be able to craft, track, and manage machine learning experiments within Databricks, ensuring precision, reproducibility, and optimal decision-making throughout your data-driven journey.

In this module, you will explore the concepts of Kaizen, DevOps, and DataOps and how these methodologies synergistically contribute to efficient and seamless data engineering workflows. Through practical examples, you will learn how Kaizen's continuous improvement philosophy, DevOps' collaborative practices, and DataOps' focus on data quality and integration converge to enhance the development, deployment, and management of data engineering platforms. By the end of this week, you will have the knowledge and perspective needed to optimize data engineering processes and deliver scalable, reliable, and high-quality solutions.