Learn the power of the Lakehouse In today's data-filled world, we need tools that allow us to be as data-driven as possible. This course guides you from start to finish on how the Databricks Lakehouse Platform provides a single, scalable, and performant platform for your data processes. Working through a real-world dataset will teach you how to accomplish various tasks within the Databricks platform. You'll start the course by learning how to administer the Databricks platform and ensuring your environment is set up securely.

Practice scalable data engineering After setting up your workspace, you will learn how to create powerful data pipelines using Databricks. You will apply different transformations to the dataset, moving it from Bronze to Silver and then Gold in a Medallion architecture. You will learn how Databricks clusters provide readily available compute power and scalability. You will set up an end-to-end Databricks Workflow to automate your entire data pipeline.

Use the Lakehouse as your data warehouse A key part of the Lakehouse architecture is that you can query your data storage like a traditional data warehouse. In this section, you will learn how Databricks SQL gives you the data warehousing performance you want on top of your data lake. You will learn how to create queries using standard ANSI SQL, and use those results to create ad-hoc dashboards against your entire dataset.

Implement governed data science and machine learning Finally, you will learn how Databricks provides a complete set of tools for data science and machine learning use cases. You will learn to track and evaluate your models using the fully integrated MLFlow framework for MLOps. You will learn how the Feature Store and Model Registry simplify the process of creating production-quality machine-learning models. Finally, you will learn how to deploy and monitor your models using built-in model serving capabilities.

Model training with MLFlow in Databricks

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