This module will guide you through setting up and navigating your Databricks environment. You'll learn about cloud service providers, create your Databricks account, and understand key features such as Unity Catalog, workspaces, and the Databricks account console to get you started.

In this section, you'll dive into importing data into Databricks, focusing on practical techniques for uploading files, using Delta Lake for data storage, and sharing data. You will also explore managed and external tables and how to perform data ingestion from Amazon S3.

This section focuses on mastering SQL in Databricks. You will learn the essentials of sorting, filtering, and grouping data, and dive into advanced SQL techniques like joins, subqueries, and window functions. Additionally, you will learn how to handle NULL values and conditional logic.

In this module, we will explore the different types of views in Databricks and their respective use cases. You will learn to create temporary, materialized, and dynamic views to manage your data and ensure efficient querying.

In this module, you will explore how AI can aid in SQL development within Databricks. Learn how to use AI to write, debug, and optimize queries, and discover how AI can streamline your workflow for better results and improved productivity.

This module focuses on using Unity Catalog to manage and secure your data in Databricks. You’ll learn about the tools for setting up workspace permissions, applying data tags, and ensuring data governance and compliance.

In this module, you will explore how to create and manage dashboards in Databricks. Learn how to design datasets, choose the right visualization types, and enhance your dashboards with features like filters, parameters, and auto-refresh functionality.

This section introduces you to AI/BI Genie spaces within Databricks. You will learn how to create, share, and optimize these spaces for better collaboration, as well as monitor their performance to enhance analytics and reporting.

In this module, you will learn about various data modeling techniques, including Star and Snowflake schemas, as well as the Medallion Architecture. You will also gain hands-on experience building streaming tables and implementing last-mile ETL processes in Databricks SQL.

In this module, we will cover techniques for analyzing and optimizing SQL queries in Databricks. You’ll explore Photon for query acceleration, learn query profiling, and optimize your workflows with Liquid Clustering and VACUUM.

In this final module, we will recap the key concepts and techniques covered throughout the course. You will have a solid understanding of how to use Databricks for data analytics, and we will provide advice on preparing for the Databricks Certified Data Analyst Associate exam and continuing your learning journey.