Databricks Certified Data Analyst Associate Exam Prep

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

12 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

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Overview

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This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Prepare for the Databricks Certified Data Analyst Associate Exam with this comprehensive course. You'll master essential tools like Delta Lake, Unity Catalog, and Databricks SQL, while learning data management, SQL querying, and advanced techniques like AI-enhanced dashboards and data modeling. The course guides you from the basics of Databricks, data import, and analysis to more complex concepts like Medallion Architecture and Data Vault Modeling. You'll gain hands-on experience in building dashboards, optimizing queries, and securing data. The curriculum also covers managing data with Unity Catalog, designing visualizations, and analyzing query performance. Ideal for anyone looking to become a certified Databricks Data Analyst, this course will ensure you're well-prepared for both the exam and real-world data analysis. By the end, you'll be able to analyze data in Databricks, develop optimized queries, create dynamic dashboards, and apply advanced data modeling techniques. You'll also be fully prepared for the exam.

Syllabus

Course Introduction

In this module, you will get an overview of Databricks and its role in modern data ecosystems. You will explore the differences between data warehouses, data lakes, and the emerging data lakehouse model, with an introduction to Delta Lake. This will set the foundation for the rest of the course.

Getting Started with Databricks

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.

Importing Data

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.

Master SQL in Databricks

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.

Views in Databricks

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.

Modern SQL Development with AI in Databricks

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.

Managing & Securing Data (Unity Catalog)

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.

Working with Dashboards and Visualizations in Databricks

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.

Developing, Sharing, and Maintaining AI/BI Genie spaces

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.

Data Modeling with Databricks SQL

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.

Analyzing Queries

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.

Summary

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.