What you'll learn:
- Learn all the topics required for the Databricks Data Analyst Associate Exam
- Create interactive dashboards using Databricks SQL with multiple visualizations and query-based parameters.
- Manage and query Delta Lake tables, ensuring data consistency and maintaining historical data for time travel.
- Integrate Databricks SQL with BI tools like Power BI and Tableau for seamless data analysis and reporting.
- Apply data blending and enhancement techniques to combine and improve data from multiple sources for better insights.
- Write and execute complex SQL queries in Databricks SQL to process and manage data in a Lakehouse architecture.
This course is designed to prepare students for success in the Databricks SQL Certification exam and beyond, by providing hands-on training in querying, managing, and visualizing data on the Databricks Lakehouse Platform. Whether you're a data analyst, business intelligence professional, data engineer, or an aspiring analyst or practitioner in cloud data platforms, this course will help you get practice with Databricks SQL and prepare for the Databricks Certified Data Analyst Associate exam.
What You Will Learn:
Section 1: Databricks SQL Essentials
Understand the diverse user base and stakeholder roles in Databricks SQL.
Learn to write basic queries and build dashboards that deliver insights.
Learn how to connect Databricks SQL to tools like Tableau, Power BI, and ingestion platforms like Fivetran.
Explore SQL endpoints and warehouses, including cost-performance trade-offs and serverless options.
Understand the Medallion Architecture and its role in batch and streaming data workflows.
Section 2: Data Management with Delta Lake
Manage tables, views, and metadata with Delta Lake.
Understand managed vs. unmanaged tables and the implications of data persistence.
Work with Data Explorer to preview, secure, and modify data access.
Learn best practices for handling PII data in organizational settings.
Section 3: SQL in the Lakehouse
Practice advanced SQL operations: joins, merges, window functions, cubes, roll-ups, and subqueries.
Optimize query performance with caching, query history, and user-defined functions (UDFs).
Utilize query history and caching to reduce development time and query latency.
Section 4: Data Visualization and Dashboarding
Create visualizations directly within Databricks SQL.
Build interactive dashboards using query parameters and scheduling.
Learn techniques to improve storytelling through visuals and dashboard sharing best practices.
Configure alerts and notifications based on data conditions.
Section 5: Applied Analytics Applications
Apply statistical analysis using descriptive and inferential statistics.
Perform data blending and enhancement for actionable business insights.
Understand "last-mile" ETL techniques tailored to project-specific needs.
Section 6:Additional Topics for Updated September 2025 Exam Guide
Learn advanced data modeling techniques including star, snowflake, and data vault schemas.
Optimize query performance using query history, profiles, caching, and clustering.
Understand and apply materialized and dynamic views for efficient data access.
Explore the Databricks Marketplace to discover and use shared datasets and models.
Get introduced to AI/BI Genie Spaces for AI-powered analytics and collaboration.
Create, enhance, and share Genie Spaces with descriptions, benchmarks, and dashboards.
Who Should Take This Course:
Ideal for beginners or data professionals, business analysts, and technical stakeholders looking to strengthen their skills in Databricks SQL and prepare for the certification exam. No prior Databricks or SQL experience is required.
By the end of the course, you'll be able to:
- Confidently navigate the Databricks SQL environment
- Query, manage, and visualize Lakehouse data
- Apply statistical concepts in real-world analytics scenarios
- Prepare thoroughly for the Databricks SQL certification exam
