
This course includes our updated coding exercises so you can practice your skills as you learn.
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Begin this Databricks Certified Data Analyst Associate exam prep course with hands-on Databricks activities, data uploads, and SQL in the Lakehouse, plus accessible resources.
Receive a clear disclaimer that this independent course is not affiliated with Databricks, contains no actual exam questions, and uses original practice questions for educational preparation.
Set up a Databricks account to explore a unified data intelligence platform for big data analytics, AI workflows, and SQL analytics across AWS, Azure, and Google Cloud.
Explore how Partner Connect enables Databricks to integrate with external tools such as Fivetran, Tableau, Power BI, and DBT Cloud via secure marketplace connections.
Create and manage a demo sales database by building a demo products table, creating a view, renaming tables with alter, and dropping tables, views, and the database.
Explore how persistent views store reusable queries in the meta store and how temporary views exist only for the current session, guiding when to use each in Databricks.
Explore data with Data Explorer in Databricks and secure it by managing permissions and privileges, granting or revoking access, and reviewing metadata, history, lineage, and data quality.
Databricks table owners control access, define schema, manage metadata, and document data while restricting PII access and applying compliance policies.
Learn SQL, the standard language for managing relational databases, and practice defining structures with create, alter, drop, and manipulate data with select, insert, update, and delete.
Practice writing select queries with where clauses to filter data using and, or, in, not in, and comparison operators like greater than or not equal to in Databricks sql editor.
Discover how subqueries simplify sql queries by nesting one query inside another, producing a temporary result set used by the outer query to filter results, without temporary views.
Leverage query history and in-memory caching in Databricks to reduce development time and speed up repeated SQL queries.
Create basic, schema specific visualizations in Databricks SQL, build dashboards, secure data access, and visualize sales by country and shipping carrier to tell a story.
Explore visualization types in Databricks SQL, from area and pie charts to combo, counter, heat map, histogram, and box plot, learning how each shows trends, distributions, and comparisons.
Create a counter with a target by summing total sales. Apply conditional formatting to show green on target and red below, with dollar formatting.
Rename the page to sales overview and apply blue tones for a tidy, cohesive dashboard. Add interactive filters for country and order date, align charts, and introduce a markdown title.
Variance measures how far data points deviate from the mean and is expressed in squared units, revealing data dispersion and risk; standard deviation is its square root.
Explore how frequency distributions organize data, compare ungrouped and grouped approaches, and use intervals and bar charts to reveal patterns and insights in datasets.
Load a new date table, join it with sales data, and create weekday features to analyze Christmas period sales, then group by weekday name and sum sales.
Data blending combines data from two or more sources to create a unified dataset for analysis. Explore use cases like blending CRM with external data and dashboards.
Explore the Databricks marketplace as an app store for data and AI assets. Access ready-to-use data sets, pre-built models, notebooks, and solution accelerators from public and private sources.
Explore materialized views and dynamic views in Databricks, comparing precomputed, cached results versus real-time query execution. Apply row level security and column level security to control data access.
Add descriptions and AI-generated comments to tables in Genomespace and Unity Catalog, edit descriptions within Genie space, run benchmarks against ground truth SQL, and review results before sharing spaces.
Learn to manage data access with sql by creating groups, granting select and modify privileges (including insert, update, delete) to engineers, and revoking permissions on the customers table.
Understand how the vacuum command cleans up old delta files in Databricks, preserving time travel within the retention window and how to extend it from seven to thirty days.
Explore two practice test modes, including 45 questions with explanations in practice mode, or take the timed final exam (90 minutes, 70% passing) to prepare for the real test.
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