Databricks Certified Data Analyst Associate Exam Preparation

Explore the roles of data engineer, data scientist, and data analyst, and follow the data workflow from collection and cleaning to analysis, visualization, and reporting using SQL databases.

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.

Identify the primary audiences for Databricks SQL—data analysts, sql specialists, and business analysts. Note side audiences such as data scientists and data engineers; highlight benefits: unified environment, auto scaling.

Create bar visualizations of fare amount and trip distance by week from the query, then save, rename, and add them to a dashboard named fare and distance totals by week.

Learn to use the Databricks SQL editor to write queries, explore catalogs, schemas, and tables, run and limit results, view sample data, and inspect permissions and data details.

Explain how Databricks SQL endpoints provide dedicated compute for SQL queries and dashboards, with serverless warehouses offering near instant start and zero management, balancing cluster size and cost.

Explore how Partner Connect enables Databricks to integrate with external tools such as Fivetran, Tableau, Power BI, and DBT Cloud via secure marketplace connections.

Upload small files to a Databricks SQL table using add data, drag and drop, or browse, supporting CSV, JSON, Avro, Parquet, and more, then set catalog, schema, and table name.

Explore the medallion architecture in Databricks—bronze raw landing, silver cleaned, and gold analytics-ready layers—organized via catalogs for trusted analytics and dashboards.

Explore how to load streaming and batch data together in Databricks with the Lakehouse architecture, enabling real-time insights and a unified analytics environment.

Delta Lake enhances data lakes with reliability, performance, and governance through ACID transactions, schema enforcement and evolution, time travel, upserts, and deletes within a medallion architecture.

Explore Delta Lake's transaction log to manage table metadata, enabling auditing, change tracking, and time travel to query historical data states.

Discover how Delta Lake delivers reliability with acid transactions and ensures atomicity, consistency, isolation, and durability, while boosting performance through metadata optimization and data compaction in lakehouse architecture.

Explore how managed and unmanaged Databricks tables differ in data and metadata handling, storage locations, and drop effects, and how the location clause creates external tables.

Learn to create, use, and drop databases, tables, and views in Databricks using SQL. Manage warehouses and schemas, and switch between databases with use for query execution.

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.

Aggregate data in sql with avg, sum, max, and min to analyze data grouped by a column and return concise totals.

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.

Explore how SQL joins combine data from multiple tables using a common key, and compare inner, left, and right joins with practical examples from website sales and products tables.

Explore ANSI SQL as the standardized language used in Databricks Lakehouse to write queries, enabling collaboration across platforms, then apply case statement for data transformation and categorization with conditional logic.

Learn how the SQL like operator uses wildcard characters % and _ to filter and categorize data, with case statements and not like in Databricks SQL.

Explore rollup and cube in sql to perform multidimensional aggregations, generating subtotals and grand totals. Compare how rollup highlights date-level subtotals while cube yields all possible groupings and cross tabulations.

Explore SQL window functions for data partitioning, using row_number over partition by sales order number order by product key to reset per order, and sum over for running totals.

Explore handling nested data formats in Databricks SQL using JSON value, JSON query, and explode to normalize arrays like skills and extract fields such as name, age, and city.

Explore merge into for upserts in Databricks, updating matched records and inserting new ones from a source table into a target delta table, and copy into load files from storage.

Enhance performance by using higher order Spark SQL functions to manipulate arrays and maps in queries, applying transform, filter, aggregate, and sort to compute a 10% discount and total sales.

Leverage query history and in-memory caching in Databricks to reduce development time and speed up repeated SQL queries.

Create and use a user defined function in Databricks SQL to sum sales amount, freight, and tax, demonstrated on website sales data.

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.

Create and manage dashboards in Databricks, add a pie chart visualization from multiple data sources, join sales with products via SQL, and apply filters to refine the view.

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 pivot table dashboards in Databricks SQL to compare shipping carriers by country and count unique sales orders, and explore two-page dashboards with scatter plots, sankey diagrams, and point maps.

Format data visuals to improve readability and insight by applying best practices for pie, bar, line, and histogram charts. Limit pie slices to 4–6, use labels, colors, and gridlines.

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.

Add a parameter to dashboards to enable interactive filtering by shipping carrier through the underlying SQL query, updating visuals like the donut chart.

Publish dashboards with embedded credentials, manage access and sharing, set refresh schedules and alerts, and securely distribute to groups while considering costs.

Explore descriptive statistics as fundamental tools to summarize data and guide analysis, focusing on mean, median, and mode, and understanding the impact of outliers and data spread.

Measure the variability of a data set by calculating the range—the difference between the highest and lowest values—revealing a gap of 53 in student scores.

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.

Explore the two main variable types—quantitative and qualitative—covering discrete and continuous measures. Identify nominal and ordinal categories with department, performance rating, work experience, salary, and weekly hours.

Enhance data through feature engineering and data enrichment to unlock richer insights, reduce time to insights, and create reusable assets for advanced modeling across retail, healthcare, and finance.

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.

Databricks certified data analyst associate exam preparation highlights last mile etl as the final stage, applying business logic, time-based calculations, segmentation, and flags to tailor data for reporting and analysis.

Discover how to use the Databricks AI assistant to enhance SQL queries, generate table descriptions, and improve dashboard visuals, including filtering and formatting.

Explore dimensional modeling for data warehouses by organizing data into fact and dimension tables, enabling fast querying and aggregations with subject and teacher details.

Explore the star schema, a data warehousing design with a fact table linked to dimension tables. See how analysts query enrollments by joining the fact to dimensions for descriptive details.

Compare snowflake and star schemas by normalizing dimension tables into subdimensions, using a fact table and foreign keys, and assess the trade-offs in joins, performance, and storage.

Explore data vault modeling with hubs, links, and satellites to track historical data and capture relationships between core business concepts like students, classes, and schools, including history and current tables.

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 query history and query profile to identify long-running queries, filter by user, date, and warehouse, and read how scans, shuffles, joins, and aggregation affect performance.

Cache tables in Databricks store query results in memory to speed up repeated queries, as shown by the NYC taxi trips example using cache table trips.

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.

Discover how liquid clustering optimizes delta tables to speed queries and cut storage costs by using smart metadata, clustering by age and product, and automatic clustering from workload history.

Explore ai/bi genie spaces in databricks to simplify ai-driven insights, enable natural language queries, and build interactive dashboards with delta lake, collaborative workspaces, and pre-built templates.

Create a genie space with all four tables, rename it retail analytics, and add sample questions like annual revenue and total sales by category, while validating table joins and instructions.

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 share a geni space in databricks using the share button to assign admins, groups, and users with manage, edit, run, or view permissions.

Demonstrate using the retail analytics genie to identify top cities, verify with pivot tables, visualize sales by state in bar and pie charts, and embed genie in dashboards via sql.

Explore Databricks data ingestion options, including loading from Amazon S3, delta sharing, APIs, and auto loader, with csv, parquet, json formats for batch or real-time streaming.

Create a streaming table in Databricks that updates in real time from a raw table, using a schedule and stream flag to add only new customers while avoiding duplicates.

Explore Delta sharing, a data sharing protocol across platforms that avoids copying data. Use Databricks to Databricks sharing or open sharing with tokens in Unity Catalog for live read-only access.

Learn to add, update, and remove tags on tables and schemas with SQL, including PII and owner metadata, for cost management and data governance.

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.

Discover lakehouse federation, which lets Databricks query external data sources via unity catalog without loading into Delta Lake, and join external and Databricks tables using standard sql.

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.