Databricks for Modern Data Analytics + Certification Prep

Discover how Databricks runs on cloud service providers like AWS, Azure, or GCP, and how managed storage and compute in the workspace simplify processing and analytics without cloud setup.

The Databricks account acts as the cloud top-level container, managed by administrators, with workspaces and unity catalog governance, while users access data through catalog explorer and notebooks.

Unity Catalog provides centralized access control, auditing, and lineage across Databricks workspaces. It enables data discovery and documentation through a metastore, catalogs, schemas, and a three-level namespace.

Sign up for Databricks on a cloud-based platform with no local install, choosing between a free edition or a 14-day trial, and sign up with Google, Microsoft, or company email.

Discover how to navigate the Databricks account console, create and manage workspaces and metastores, attach a metastore to a workspace, and understand Unity Catalog’s three-level namespace.

Explore the Databricks workspace user interface, create notebooks and folders, switch cell languages, and navigate the Unity Catalog, SQL warehouse, marketplace, and dashboards to perform data analytics tasks.

Upload a file to create a customers table in a retail schema via the Databricks UI on free edition, using the workspace catalog and delta managed data.

Write basic SQL queries in Databricks using a serverless SQL warehouse, querying catalogs, schemas, and tables, applying where, in, and, or conditions, and limiting results.

Learn to upload local csv files to a unity catalog volume using the ui, including creating a managed volume, uploading products.csv and sales.csv, and managing permissions.

Create a delta table from a csv file stored in a Databricks volume by reading files with read_files and then creating or replacing the table using a defined schema.

Use the alter table command with rename to move a delta table from the default schema to the retail schema, refreshing the catalog to verify the update.

Load data into an existing delta table with the copy into command from a file location after creating empty table; it is idempotent and uses header and infer schema options.

Create a shipping delta table and insert data with insert into, specifying columns in order (shipping ID, ship mode, shipping cost, carrier), then refresh the catalog to verify four rows.

Explore Delta Lake and Delta Tables, and learn acid transactions, insert/update/delete/merge capabilities, time travel to previous versions, and schema enforcement and evolution for robust data analytics.

Compare managed and external tables in Unity catalog to see how Databricks controls storage lifecycle for managed tables while users manage external table storage and metadata.

Ingest data from an S3 bucket into Databricks by creating a cloud ingestion schema and external location, configuring the AWS stack, and creating a delta table with transaction logs.

Explore Delta sharing in Databricks to securely share data across organizations without duplication, using Unity Catalog and open sharing for both Databricks and non-Databricks recipients.

Explore the databricks marketplace, a data and assets platform powered by open source data sharing, enabling you to subscribe to datasets and assets and query tables under Unity Catalog governance.

Learn how to sort query results with the ORDER BY clause in SQL, including ascending and descending options, multi-column sorting, and the impact after filtering and aggregation.

Explore how aggregate functions in Databricks SQL compute counts, sums, averages, max, min, and approximations like approx count distinct with group by.

Learn to group data with group by in Databricks SQL and filter grouped results with having, using aggregates such as sum and average.

Learn to use string, date, and conversion functions in Databricks, including len, upper, lower, trim, substring, replace, concat, current date, difference between two dates, toDate, toTimestamp, cast, and try cast.

Discover how Databricks SQL joins combine data from multiple tables using common columns. Learn inner, left, right, full outer, semi, anti, and cross joins with practical syntax.

Explore set operators in databricks sql to merge rows from multiple queries. Learn union, unionall, intersect, and except, including duplicates handling and column compatibility rules.

Handle null values in Databricks SQL using coalesce to return the first non-null value, isnull to test for null, and ifnull to replace nulls with a backup expression.

Master conditional logic in Databricks SQL with case expressions, exploring simple and searched forms to derive stock status, category code, and price band.

Explore subqueries in Databricks SQL, where nested queries feed the main query in select, from, where, and having clauses, returning single values or sets like average and maximum.

Explore common table expressions (ctes) in databricks sql, creating temporary named result sets with with clauses that are reusable within a single query.

Explore window functions in Databricks SQL, learn how over clauses define windows, partition and order data, and compare aggregate, ranking, and value window functions with practical examples.

Standard views in Databricks SQL are virtual tables that store only the query definition and execute the underlying SQL each time, providing fresh data with low storage.

Create or replace a temporary view for short-lived queries inside a session. Temporary views mirror standard views but are not stored in the catalog, enabling exploratory data analysis and testing.

Learn how materialized views in Databricks, a Unity Catalog managed table, store pre-computed results, refresh on schedule or manually, and offer faster queries while managing storage and data staleness.

Dynamic views enforce row and column security and data masking based on user identity, using current_user and is_member to filter rows and reveal or hide columns.

Shows how Databricks AI writes and explains sql by anchoring catalog, schema, and tables, generating well-formatted queries and code explanations.

Learn to debug and optimize SQL queries with AI in the SQL editor, covering syntax and logical debugging, data quality checks, and performance tips like selective columns and efficient joins.

Explore best practices for AI and analyst workflow in Databricks, positioning AI as a copilot that accelerates SQL development while emphasizing human oversight and business logic validation.

Learn to convert sql queries into PySpark DataFrame syntax with ai assistance, bridging sql expertise to data engineering in Databricks, and explore formatting, explanation, and debugging steps.

Explore Unity Catalog fundamentals and centralized metadata governance across multiple workspaces, enabling data discovery, access control via catalog explorer, and a shared metastore for consistent analytics.

Discover how Unity Catalog binds a metastore to sales and marketing workspaces. Learn to manage users and permissions, and grant read access to catalogs like coffee sales.

Learn how to apply governed, system, and custom tags in Unity Catalog objects to improve data discovery and governance, using the UI and SQL code examples.

Learn to set, verify, and unset tags in Unity Catalog using SQL for schemas, tables, and columns, apply governed tags like ready for production, and discover tags with information schema.

Explore how data lineage in Unity Catalog traces data flow from sources through tables and views to downstream consumers, enabling impact analysis and understanding data origin and transformations.

Learn how Unity Catalog controls access to secureable objects—catalogs, schemas, tables, and more—through roles, privileges, and group-based sharing, guided by least-privilege best practices.

Explore how the AIBI dashboard in Databricks turns raw data into real-time visual insights with AI-assisted query and visualization creation, interactive filters, and shareable dashboards.

Design Databricks dashboards by building canvas visualizations, adding data via SQL or uploads, using widgets and filters, and publishing or sharing dashboards built from enrich_sales, customer_info, and sales_goal.

Explore creating visualizations in Databricks SQL dashboards, including bar, pie, line, and scatter charts, using axis options, aggregations, formatting, and tooltips.

Explore building and formatting diverse visualizations in Databricks SQL dashboards, including combo charts showing sales and profit by month, stacked bars, histograms, box plots, and pivot tables.

Learn to build and format counters in Databricks dashboards to display total profit, average discount, and sales versus targets, using conditional coloring, text boxes, and images.

Explore AI powered BI dashboards that generate SQL queries, run 2022 data, and create visualizations like bar charts, with AI to adjust fields, filters, legends, and page names.

Understand local and global filters on Databricks dashboards, where local filters apply to one page and global filters affect all pages from the same dataset, plus cross filtering updates charts.

Define and use parameters at the query level to drive dynamic filters in dashboards. Use string and numeric parameters to control region filters and top-n visuals without hard-coded values.

Learn how to publish and share Databricks dashboards securely by managing data permissions, editing drafts, and sharing with workspace or external users via links or embeds.

Schedule automatic dashboard refreshes in Databricks to update dashboards as the underlying sales data changes, using a frequency and timezone. The non-materialized enrich_sales view updates, and subscribers receive email notifications.

Create visualizations in notebooks and the SQL editor by configuring charts like bar and pie charts, adjusting axes, colors, and data labels, and add them to dashboards.

Learn how to create alerts in Databricks SQL to monitor a query result and trigger notifications when a threshold is met, with a total profit example and five-minute schedules.

Empower users to ask data questions in natural language and have Genie, an ai-powered chat, translate them into Databricks SQL, returning tables or visualizations for self-service analytics.

Create a Databricks genie space using the sales, products, customers, and shipping tables, enrich metadata with Unity Catalog, and configure joins and AI instructions for SQL queries.

Monitor genie spaces to capture user analytics questions and optimize responses with SQL queries, instructions, and filters, using benchmarks and Unity Catalog metadata to improve accuracy.

Assign role-based permissions to genie spaces, share with business users or external applications, and govern access from view to manage to ensure safe data exploration.

Explore industry standard data modeling techniques such as star, snowflake, and data vault schemas to optimize analytics performance, using fact and dimension tables to relate measures to descriptive attributes.

Explains how star schemas organize data around a central fact table with surrounding dimension tables linked by primary and foreign keys, and how snowflake schemas normalize dimensions into related tables.

Discover data vault modeling that overcomes star schema limits by using hubs, links, and satellites to enable historical tracking, schema evolution, and incremental loading.

Explore the medallion architecture for lakehouse data, moving from bronze raw ingestion to silver cleansing and gold analytics, with data vault, star, and snowflake schemas.

Explore streaming data in the medallion architecture with Databricks, building incremental pipelines across bronze, silver, and gold layers that process only new data for scalable analytics.

Explore the concept of last mile ETL within the gold layer of the data platform, enabling precomputed business metrics and dashboard-ready reporting tables.