Databricks SQL Analytics and Lakehouse Reporting Training Course

Course overview

Databricks SQL gives analytics teams a governed way to query lakehouse data, build shared dashboards, and distribute operational insight without copying data into separate reporting platforms. Yet many teams still rely on slow notebook queries, unmanaged extracts, inconsistent KPI definitions, and dashboards that expose more data than intended. This course addresses the practical work of turning Delta Lake data into reliable SQL analytics products: performant queries, controlled access, reusable datasets, scheduled refreshes, alerts, and decision-ready dashboards.

Participants learn to use Databricks SQL Warehouses, the SQL editor, query history, query profiles, dashboards, alerts, and scheduled jobs. They write and tune analytical SQL using common table expressions, window functions, joins, aggregations, parameters, and date logic; model reporting-ready data in Delta Lake; and apply Unity Catalog permissions, row filters, column masks, and lineage-aware governance. The course also covers connecting Databricks SQL to Power BI, selecting between live and import-style reporting patterns, and designing KPI definitions that remain consistent across teams.

Delivery combines instructor demonstrations with guided work in a Databricks workspace and a realistic sales, inventory, and customer-service reporting case. Each participant develops a governed reporting pack: documented SQL queries, a curated reporting dataset, a Databricks SQL dashboard with parameters and alerts, and an implementation plan for bringing the pattern into their own team. The final workshop requires participants to diagnose performance, validate metric logic, apply access controls, and present a dashboard designed for a business stakeholder.

The course is best suited to analysts, analytics engineers, BI developers, and data professionals who already work with SQL and need to make Databricks a dependable reporting and self-service analytics platform.

Course objectives

By the end of this course, participants will be able to:

• Configure Databricks SQL Warehouses for interactive analysis, dashboard workloads, and cost-aware query execution
• Write analytical SQL using CTEs, window functions, joins, conditional aggregation, and parameterised date logic
• Create Delta Lake reporting tables and views that provide stable, reusable datasets for dashboards
• Interpret query profiles and query history to identify scans, shuffles, skew, and inefficient SQL patterns
• Build Databricks SQL dashboards with visualisations, query parameters, drill-through links, schedules, and alerts
• Apply Unity Catalog grants, row filters, column masks, and object ownership to govern reporting access
• Connect Power BI to Databricks SQL and choose an appropriate model for governed dashboard consumption
• Produce a documented lakehouse reporting pack containing KPI definitions, SQL assets, access rules, and deployment actions

Benefits of attending

Target competencies

Who should attend

• Data Analysts — who need to turn lakehouse data into trusted recurring reports and dashboards
• BI Developers — who build semantic reporting assets and need a governed Databricks SQL delivery pattern
• Analytics Engineers — who prepare curated Delta tables and views for downstream analytical consumption
• Data Engineers — who support reporting workloads and must balance performance, cost, and access control
• Power BI Developers — who need to connect governed Databricks data to enterprise BI reports
• Data Platform Managers — who need to standardise self-service analytics on a lakehouse architecture

Requirements and prerequisites

Participants should be comfortable writing multi-table SQL queries, including SELECT statements, joins, GROUP BY, CASE expressions, and basic date functions. Experience using a relational database, data warehouse, BI tool, or Databricks workspace is useful. Participants should understand the distinction between tables, views, schemas, and dashboard metrics, and should be able to interpret business measures such as revenue, margin, or service levels. Prior Python, Spark programming, machine learning, and data engineering pipeline development are not required. A temporary Databricks training workspace and sample data are provided for practical work.

Training methodology

The instructor demonstrates each capability in a live Databricks workspace before participants complete structured SQL and dashboard tasks against a shared business dataset. Exercises progress from query authoring and Delta reporting views to query-profile diagnosis, Unity Catalog access design, and dashboard publication. Small-group reviews compare KPI definitions and reporting architecture choices for different stakeholder needs. Daily debriefs connect technical decisions to cost, security, and report reliability. On day five, participants assemble their reporting pack and create a practical adoption plan for a selected workplace reporting use case.

Course outline

Day 1: Databricks SQL foundations and analytical query design

• Lakehouse reporting architecture and the role of Databricks SQL
• Databricks SQL workspace navigation, SQL editor, catalog explorer, and query history
• SQL Warehouses, serverless options, warehouse sizing, and auto-stop controls
• Unity Catalog three-level namespace for reporting objects
• Analytical joins, aggregations, CASE logic, and null handling
• Common table expressions and reusable query structure
• Window functions for rankings, running totals, and period comparisons

Workshop: Build a sales-performance SQL query set that calculates regional revenue, margin, ranking, and month-on-month movement from governed Delta tables.

Day 2: Curated Delta datasets and reliable KPI logic

• Delta Lake tables, managed versus external tables, and reporting implications
• Creating reporting views and materialised views for business consumption
• Dimensional reporting concepts: facts, dimensions, grain, and conformed measures
• Data quality checks for duplicate records, late-arriving data, and invalid measures
• Date dimensions, fiscal calendars, and period-to-date calculations
• Parameterised SQL queries and dashboard filter design
• KPI definition documentation and metric reconciliation methods

Workshop: Create a curated customer and sales reporting layer with documented KPI definitions and reconciliation checks against source totals.

Day 3: Query performance, workload management, and cost control

• Databricks SQL query profiles and execution-plan interpretation
• Identifying full scans, shuffle operations, skew, and costly joins
• Predicate pushdown, column selection, partition pruning, and file skipping
• Optimising Delta tables with OPTIMIZE, ZORDER, and liquid clustering considerations
• SQL rewrite techniques for joins, aggregations, and window functions
• Query history analysis, query tags, and workload monitoring
• Warehouse concurrency, scaling behaviour, caching, and cost-management choices

Workshop: Diagnose and improve a slow executive-report query using its query profile, then record the performance changes and warehouse recommendation.

Day 4: Databricks SQL dashboards and governed business access

• Databricks SQL visualisations, dashboard layout, and stakeholder-oriented design
• Dashboard parameters, filter interactions, and drill-through navigation
• Schedules, subscriptions, alerts, and notification thresholds
• Dashboard sharing, permissions, and published asset management
• Unity Catalog grants, ownership, and least-privilege reporting access
• Row filters and column masks for restricted business data
• Data lineage, audit considerations, and reporting asset documentation

Workshop: Publish a role-specific operations dashboard with parameters, an exception alert, and row-level access rules for regional managers.

Day 5: Power BI integration and lakehouse reporting implementation

• Power BI connectivity patterns for Databricks SQL
• Import, DirectQuery, and composite-model considerations for lakehouse reporting
• Selecting reporting datasets, views, and SQL Warehouses for BI consumption
• Semantic-model alignment and preventing duplicated KPI logic
• Dashboard validation with business acceptance criteria
• Reporting release checklists, ownership, and change-control practices
• Lakehouse reporting roadmap and operating-model planning

Workshop: Complete and present a governed reporting pack comprising a Databricks SQL dashboard, Power BI connection design, KPI document, access model, and 90-day implementation plan.

Tools & standards covered

Databricks SQL, Unity Catalog, Delta Lake, Microsoft Power BI

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions