Course Introduction
Databricks Data Analytics Fundamentals Training Course provides a comprehensive introduction to modern data analytics using the Databricks Data Intelligence Platform. The course develops practical understanding of how analysts can discover, query, transform, visualize, and communicate insights from enterprise data while working within a lakehouse architecture. Participants gain a structured foundation in analytics concepts, Databricks SQL, notebooks, dashboards, data modeling, and governed data access.
The course introduces the Databricks lakehouse architecture and explains how it combines scalable data-lake storage with data-warehouse-style analytics. Participants explore how data can be stored and analyzed without creating unnecessary isolated systems for different workloads. The training also introduces the medallion architecture and explains how data can progressively move through staging and refinement layers to support trustworthy analytical use.
Participants develop practical skills using Databricks SQL, including writing queries, filtering and aggregating data, joining datasets, creating reusable analytical logic, and investigating query results. Databricks SQL provides a SQL editor, SQL warehouses, dashboards, alerts, jobs, and other capabilities for analytics and reporting. Participants also learn how SQL can work alongside notebooks and other Databricks tools to support broader analytical workflows.
The training emphasizes exploratory data analysis and business insight generation. Participants learn how to use notebooks to query and visualize data, investigate trends, identify relationships, and communicate findings. They also explore AI/BI dashboards, which provide interactive visualization capabilities and AI-assisted authoring for turning datasets and queries into shareable analytical reports.
Data governance and trustworthy analytics are important components of the course. Participants are introduced to Unity Catalog, Databricks' unified governance layer for data and AI, and examine concepts such as access control, data discovery, lineage, auditing, and data classification. They also learn why governed data definitions and lineage are important when multiple teams rely on shared analytical datasets and business metrics.
The course also incorporates emerging analytics capabilities, including AI-assisted SQL development, AI/BI dashboards, Genie Agents, semantic and metric modeling, natural-language data exploration, automated alerts, and intelligent analytics. Participants learn how these technologies can complement established analytical practices while considering data quality, governance, interpretation, and responsible use. By the end of the course, learners will have a strong foundation for performing practical analytics and progressing into more advanced Databricks data, engineering, and AI capabilities.
Duration
5 days
Who Should Attend
-
Data analysts seeking a structured foundation in Databricks analytics, SQL, visualization, and business intelligence.
-
Business intelligence professionals transitioning to Databricks-based lakehouse analytics and reporting environments.
-
SQL developers who want to apply their existing querying skills within Databricks SQL and lakehouse architectures.
-
Power BI and BI specialists interested in expanding their knowledge of Databricks data and analytics capabilities.
-
Data engineers who need a stronger understanding of how analysts consume and interpret engineered datasets.
-
Data scientists seeking practical skills in exploratory analysis, visualization, SQL, and governed data discovery.
-
Business users and decision-makers who want to understand modern self-service analytics and AI-assisted data exploration.
-
Reporting specialists responsible for creating dashboards, reports, metrics, and recurring analytical insights.
-
Database professionals moving from traditional data warehouses toward modern cloud lakehouse environments.
-
Cloud and technology professionals seeking foundational knowledge of Databricks analytics and its integrated data platform.
-
Consultants implementing Databricks analytics solutions and supporting organizations with data-driven transformation initiatives.
-
Project managers and analytics leaders overseeing adoption of modern data platforms and enterprise business intelligence solutions.
Course Objectives
-
Explain the Databricks lakehouse architecture and describe how scalable data storage, processing, SQL analytics, and business intelligence work together.
-
Navigate the Databricks workspace and identify the primary interfaces used for SQL querying, notebooks, dashboards, data discovery, jobs, and analytics.
-
Write effective Databricks SQL queries using filtering, aggregation, joins, sorting, conditional logic, and other fundamental analytical techniques.
-
Use SQL warehouses appropriately for interactive analytics while understanding compute concepts, query execution, resource utilization, and analytical workloads.
-
Perform exploratory data analysis using Databricks notebooks, SQL, Python, visualizations, and structured analytical techniques to investigate business questions.
-
Build interactive AI/BI dashboards that transform datasets and analytical queries into useful visualizations, filters, metrics, and shareable business insights.
-
Apply data modeling principles to organize datasets, dimensions, measures, relationships, and reusable business logic for consistent analytical results.
-
Understand Unity Catalog governance concepts including data discovery, permissions, lineage, auditing, classification, and responsible access to analytical assets.
-
Apply analytical performance and data-quality practices that improve query efficiency, reliability, consistency, interpretability, and user experience across Databricks workloads.
-
Evaluate emerging AI/BI capabilities including Genie Agents, AI-assisted authoring, semantic metrics, natural-language analytics, and intelligent data exploration.
Comprehensive Course Outline
Module 1: Databricks Analytics Foundations
-
Understanding Databricks as a unified Data + AI Platform and examining its role in modern analytics, business intelligence, data engineering, and AI workloads.
-
Exploring the lakehouse architecture and understanding how data-lake scalability and warehouse-style analytics can support integrated enterprise data environments.
-
Navigating the Databricks workspace and identifying core interfaces for SQL, notebooks, dashboards, data discovery, jobs, and analytical development.
-
Examining common analytics use cases and identifying how Databricks can support operational reporting, exploratory analysis, business intelligence, and advanced analytics.
Module 2: Lakehouse Data and Analytics Architecture
-
Understanding lakehouse concepts and how organizations can maintain scalable analytical data without creating isolated systems for different analytical workloads.
-
Exploring medallion architecture and understanding how data can be progressively refined through staging, transformation, validation, and business-ready layers.
-
Working with tables, views, files, schemas, catalogs, and other data objects used to organize analytical information within Databricks.
-
Comparing traditional data warehouses, data lakes, and lakehouse architectures based on scalability, analytical flexibility, governance, performance, and business requirements.
Module 3: Databricks SQL Fundamentals
-
Writing SQL queries to retrieve, filter, sort, aggregate, and analyze business information stored within Databricks data environments.
-
Applying joins, subqueries, common table expressions, conditional expressions, date functions, string functions, and analytical calculations to real-world datasets.
-
Understanding SQL warehouses and how they provide compute resources for interactive querying, analytics, dashboards, and automated SQL workloads.
-
Using query history, query profiles, and performance information to understand execution behavior and identify opportunities for analytical improvement.
Module 4: Data Exploration and Analytical Techniques
-
Conducting exploratory data analysis to understand distributions, trends, relationships, anomalies, missing values, and other important characteristics.
-
Using Databricks notebooks to combine SQL and programming languages such as Python for flexible exploration, transformation, analysis, and visualization.
-
Developing analytical questions and selecting suitable calculations, aggregations, comparisons, and visualizations to investigate business performance.
-
Applying structured analytical thinking to move from raw observations toward evidence-based insights that can support operational and strategic decisions.
Module 5: Data Preparation and Transformation
-
Understanding how analytical datasets are prepared through cleansing, transformation, filtering, aggregation, enrichment, and validation processes.
-
Using SQL and notebook-based techniques to handle missing values, inconsistent records, duplicate information, incorrect formats, and analytical data-quality issues.
-
Exploring Delta Lake and lakehouse data-management concepts that support reliable and scalable analytical datasets within Databricks.
-
Designing reusable data-preparation workflows that provide analysts with consistent, well-structured, trustworthy, and appropriately documented information.
Module 6: Data Modeling and Business Metrics
-
Understanding dimensions, measures, relationships, calculated fields, and other modeling concepts used to transform raw datasets into business-oriented analytical structures.
-
Creating reusable analytical logic that supports consistent calculations and reduces differences in how teams interpret important organizational metrics.
-
Exploring metric views and semantic modeling approaches for defining governed business metrics that can be reused across queries and dashboards.
-
Applying appropriate modeling strategies to support multi-dimensional analysis, cross-filtering, aggregation, performance, and consistent business reporting.
Module 7: AI/BI Dashboards and Visualization
-
Creating AI/BI dashboards that transform datasets and SQL queries into interactive visualizations designed for recurring business analysis and reporting.
-
Applying charts, tables, filters, layouts, calculations, and interactive controls to communicate analytical findings clearly to different audiences.
-
Using AI-assisted dashboard authoring to accelerate visualization development while reviewing generated outputs for accuracy, relevance, and business context.
-
Sharing published dashboards and enabling users to explore analytical information through interactive and increasingly conversational business intelligence experiences.
Module 8: Governance with Unity Catalog
-
Understanding Unity Catalog as the unified governance layer for Databricks data and AI assets, including access control, discovery, lineage, and auditing.
-
Exploring catalogs, schemas, tables, views, permissions, and other governed objects used to organize and control enterprise analytical information.
-
Using data lineage to understand upstream sources, transformations, downstream dashboards, jobs, notebooks, and dependencies across analytical workflows.
-
Applying governance principles to improve data trust, security, discoverability, accountability, compliance, and responsible self-service analytics.
Module 9: Analytics Performance, Alerts, and Automation
-
Understanding factors that influence analytical performance, including query design, data structures, compute resources, joins, filtering, aggregation, and workload characteristics.
-
Creating alerts that monitor query results and identify conditions requiring attention, notification, investigation, or business intervention.
-
Using scheduled jobs and automated SQL workflows to support recurring reporting, data processing, monitoring, and analytical activities.
-
Applying practical optimization approaches that balance analytical responsiveness, resource consumption, reliability, maintainability, and business requirements.
Module 10: Emerging AI/BI and Advanced Analytics
-
Exploring Databricks AI/BI and understanding how AI-powered analytics can combine dashboards, governed semantics, natural-language interaction, and organizational data context.
-
Working with Genie Agents and conversational analytics concepts that enable users to ask questions about business data using natural language.
-
Examining AI-assisted authoring and analytical development while emphasizing validation, governance, data quality, transparency, and appropriate human oversight.
-
Developing an end-to-end analytics solution that integrates governed data, SQL analysis, exploratory workflows, semantic metrics, dashboards, alerts, and emerging AI capabilities.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
