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Databricks Fundamentals 2026: Hands-On Projects

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This course contains the use of artificial intelligence. Learn Databricks, SQL, PySpark, Delta Lake, Lakehouse Architecture, AI/BI, and Cloud Data Engineering Through Real-World ProjectsWant to learn Databricks and build the practical skills needed for modern Data Engineering, Big Data Analytics, and Cloud Data Platforms? This hands-on course takes you from complete beginner to building real-world Databricks Lakehouse solutions using SQL, Python, PySpark, Apache Spark, Delta Lake, and Unity Catalog.

Whether you're an aspiring Data Engineer, Data Analyst, Python Developer, or Cloud Engineer, you'll learn how organizations process, transform, govern, automate, and analyze massive datasets using the Databricks Data Intelligence Platform. And the best part? You'll start with Databricks Free Edition, making it easy to begin learning without setting up expensive cloud infrastructure.

What You'll LearnThis course goes beyond theory. You'll build practical projects while learning essential Databricks Data Engineering technologies. Databricks Fundamentals: Navigate the workspace, create notebooks, manage data, and understand the Databricks ecosystem.

Lakehouse Architecture: Explore Data Lakes, Data Warehouses, and Medallion Architecture with Bronze, Silver, and Gold layers. Databricks SQL: Write queries, perform joins, analyze business data, and create reusable SQL transformations. Apache Spark and PySpark: Process large datasets using Spark DataFrames, transformations, aggregations, and distributed computing.

Delta Lake: Build reliable Delta tables using ACID Transactions, MERGE, Time Travel, and incremental processing. Unity Catalog: Understand Data Governance, access controls, metadata management, and data lineage. Lakeflow: Build automated ETL Pipelines, schedule workflows, and monitor data processing.

Lakeflow Designer: Create visual, No-Code Data Pipelines using drag-and-drop transformations. Genie Code: Explore AI-Assisted Data Engineering, SQL generation, code debugging, and productivity improvements. Databricks AI/BI: Build interactive dashboards and explore Genie Agents for natural-language analytics.

Machine Learning and AI: Discover MLflow, foundation models, and introductory AI Agents on Databricks. AWS and Azure Databricks: Learn introductory cloud deployment concepts and practices. Learn by Building Real-World ProjectsYou'll work with a fictional retail company facing realistic challenges involving sales, customers, inventory, and business reporting.

Throughout the course, you'll build SQL Analytics Projects, develop PySpark ETL Pipelines, implement Delta Lake Medallion Architecture, automate workflows with Lakeflow, and create Business Intelligence Dashboards. Your final End-to-End Databricks Capstone Project brings everything together into a portfolio-ready Retail Intelligence Platform, complete with data ingestion, transformations, governance, analytics, and deployment documentation. Who Should Take This Course?

This course is designed for Databricks Beginners, aspiring Data Engineers, SQL Developers, Data Analysts, Software Engineers, and professionals exploring Cloud Data Engineering, Data Lakehouse Architecture, and AI-Powered Analytics. No previous Databricks experience is required. Basic familiarity with Python or SQL is helpful, but beginners are welcome.

Start Building Your Databricks Skills TodayBy completing this course, you'll gain practical experience with modern Data Engineering Workflows, understand how enterprise Lakehouse platforms operate, and develop projects you can confidently discuss during technical interviews. Stop watching disconnected tutorials. Start building real solutions with Databricks Fundamentals 2026!

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This course contains the use of artificial intelligence.What if your AI application could do more than generate answers? What if it could decide which model should handle a task, whether a tool call is safe, and when a human needs to step in?Welcome to Jev Fundamentals: Decision AI for Beginners, a hands-on introduction to Decision AI, AI agents, and intelligent automation. You'll explore how Jev by TypeSafe supports structured decisions inside real-world software, and learn to combine those decisions with clear business rules, confidence thresholds, and human oversight.Rather than watching disconnected demos, you'll build an evolving case study called BrightDesk DecisionOps. Starting with a simple customer-support classifier, you'll progress toward an AI decision engine that can route requests, evaluate risk, check evidence, and support safer agent workflows.What You'll Build and LearnYou'll begin with Python, the Jev API, and core decision concepts such as classification, scoring, and probability-based decisions. Then you'll learn how to design useful decision questions, interpret model outputs, set confidence thresholds, and handle uncertain results without treating every prediction as fact.Next, you'll explore LLM routing and AI model selection. Build workflows that choose between smaller and larger large language models (LLMs) based on task type, cost, and complexity. Integrate decision logic into LangChain and LangGraph, using structured application state, conditional branches, and human-in-the-loop approvals.You'll also tackle a critical challenge in agentic AI: deciding when an agent should be allowed to act. Build tool-call gating and AI guardrails that flag risky actions while enforcing permissions through deterministic code. Investigate prompt injection detection, AI safety, hallucination detection, citation verification, and retrieval-augmented generation (RAG) evidence checks.How do you know whether a decision model is actually improving your system? You'll create an AI benchmarking workflow that compares Jev with LLM classifiers and rule-based baselines, measuring accuracy, precision, recall, F1 score, latency, and inference cost. You'll learn why evaluation datasets, false positives, and confidence calibration matter more than flashy performance claims.Finally, we'll cover production AI engineering: model version pinning, API integration, fallback strategies, observability, audit logging, enterprise data security, privacy, and AI governance. Your capstone brings these skills together in a practical, reviewable Decision AI application.Who Should Take This Course?This course is designed for beginners, Python developers, AI engineers, software architects, automation builders, and professionals exploring generative AI or enterprise agent systems. Basic Python familiarity helps; prior Jev experience isn't required.Most examples use approachable code and synthetic business data. Optional local models can support comparisons, while live Jev exercises require access to TypeSafe's service. Because frameworks evolve, you'll also learn patterns that remain useful when SDKs and APIs change.You will leave with reusable project ideas for customer support automation, enterprise copilots, document review, and intelligent workflow orchestration. Whether you want to improve existing AI agents or prototype new applications, this course gives you a foundation.By the end, you won't just know the terminology. You'll have hands-on experience building AI systems that can route intelligently, verify evidence, measure performance, and escalate uncertainty—instead of blindly trusting every model response.

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Databricks Fundamentals 2026: Hands-On Projects

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Jev Fundamentals: Decision AI for Beginners

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Databricks Fundamentals 2026: Hands-On Projects

About this course

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Jev Fundamentals: Decision AI for Beginners

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