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Databricks for Data Engineers: Full Curriculum (Structured)

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“This course contains the use of artificial intelligence”Welcome to the ultimate Data Engineering Bootcamp, designed to take you from foundational concepts to building production-grade data systems used in real-world companies. In this course, you won’t just learn theory—you’ll build end-to-end data pipelines, work with Databricks, and deliver business-ready dashboards using modern tools and best practices. We start with the fundamentals of data engineering, including the Medallion Architecture (Bronze, Silver, Gold), and gradually move into advanced topics like ETL vs ELT, batch and streaming pipelines, and incremental processing.

You’ll learn how to work with different data formats like CSV, JSON, and Parquet, and how to design efficient pipelines using Apache Spark. We’ll also cover how to build optimized storage using Delta Lake, ensuring your data is reliable, scalable, and ready for analytics. As you progress, you’ll master data optimization techniques such as partitioning, query optimization, caching, and cost optimization strategies like cluster sizing and autoscaling.

These are critical skills used by professional data engineers to improve performance and reduce cloud costs. We also go deep into data governance and security, where you’ll learn how to use Unity Catalog, implement role-based access control (RBAC), and manage data lineage to track how data flows through your system. Once your data is prepared, you’ll move into the analytics layer—learning how to create Gold tables that are structured for business use.

You’ll use SQL Endpoints to run analytical queries and understand how to optimize them for performance. Finally, you’ll connect everything to BI tools like Power BI and Tableau, creating dashboard-ready data and building visualizations that drive real business decisions. What Makes This Course Unique?

This is not just another tutorial-based course.

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This course contains the use of artificial intelligence. Lectures are narrated by Katie, an AI voice, and AI tools helped produce the scripts, animations, quizzes and the promo video, where Vinay’s footage and voice are AI-generated from his own photos and recordings. The CodeSierra team curated the curriculum, and every notebook was run and tested.This course goes past the demo. You build something complete: a retention and support assistant for NimbusTel, a fictional telecom company. A support agent types a customer ID. The assistant looks up the customer, predicts their churn risk (how likely they are to leave) with a machine learning model, searches the company's policy documents and drafts a retention offer that quotes those policies and passes the company's offer rules. The agent reviews it, then approves and saves it.To get there, you learn both halves of modern AI and how to join them.Predictive AI. You start with Python from scratch, then explore and clean data with pandas, train and evaluate models with scikit-learn, and handle the problems that break real projects: data leakage, imbalanced classes and accuracy scores that hide a useless model. You build a churn model on a public dataset of 7,043 telecom customers.Deep learning and generative AI. You build neural networks in PyTorch, see how transformers and large language models work, and learn why they hallucinate. You write prompts that hold up, call models from Python through an API, and get reliable structured outputs.AI applications. You build retrieval-augmented generation (RAG) over documents, give a model tools to call, add guardrails against prompt injection, test your app with evaluation cases, and put a Gradio interface on it.Responsible AI. You measure bias in your own model, explain its predictions with SHAP, and learn which data should never leave your machine.The capstone ties it all together. You design, build, test and deploy the NimbusTel assistant to Hugging Face Spaces (or a free temporary public link), and finish with a portfolio project you can explain in an interview.How the course works. 18.6 hours of video across 10 modules. Short animated lectures explain each idea; hands-on lectures build notebooks cell by cell with real outputs. Every hands-on lecture ends with a practice exercise, and every module ends with a quiz. Everything runs in Google Colab in your browser. Modules 6–10 use the OpenAI API (a few US dollars of credit covers the whole course). Lecture 6.15 shows how to run chat calls on an open model; the search, image and capstone lectures use the OpenAI API.

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This course contains the use of artificial intelligence.Building an AI agent that works for five minutes is easy. Building one that works for five hours, unattended, is a different problem.Leave an agent running overnight and it breaks in predictable ways. Its context fills up and it starts mixing up customers. The server restarts at 2 AM and nobody knows whether that refund went through. It gets stuck in a loop and burns money until morning. It marks work as "done" when nothing was done. And sometimes it takes an action nobody can undo.This course teaches the design patterns that prevent each of these failures. They are the same patterns behind today's coding agents, background agents and durable workflow engines, explained clearly enough that you can apply them in any stack.One story, one night, one agentThe whole course follows Nightshift, an agent working the night shift at Pebblecart, a small online store with 600 support tickets left over from Black Friday. Nightshift has real tools: it looks up orders, reads the refund policy, emails customers and issues refunds.The first night goes badly. Section by section, you give Nightshift the patterns it needs, and in the last lesson it finishes a full night: tickets resolved, risky refunds waiting for approval, hard cases handed to humans, and a sensible bill.The 5 ways long-running agents break, and what you'll learn to fix themForgetting: compaction, clearing tool results, just-in-time retrieval, sub-agents, progress files, structured work lists, goal anchoring.Dying: checkpoints, event logs and replay, idempotent tools, workflow engines, suspend and resume with webhooks, work queues with leases.Getting stuck: retries with backoff, loop detection, token and money budgets, stop conditions, circuit breakers and fallback models.Lying: verification with facts instead of the agent's word, evaluator–optimizer reviewers, overnight reconciliation.Acting recklessly: approval gates, async human approval, escalation, and defending against prompt injection in customer emails.You'll also learn when multiple agents beat one, how to hand off work between agents, how to trace an 8-hour run, and how to cut the cost of a night's work.What makes this course differentPatterns, not a framework. Everything applies whether you use the Claude Agent SDK, LangGraph, Cloudflare Agents, Temporal, Inngest or your own loop. Each lesson ends with where the pattern already exists in popular platforms.Short, focused lessons. Every video is 3 to 6 minutes and teaches one pattern.Animated diagrams instead of slides. Every idea is drawn and animated step by step, so you can see the mechanism, not just hear about it.Pseudocode code cards. Each pattern ends with a short pseudocode card you can translate into any language. The harness grows lesson by lesson, with new lines highlighted.No setup, no repo to maintain. Watch on your phone, on the train, or between meetings.Also includedA scenario quiz at the end of every section, based on real incidents from Nightshift's nightsA final assignment: design the harness for your own agent on one pagePattern cards (PDF): one card per pattern with the problem, diagram, pseudocode and trade-offsPattern map (PDF): every pattern on one page, organized by failure modeOvernight checklist (PDF): what to check before you let an agent run unattendedBy the end of this course, you'll be able to look at any agent, predict how it will fail on a long run, and choose the patterns that keep it on track.Enroll now and give your agent a night shift it can survive.This course is independent and is not affiliated with or endorsed by any of the companies or products mentioned. Pebblecart and Nightshift are fictional names created for this course.

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Databricks for Data Engineers: Full Curriculum (Structured)

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