2026 edition · Pre-recorded lessons with support
One path from SQL basics to production pipelines, governance, CI/CD and AI on Databricks, with certification prep and interview practice running alongside, not tacked on at the end.
Go from data enthusiast to Databricks Certified Data Engineer extraordinaire with our comprehensive Zero to Hero course - your journey to mastery starts here!
Most students can write a PySpark transformation. Far fewer can tell you why their job got slower last week, how to restart a streaming pipeline without losing data, or how to get code into production without copying notebooks by hand. The course is built around those questions.
Freshers & career switchers
Working engineers
Hi, I'm Sagar. Here's why I built this course.
Lead Data Engineer · Founder & instructor, GeekCoders
I've been teaching Databricks and data engineering through GeekCoders since 2022. Since 2024 I've also been on the founding team of an early-stage startup building a data platform that runs natively on Databricks, so most of my week is spent on exactly the things this course covers.
Most students can write a PySpark transformation. Far fewer can tell you why their job got slower last week, how to restart a streaming pipeline without losing data, or how to get code into production without copying notebooks by hand. So the course is one path from SQL basics to production pipelines, governance, CI/CD and AI on Databricks, with certification prep and interview practice running alongside.
You'll write a lot of code. You'll also break things on purpose and fix them, because that's the part most courses skip and the part that makes you useful on a team.
"If you put in the hours, I'll make sure every one of them counts. See you in Module 01."
Roughly 70% of the course is hands-on engineering. Every lesson follows the same four steps, so you always know what's coming and can keep going on your own.
A real example
In the Delta Lake module, MERGE INTO is not the end. You implement an upsert, push duplicate and late-arriving records through it, check the results, dig into a failure, and explain when a different incremental pattern would have been the better choice.
Foundations come first, then core Databricks engineering, then production-grade delivery, then AI and data applications. Every module includes concept lessons with the internals explained, an instructor-led build, an independent challenge, a hands-on lab on realistic data, interview questions and certification-style scenarios.
For anyone starting from zero. These four modules are the base everything else sits on.
Lab: Design the full data architecture for an e-commerce company and identify every component.
Lab: Solve 25 SQL problems on sales, customer, order and transaction data.
Lab: Build a reusable API ingestion utility with pagination, logging and error handling.
Lab: Compare join and aggregation strategies, inspect the plans, and diagnose a skewed workload.
The heart of the job: Delta Lake, ingestion, modelling, Lakeflow pipelines and orchestration.
Lab: Pick the right compute for an ETL job, a SQL dashboard, a streaming pipeline and interactive development.
Lab: Build an incremental Delta table, merge late updates, recover an old version and optimise it.
Lab: Ingest files and API data, handle schema drift, and make the whole process restartable.
Lab: Build a customer and orders model with SCD Type 2 and incremental Gold aggregates.
Lab: Create a Bronze-to-Gold pipeline with data quality rules and incremental CDC.
Lab: Orchestrate ingestion, transforms, quality checks and reporting with retries, alerts and backfills.
What separates a pipeline that ran once from a platform a business can rely on.
Lab: Set up role-based access, column masking, a governed volume and an auditable pipeline.
Lab: Speed up a slow pipeline and an expensive SQL workload; document runtime, cost and correctness before and after.
Lab: Build a near-real-time events pipeline that handles late events and survives a checkpoint recovery.
Lab: Deploy a full pipeline to test, then promote it to production through a Git-based workflow.
Lab: Diagnose five injected production failures from logs, metrics and execution details.
Lab: Design a governed data-sharing solution and justify the right integration pattern.
AI-powered ETL, RAG, agents, apps and Lakebase. These are an extra career track and are not counted as exam coverage unless the live official guide includes them.
Lab: Design ingestion for several sources and implement one supported connector end to end.
Lab: Turn resumes or job descriptions into validated, structured data and compare against a rule-based baseline.
Lab: Build a document Q&A assistant, measure retrieval quality, and add access controls.
Lab: Build a data-quality investigation agent that queries approved data and writes an evidence-based report.
Lab: Build a data-quality management app with transactional status updates and lakehouse history.
Lab: Deliver a complete solution: architecture, code, tests, deployment, quality metrics and a walkthrough.
Interviewers ask about what you've built, so these aren't copy-the-notebook exercises. Each comes with requirements, a starter repo, tasks, expected output, failure scenarios and a review checklist.
01 · Beginner
Ingest CSV and JSON data, build Bronze, Silver and Gold tables, clean the data with PySpark and SQL, and finish with a sales dashboard.
Flow: CSV / JSON → Bronze → Silver → Gold
02 · Beginner → Intermediate
Ingest files and paginated APIs with restartability, schema evolution, duplicate prevention, error logging and incremental processing.
Flow: Files / API → Auto Loader → Bronze (checkpointed)
03 · Intermediate
Combine customer, order and transaction data, resolve duplicates, implement CDC with SCD Type 2, and build incremental Gold metrics.
Flow: Customers + orders + txns → MERGE → SCD2 → 360 view
Built to be explained in an interview, not just run once.
Exam prep doesn't wait for the last week. Short assessments run through the core modules, then each exam gets its own revision track.
For freshers and engineers new to Databricks. Aligned to the official exam guide effective May 4, 2026, and re-checked before every exam cohort.
| Platform fundamentals | Modules 1, 5, 6 |
| Ingestion and loading | Modules 7, 9, 17 |
| Transformation and modelling | Modules 2, 4, 6, 8, 9 |
| Lakeflow Jobs | Module 10 |
| CI/CD | Module 14 |
| Troubleshooting and optimisation | Modules 4, 12, 15 |
| Governance and security | Module 11 |
For working engineers focused on production. Aligned to the official Professional guide (November 30, 2025 version), re-checked against the live guide before revision modules go out.
| Developing processing code | Modules 4, 6, 8, 9, 13 |
| Ingestion and acquisition | Modules 7, 17 |
| Transformation and quality | Modules 8, 9, 15 |
| Sharing and federation | Module 16 |
| Monitoring and alerting | Modules 10, 12, 15 |
| Cost and performance | Modules 4, 6, 12 |
| Security, governance and compliance | Modules 11, 16 |
| Debugging and deployment | Modules 14, 15 |
| Data modelling | Modules 6, 8 |
The AI and app modules are an extra career track. They are not counted as exam coverage unless the live official guide includes them.
Interview practice runs through the whole course, then comes together in its own track.
SQL & PySpark coding
Architecture & scenario rounds
Spark internals & troubleshooting
Mock interviews & project defence
You don't need every advanced topic before you're useful. Level 1 gets you job-ready; Level 2 takes you deeper.
Beginner → Intermediate · 8–10 weeks at 12–15 hrs/week
Outcome: a solid foundation, finished projects, and structured preparation for the Associate exam and entry-level interviews.
Intermediate → Advanced · another 6–8 weeks
Outcome: broader production skills, a stronger portfolio and focused prep for the Professional exam. Durations are estimates; if you already know SQL, Python and Spark, you'll move faster.
Exam results or job placement. Certification exams are run by Databricks; this course prepares you thoroughly but cannot guarantee exam outcomes or job offers.
22 modules, 120–150 hours, three projects and a capstone. Full course complete by end of November 2026.
