Databricks Certified Engineer
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8-Course Combo Β· Databricks Zero to Expert

Databricks Zero to Expert: PySpark, Delta Lake & GenAI (With Projects & Interview Prep)

Unleash your data superpowers with Databricks! Master PySpark, Delta Lake, and GenAI with hands-on projects and ace your interview preparation. From zero to expert in no time.

8 Courses Β· Best Value Combo Self-paced Β· Lifetime Access Projects + Interview Prep
What's included

8 Courses Β· From Spark Fundamentals to Production GenAI

A complete Databricks learning path designed for 2026. Master the certification syllabus, sharpen your interview skills with 50+ real PySpark problems, learn dimensional data modelling, and ship production-grade Agentic & GenAI applications using Mosaic AI, Vector Search, LangGraph, and MCP. 100% real-time, project-driven, lifetime access.

01

Databricks Certified Data Engineer · Zero to Hero ⭐

Spark internals, PySpark, Delta Lake, Unity Catalog, DLT, Workflows + new UI features (Playground, Lakeflow, Dashboards, Alerts) β€” fully aligned to the certification exam syllabus.

02

Practice 50 PySpark Interview Questions

50 curated PySpark coding problems from real MNC rounds β€” joins, window functions, optimization, skew handling β€” solved step-by-step in Databricks notebooks.

03

Databricks End-to-End: Delta Lake + GenAI

Production Lakehouse build β€” Bronze/Silver/Gold medallion with Auto Loader, then layered with Vector Search, RAG, and Mosaic AI Model Serving on real data.

04

Build AI Application using Databricks + LLM

Ship a deployable AI app β€” Mosaic AI Agent Framework, Unity Catalog functions as agent tools, model serving, evaluation, and access control end-to-end.

05

Databricks GenAI Project πŸ†•

Latest 2026 batch β€” full GenAI build with Mosaic AI, Agent Bricks, Genie, Lakeflow, and current production deployment patterns. The flagship resume project.

06

Databricks Agentic AI Bootcamp Β· 4 Production Agents πŸ†•

Build 4 real agent systems end-to-end β€” Data Dictionary App, ResumeΓ—JD Matcher, Hospital Prior-Auth Agent, Support Ticket Triage β€” with LangGraph, MCP servers, and Databricks Apps.

07

Master in Data Modelling & Data Warehouse

Dimensional modeling done right β€” star/snowflake schemas, facts & dimensions, SCDs, and warehouse architecture. The design foundation every Lakehouse project sits on.

08

Generative AI Engineer Training Β· Build, Deploy & Scale

8-week GenAI engineering mastery β€” RAG with LangChain & Bedrock, agents with CrewAI, vector DBs (FAISS, Pinecone, Chroma), and observability with LangSmith & LangFuse.

Students placed in top companies Β· up to 100% hike

Syllabus

Courses in the package

A detailed module-by-module breakdown of all 8 courses inside the combo. Each module is a full standalone course on the GeekCoders catalog β€” sequenced here as a single learning path: foundations β†’ interview drill β†’ data modelling β†’ end-to-end projects β†’ production Agentic & GenAI on Databricks.

8 Courses
100% Real-time content
Lifetime Access
Cert + GenAI Track
01

Databricks Certified Data Engineer Β· Zero to Hero

Foundation Β· Cert-aligned
  • Databricks workspace mastery β€” clusters, notebooks, repos, jobs, cluster policies, compute types
  • Apache Spark architecture β€” driver/executor model, DAGs, lazy evaluation, RDDs vs DataFrames
  • PySpark deep dive β€” transformations & actions, joins, aggregations, window functions, UDFs & pandas UDFs
  • Delta Lake internals β€” ACID transactions, MERGE, schema evolution, time travel, OPTIMIZE, VACUUM, Z-ORDER
  • Unity Catalog β€” catalogs & schemas, governance, fine-grained access control, lineage, service principals
  • Delta Live Tables (DLT) β€” declarative pipelines, expectations, change data capture, streaming tables
  • Databricks Workflows β€” job orchestration, multi-task pipelines, retries, dependencies, notifications
  • Latest Databricks UI features β€” Playground, Lakeflow, Dashboards, Alerts, SQL Editor
  • Mosaic AI & Vector Search basics β€” first GenAI POC on the Lakehouse
  • Certification prep β€” exam-pattern walkthroughs, practice tests, scoring guidance
02

Practice 50 PySpark Interview Questions

Interview Drill
  • 50 curated PySpark coding problems sourced from real MNC interview rounds
  • DataFrame API patterns β€” filter, select, joins, groupBy, aggregations, pivot/unpivot
  • Window functions β€” ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, running totals, top-N per group
  • Spark SQL β€” analytical queries, deduplication, gaps & islands, sessionization patterns
  • Performance tuning β€” broadcast hints, repartition vs coalesce, AQE, skew handling
  • Data engineering scenarios β€” SCD Type 1/2, incremental loads, CDC handling, MERGE patterns
  • Step-by-step solution walkthroughs in Databricks notebooks with explanation of each design choice
  • Common interview traps β€” what interviewers actually evaluate and the "gotchas" that trip candidates
03

Databricks End-to-End Project Β· Delta Lake + GenAI

End-to-End Project
  • Project architecture β€” Bronze β†’ Silver β†’ Gold medallion layout on Delta Lake
  • Data ingestion β€” Auto Loader, Cloud Files, structured streaming for incremental pipelines
  • Transformations in PySpark β€” business logic, dimensional modeling, data quality enforcement
  • Delta features in production β€” CDF, MERGE, Z-ORDER, generated columns, identity columns
  • Vector Search index design β€” embedding pipelines, chunking strategies, similarity scoring
  • RAG pattern β€” retrieval, prompt construction, response grounding, citation handling
  • Mosaic AI Model Serving β€” endpoint deployment, auth, rate limits, monitoring
  • End-to-end GenAI feature β€” from raw documents to a governed, usable AI capability
04

Build AI Application using Databricks + LLM

Production AI App
  • LLM fundamentals on Databricks β€” Foundation Models & external models, prompt engineering basics
  • Mosaic AI Agent Framework β€” building tool-using agents on the Lakehouse
  • Vector Search β€” choosing fields, chunking strategy, filters, hybrid search, similarity thresholds
  • Unity Catalog functions as agent tools β€” governance + reusability for AI workflows
  • Model Serving β€” provisioned throughput vs pay-per-token, caching, monitoring
  • Evaluation β€” Mosaic AI Agent Evaluation, judges, regression testing for prompts
  • App layer β€” exposing the AI app via REST API or front-end UI with proper auth
  • End-to-end production AI application with logging, observability, and access control
05

Databricks End-to-End GenAI Project πŸ†•

Latest Flagship Β· 2026
  • Latest 2026 GenAI project β€” built on the newest Databricks Mosaic AI capabilities
  • Agent Bricks β€” defining agents declaratively with Unity Catalog-governed tools
  • Genie β€” natural-language analytics over the Lakehouse, integrated into the AI workflow
  • Lakeflow β€” modern ETL orchestration replacing traditional Workflows for AI pipelines
  • Vector Search at scale β€” index sizing, hybrid retrieval, filter pushdown, freshness
  • Mosaic AI Agent Evaluation β€” production evaluation harness, judge models, online metrics
  • Model serving + monitoring β€” provisioned throughput endpoints, cost controls, drift detection
  • Final deliverable β€” a resume-grade GenAI flagship project that recruiters reply to within hours
06

Databricks Agentic AI Bootcamp Β· Build 4 Production Agents πŸ†•

Agentic AI Β· 4 Portfolio Projects
  • Agent architecture on Databricks β€” Mosaic AI, Unity Catalog, LangGraph orchestration, MCP servers, Databricks Apps
  • Project 1 Β· Data Dictionary App β€” MCP server that reads Unity Catalog schemas and auto-generates column documentation with LLMs, served through a conversational chat interface
  • Project 2 Β· Resume & JD Matcher β€” LLM resume parsing, embeddings in Databricks Vector Search, LangGraph agent with UC Functions to score, rank, and explain candidate fit
  • Project 3 Β· Hospital Prior-Authorization Agent β€” Streamlit Databricks App + MCP server + LangGraph multi-step decisioning, live patient context from Unity Catalog, human escalation for complex cases
  • Project 4 Β· Support Ticket Triage β€” classification, priority assignment, and routing from historical resolution patterns, with auto-drafted first responses
  • MLflow evaluation β€” measuring agent performance continuously with human-in-the-loop review steps
  • Deployment β€” shipping agents to production with Mosaic AI serving, governance, and access control
  • Outcome β€” four complete, demo-ready agent projects you can defend in interviews
07

Master in Data Modelling & Data Warehouse

Design Foundation
  • Data modeling fundamentals β€” conceptual, logical, and physical models; entities, relationships, normalization
  • Data warehouse architecture β€” staging, integration, and presentation layers; Kimball vs Inmon approaches
  • Dimensional modeling β€” star and snowflake schemas, fact and dimension table design, grain definition
  • Slowly Changing Dimensions β€” SCD Type 1/2/3 patterns and when to use each
  • Facts deep dive β€” additive, semi-additive, and factless facts; surrogate keys and conformed dimensions
  • Hands-on projects β€” building complete warehouse models from real business requirements
  • Bridging to the Lakehouse β€” how classic dimensional design maps onto Delta Lake medallion architecture
08

Generative AI Engineer Training Β· Build, Deploy & Scale

GenAI Engineering Β· 8 Weeks
  • GenAI foundations β€” LLMs, embeddings, vector databases, RAG, and agent concepts from the ground up
  • Prompt engineering β€” best practices, advanced prompting techniques, and systematic prompt debugging
  • Embeddings & vector search β€” semantic search with FAISS, Pinecone, Chroma, and more
  • RAG applications β€” building and scaling pipelines with LangChain, AWS Bedrock, LangFlow, and Phi Data
  • Agentic AI β€” creating and orchestrating autonomous agents with LangChain, CrewAI, and LangFlow
  • Observability β€” monitoring and evaluating GenAI apps with LangSmith, LangFuse, and LangWatch
  • Bonus sessions β€” LangServe integration, MCP and Agent-to-Agent (A2A) best practices
  • Capstone β€” deploy a complete GenAI solution in your chosen domain
Databricks Certified Data Engineer - Zero to Hero

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Practice 50 PySpark Interview Questions

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Build AI Application using Databricks and LLM

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Student feedback

What learners say

β˜…β˜…β˜…β˜…β˜…
The Databricks deep-dive is unmatched. Spark internals, Delta Lake, Unity Catalog, DLT β€” all covered with real scenarios. Cleared the Databricks Certification on first attempt.
Toshita
Toshita London Stock Exchange
β˜…β˜…β˜…β˜…β˜…
The GenAI + Databricks project changed my resume completely. Recruiters started replying within hours once I added Vector Search and RAG on Databricks. 100% real-time content.
Surajit
Surajit LatentView Analytics
β˜…β˜…β˜…β˜…β˜…
Sagar explains every Databricks concept with end-to-end production context. The 50 PySpark questions alone are worth the bundle price. Now leading a Databricks project.
Mahesh
Mahesh Persistent Systems
What we offer

Why this combo works

Pre-recorded + Live

Self-paced video lessons you can pause and rewatch anytime, plus live cohort sessions in the GenAI Engineer Training.

Cert + Project + Interview

The only Databricks combo that bundles certification prep, real projects, and 50+ PySpark interview problems together.

Community & Networking

Join 6,000+ learners. Network, get doubt resolution, and collaborate in exclusive chat groups.

6 Agent + GenAI Projects

Build production-grade Agentic & GenAI applications using Mosaic AI, Vector Search, RAG, LangGraph, MCP, and Agent Bricks β€” straight onto your resume.

50 PySpark Drill

50 real interview problems solved end-to-end in Databricks notebooks β€” the strongest PySpark prep on the market.

Verified Certificates

Earn a shareable certificate per course on completion β€” add them to LinkedIn with a single click.

Common questions

Got questions?

Quick answers about this 8-course Databricks Zero to Expert combo.

Still unsure?

Ask on WhatsApp
What does this 8-course combo include?

Eight Databricks-focused courses sequenced as one path: (1) Databricks Certified Data Engineer Β· Zero to Hero, (2) Practice 50 PySpark Interview Questions, (3) Databricks End-to-End Project Β· Delta Lake + GenAI, (4) Build AI Application using Databricks + LLM, (5) Databricks End-to-End GenAI Project Β· Batch 3, (6) Databricks Agentic AI Bootcamp Β· Build 4 Production Agents, (7) Master in Data Modelling & Data Warehouse, and (8) Generative AI Engineer Training Β· Build, Deploy & Scale.

Is this pre-recorded or live?

Most courses are pre-recorded with lifetime access β€” learn at your own pace, start, pause, and resume anytime. The Generative AI Engineer Training also includes live cohort sessions with weekly bonus classes.

Will this prepare me for the Databricks Certified Data Engineer exam?

Yes. Module 1 (Zero to Hero) is built around the certification exam syllabus β€” Spark, Delta Lake, Unity Catalog, DLT, Workflows. The remaining modules give you the hands-on depth that exam questions assume. Multiple students have cleared the exam on first attempt using this content.

Do I need prior experience?

Basic Python and SQL knowledge is recommended. Spark/Databricks experience is not required β€” Module 1 starts from fundamentals and builds up to production-grade scenarios.

What's covered on the Agentic & GenAI side?

Five of the eight courses go deep into GenAI and Agentic AI β€” Vector Search, RAG, Mosaic AI Agent Framework, Agent Bricks, Genie, Lakeflow, LangGraph, MCP servers, Model Serving, and end-to-end deployment. The Agentic AI Bootcamp alone gives you four production agent projects, and you finish with multiple flagship GenAI builds on your resume.

Will I get a certificate for each course?

Yes. You receive a verified GeekCoders certificate for each of the 8 courses you complete within the combo β€” sharable directly on LinkedIn.

Do I get access to all 8 courses immediately?

Yes β€” on purchase you get instant lifetime access to all 8 courses simultaneously. Work through them in any order you choose.

Enroll Now β†’