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Combo Bundle

Databricks + Azure Data Factory Combo: From Pipelines to a Production GenAI Project

Three courses that cover the stack data engineering job postings ask for: Databricks Zero to Hero, Azure Data Factory interview scenarios, and a real end-to-end project built on Delta Lake, Vector Search and LLMs.

22 Databricks modules ADF scenario-based training Practice projects + capstone Pre-recorded lessons
Created by Sagar Prajapati · Language: English
3
Courses in one bundle
22
Databricks modules
10
Phases in the GenAI project
4
Projects and a capstone
The bundle

Everything in one path

Learn the Databricks platform end to end, cover the Azure Data Factory scenarios that come up in interviews, and finish with a project you can put on your resume and walk an interviewer through.

Course 1 · Databricks

Databricks Certified Data Engineer: Zero to Hero

  • SQL, Python, Spark and lakehouse fundamentals
  • Delta Lake, Auto Loader, Lakeflow pipelines and jobs
  • Unity Catalog, CI/CD with Asset Bundles, monitoring
  • Streaming, performance tuning, AI Functions and RAG
  • Associate and Professional exam tracks
Course 2 · Azure

Master in Azure Data Factory: Interview Questions and End-to-End Project

  • Pipelines, activities, datasets and linked services
  • Integration runtimes, triggers and parameters
  • Incremental loading with watermark patterns
  • Common ADF interview questions, answered with reasoning
  • Real-time scenario questions and best practices
Project · GenAI

Databricks End-to-End Project with Delta Lake and Gen AI

  • Procurement automation: PO, GR and Invoice matching
  • LLM-based extraction from PDFs
  • Vector Search, Delta Lake Bronze to Gold
  • Databricks Workflows, monitoring and CI/CD

What's covered across the combo

Skill Databricks Zero to Hero Azure Data Factory GenAI Project
Spark and PySpark Yes, from fundamentals to tuning Used with Databricks activities Yes
Delta Lake and medallion architecture Yes, in depth Lands data into ADLS Bronze to Gold
Pipeline orchestration Lakeflow Jobs Pipelines, triggers, parameters Workflows DAG
Incremental loading Auto Loader, MERGE, CDC Watermark and last-modified patterns Incremental PDF ingestion
Governance Unity Catalog Access and secrets Unity Catalog
CI/CD Asset Bundles, GitHub Actions Git integration and deployment Asset Bundles
Interview preparation Question practice alongside modules Scenario-based questions Project walkthrough
Generative AI AI Functions, RAG, Agents n/a LLMs, embeddings, Vector Search
Course 1 of 3

Databricks Certified Data Engineer: Zero to Hero

One path from SQL basics to production pipelines, governance, CI/CD and AI on Databricks. Certification prep and interview practice run alongside the modules.

22 modules

Phase A · Foundations

01 · Data engineering and the lakehouse
  • What data engineers do on real projects; OLTP vs OLAP
  • Warehouse vs lake vs lakehouse; ETL vs ELT, batch vs streaming
  • Medallion architecture: Bronze, Silver, Gold
  • Workspace, metastore, catalogs, schemas, tables and volumes
Lab: Design the full data architecture for an e-commerce company.
02 · SQL for data engineers
  • Joins (inner, outer, semi, anti), aggregations, subqueries, CTEs
  • Window functions for ranking, running totals and dedup
  • Dates, strings, arrays, maps and JSON
  • Interview classics: gaps and islands, top-N per group, sessionization
Lab: SQL problems on sales, customer and transaction data.
03 · Python for data engineering
  • Collections, functions, comprehensions, generators, exceptions, logging
  • Reading JSON, CSV, Parquet and API responses
  • REST APIs: pagination, auth concepts, retries; testing and debugging
Lab: A reusable API ingestion utility with pagination and error handling.
04 · Apache Spark and PySpark fundamentals
  • Driver, executors, jobs, stages, tasks, partitions; lazy evaluation
  • Narrow vs wide transformations and shuffles; repartition, coalesce, caching
  • Broadcast joins, skew, shuffle partitions, Catalyst and physical plans
  • Reading the Spark UI and execution plans
Lab: Compare join strategies, inspect plans and diagnose a skewed workload.

Phase B · Core Databricks engineering

05 · Databricks platform and compute
  • All-purpose vs job compute, SQL warehouses, serverless, Photon
  • Compute policies, autoscaling, access modes; cost drivers
  • Notebooks, Git folders, Catalog Explorer; DBFS vs Unity Catalog volumes
Lab: Choose the right compute for four different workloads.
06 · Delta Lake, in depth
  • Transaction log, ACID guarantees, schema enforcement and evolution
  • MERGE INTO, upserts, deletes; time travel and restore
  • OPTIMIZE, VACUUM, data skipping, liquid clustering, Change Data Feed
Lab: Build an incremental Delta table, merge late updates, recover an old version.
07 · Ingestion: files, APIs and databases
  • Auto Loader: schema inference, evolution, rescued data; COPY INTO
  • JDBC and REST APIs with rate limits and retries
  • Idempotency, duplicates, audit columns, corrupt records
Lab: Ingest files and API data, handle schema drift, make the process restartable.
08 · Data modelling and transformation
  • Bronze, Silver and Gold implementation; cleansing, dedup, validation
  • Star schema, surrogate keys, grain; SCD Type 1 and Type 2
  • CDC, out-of-order events, late-arriving facts
Lab: Customer and orders model with SCD Type 2 and incremental Gold aggregates.
09 · Lakeflow Spark Declarative Pipelines
  • Streaming tables, materialized views, flows and sinks
  • Expectations, quality metrics and quarantine
  • AUTO CDC, SCD Type 1 and 2, event ordering
Lab: Bronze-to-Gold pipeline with data quality rules and incremental CDC.
10 · Lakeflow Jobs and orchestration
  • Multi-task jobs, dependencies, parameters, task values
  • Schedules, triggers, retries, notifications, repair runs
  • Idempotent workflows and backfills
Lab: Orchestrate ingestion, transforms and quality checks with alerts and backfills.

Phase C · Production and professional level

11 · Unity Catalog and enterprise governance
  • Grants, ownership, service principals, storage credentials, external locations
  • Row filters, column masks, PII and retention; lineage and audit logs
Lab: Role-based access, column masking, a governed volume and an auditable pipeline.
12 · Performance and cost engineering
  • Spark UI, query profiles, reducing shuffles, handling skew
  • Partition sizing, small files, compaction, Z-ORDER, Photon
  • System tables and cost allocation
Lab: Speed up a slow pipeline; document runtime, cost and correctness before and after.
13 · Structured Streaming and real-time data
  • Micro-batch execution, triggers, checkpoints, watermarks, event time
  • Stream-stream and stream-static joins; Kafka and Event Hubs
  • Failures, checkpoint recovery and monitoring
Lab: Near-real-time events pipeline that handles late data and survives a checkpoint recovery.
14 · Testing, CI/CD and deployment
  • Git workflows, Databricks Asset Bundles, dev, test and prod targets
  • GitHub Actions and service principals; unit and data-quality tests
Lab: Deploy a pipeline to test, then promote it to production.
15 · Monitoring, observability and troubleshooting
  • Job and pipeline monitoring, system tables, event logs, alerts
  • Freshness, volume anomalies, schema drift; root-cause analysis
Lab: Diagnose five injected production failures.
16 · Data sharing and interoperability
  • Delta Sharing, Lakehouse Federation, Apache Iceberg
  • Open table formats, trade-offs, data contracts
Lab: Design a governed data-sharing solution.

Phase D · Modern Databricks and AI

17 · Lakeflow Connect and advanced ingestion
  • Managed connectors for SaaS, database and file sources; Auto Loader vs JDBC vs connectors
18 · AI Functions and AI-powered ETL
  • Extraction, classification, enrichment and summaries in SQL and pipelines; cost control and human review
19 · Vector Search, embeddings and RAG
  • Embeddings, chunking, vector indexes, hybrid retrieval, evaluation and hallucination checks
20 · Agentic AI on Databricks
  • Tool calling, multi-step workflows, human approval, tracing and model serving
21 · Databricks Apps, Lakebase and operational apps
  • Interactive apps on Databricks Apps, Lakebase Postgres, transactional state and permissions
22 · Capstone: architecture and production readiness
  • Turn business requirements into architecture; contracts, SLAs, security and recovery
  • CI/CD, monitoring and presenting trade-offs like a senior engineer
Lab: Deliver a complete solution with code, tests, deployment and a walkthrough.

Practice projects and exam tracks

Beginner

Retail sales lakehouse

CSV / JSON → Bronze → Silver → Gold
  • Delta Lake, medallion, joins and schema handling
  • Finish with a sales dashboard
Beginner → Intermediate

Incremental ingestion framework

Files / API → Auto Loader → Bronze
  • Restartability, schema evolution, duplicate prevention
  • Error logging and incremental processing
Intermediate

Customer 360 and CDC

Customers + orders + txns → MERGE → SCD2
  • Resolve duplicates, implement SCD Type 2
  • Build incremental Gold metrics

Track A · Data Engineer Associate

For freshers and engineers new to Databricks.

  • Topic-wise question bank with explanations
  • Mock exams and a revision sheet

Track B · Data Engineer Professional

For working engineers focused on production.

  • Scenario questions that need trade-off thinking
  • Performance and CI/CD case studies
Course 2 of 3

Master in Azure Data Factory: Interview Questions and End-to-End Project

Learn ADF the way interviewers test it: by explaining design choices, handling failures, and solving real-time scenarios.

Core concepts

  • Pipelines, activities (Copy, Lookup, ForEach, Until), datasets and linked services
  • Integration runtimes: Azure vs self-hosted, and when each is used
  • Triggers: schedule, tumbling window, event-based, and debug vs trigger-now
  • Parameters and dynamic content for reusable pipelines
  • Error handling, retries, logging and monitoring

Real-time scenarios

  • Incremental loading with watermark and last-modified patterns
  • Iterating over files and building metadata-driven frameworks
  • Choosing Copy activity vs Mapping Data Flows vs calling Databricks
  • Securing credentials with Key Vault and managed identity
  • CI/CD with Git integration and ARM template deployment

Interview preparation

  • Common ADF interview questions answered with reasoning
  • Blob vs ADLS Gen2, linked service vs dataset, and other frequently asked comparisons
  • Design questions on production failures, cost and recovery

Hands-on end-to-end project

  • Multi-source ingestion from APIs, SFTP, on-premises SQL and Blob storage
  • Landing data in ADLS Gen2 and transforming it toward Delta Lake
  • Serving a Power BI report connected to the Gold layer
Course 3 of 3 · End-to-End Project

Databricks End-to-End Project with Delta Lake and Gen AI

Build a procurement automation pipeline. It extracts data from POs, goods receipts and invoices with LLMs, matches them, and scores confidence.

The problem

Organizations receive Purchase Orders, Goods Receipts and Invoices as PDFs from many vendors. Reconciling them by hand leads to mismatched records, slow validations, human errors and delayed payments. This project automates that work.

01PDF ingestion via UC Volumes
02LLM-based text extraction
03Embeddings and Vector Search
04Delta Lake Bronze → Silver → Gold
05PO ↔ GR ↔ Invoice matching
06Confidence scoring and exceptions
07Unity Catalog governance
08Databricks Workflows
09Monitoring dashboards
10Deployment and demo walkthrough

Project phases

01 · Project overview and architecture
  • The procurement problem; end-to-end architecture; repo structure
02 · Environment setup and Unity Catalog
  • Workspace with UC enabled; catalogs, schemas, volumes; service principals and secrets; Git integration
03 · PDF ingestion pipeline
  • Landing PDFs from S3 or ADLS; Auto Loader for incremental ingestion; duplicates and failed loads
04 · LLM-based text extraction
  • PDF parsing with OCR fallback; prompt design for structured fields; Model Serving; schema-first JSON outputs
05 · Embeddings and Vector Search
  • Choosing embedding models; Vector Search endpoints; Delta-synced indexes; hybrid search
06 · Delta Lake: Bronze, Silver, Gold
  • Medallion for unstructured data; MERGE and schema evolution; Change Data Feed
07 · PO ↔ GR ↔ Invoice matching
  • Three-way match on quantities, prices and vendors; fuzzy and semantic matching; tolerance bands
08 · Confidence scoring and exceptions
  • Match confidence score (0–1); auto-approvals; exception queue; audit trail
09 · Databricks Workflows and orchestration
  • Job DAG across phases; conditional branches; retries, alerts and scheduling
10 · Dashboards, monitoring and deployment
  • SQL dashboards for procurement KPIs; LLM cost monitoring; CI/CD with Asset Bundles; final demo

What's included

  • Codebase, sample PDFs and notebooks
  • Pre-recorded lessons you can pause and rewatch

How to get the most from it

  • Run it on a Databricks trial workspace with Unity Catalog
  • Write up the architecture as a portfolio summary
  • Practice explaining the three-way match design in interviews
FAQ

Common questions

Who is this combo for?

Freshers and career switchers who want a complete Azure and Databricks path, and working engineers who want production depth and a portfolio project. Basic SQL and Python will help you move faster.

Do I need prior Databricks or Azure experience?

No for the Databricks course, which starts from SQL, Python and Spark basics. Some familiarity with SQL and Python makes the ADF course and the GenAI project easier.

In what order should I take the courses?

Start with Databricks Zero to Hero, then ADF for the Azure side, then the GenAI project as your capstone. You can also start with the project if you already know Spark and Delta Lake.

Are the courses self-paced?

Yes. All lessons are pre-recorded. Watch at your own pace, pause, rewind and revisit modules.

Get the full stack in one bundle

Databricks Zero to Hero, Azure Data Factory, and the GenAI end-to-end project in one combo.

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$110

$199

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