Live online · 100 hours · Beginner to Advanced
Azure Data Engineering Masterclass — 100 Hours across ADF, Databricks, Synapse and Microsoft Fabric
The Azure stack as it is in 2026 — Fabric-first, with ADF, Databricks and Synapse in their real-world places.
What you will be able to do
- Build metadata-driven ADF/Fabric pipelines that scale to hundreds of tables without copy-paste
- Design an ADLS Gen2 medallion lake with Delta and Unity Catalog on Azure Databricks
- Work fluently in Microsoft Fabric: OneLake, Lakehouse, Warehouse, Notebooks, Pipelines, Direct Lake
- Stream with Event Hubs, Kafka-on-Event-Hubs, Stream Analytics and Fabric Real-Time Intelligence
- Govern with Purview/Fabric governance, managed identities, Key Vault and private endpoints
- Pass DP-700 (and understand what carried over from the retired DP-203)
Curriculum — 100 hours across 10 modules
Sessions run live; every session is recorded. Labs are hands-on from module one.
- Subscriptions, resource groups, RBAC, managed identities, service principals
- ADLS Gen2: hierarchical namespace, ACLs vs RBAC, access tiers, lifecycle policies
- Networking basics: VNets, private endpoints, firewalls — the parts that break pipelines
- Key Vault for secrets; Azure CLI, Bicep and Terraform
- Cost management, budgets and tagging
- Lab: secure storage account + private endpoint + Key Vault-backed access
- Pipelines, activities, integration runtimes (Azure, self-hosted, SSIS)
- Linked services, datasets, parameters, variables, expressions
- Copy activity at scale: staging, PolyBase, partitioned copy, DIU tuning
- Mapping Data Flows vs Databricks — when each is the right tool
- Metadata-driven framework: control tables, ForEach, dynamic datasets, watermarks
- Triggers, monitoring, alerts, retry policies, CI/CD with ADF Git integration
- Lab: build the metadata framework used in Project 1
- Workspace, clusters, pools, cluster policies; Unity Catalog on Azure
- Mounting vs abfss:// paths; credential passthrough and managed identities
- PySpark essentials, Delta Lake operations, MERGE, time travel, OPTIMIZE
- Auto Loader from ADLS, Structured Streaming from Event Hubs
- Delta Live Tables and Workflows; Databricks + ADF orchestration patterns
- Lab: bronze→silver→gold medallion lake with quality expectations
- Fabric architecture: capacities, workspaces, OneLake, shortcuts, domains
- Lakehouse vs Warehouse vs Eventhouse — choosing correctly
- Fabric Notebooks, Spark pools, environments, V-Order
- Data pipelines, Dataflows Gen2, Copy Job, Mirroring
- Direct Lake semantic models, Power BI integration, DAX basics for engineers
- Real-Time Intelligence: Eventstreams, KQL databases, activator alerts
- Fabric CI/CD: deployment pipelines, git integration, Fabric APIs
- Lab: build Project 2 end to end in Fabric
- Dedicated SQL pools: distributions (hash/round-robin/replicated), partitions, resource classes
- Serverless SQL pools over the lake; OPENROWSET, external tables, cost per TB
- Synapse Spark pools and pipelines; Synapse Link for Cosmos DB / Dataverse
- Where Synapse still fits now that Fabric exists — an honest assessment
- Lab: tune a dedicated pool query with the right distribution key
- Event Hubs: partitions, consumer groups, capture, throughput units, Kafka endpoint
- Kafka fundamentals and running Kafka workloads against Event Hubs
- Stream Analytics: windowing, reference data, output sinks, watermark behaviour
- Databricks Structured Streaming from Event Hubs/Kafka with exactly-once sinks
- IoT Hub overview; Fabric Eventstreams
- Lab: Project 3 real-time telemetry pipeline
- Azure SQL Database / Managed Instance for data engineers; elastic pools
- T-SQL for ETL: MERGE, temporal tables, partition switching
- Cosmos DB: partition keys, RU/s, change feed, analytical store
- Dimensional modelling: star schema, SCDs, surrogate keys, fact grain
- Lab: design and load a conformed star schema
- Airflow on Azure: Azure Data Factory Managed Airflow, AKS-hosted, or Astronomer
- DAG patterns for Azure: sensors, ADF/Databricks operators, backfills
- Azure DevOps and GitHub Actions: build/release for ADF, Databricks (DABs) and Fabric
- Testing data pipelines; Great Expectations / Fabric data quality
- Lab: full CI/CD promotion dev → test → prod
- Microsoft Purview: scanning, classification, lineage, glossary
- Fabric governance: domains, endorsement, sensitivity labels, Purview integration
- Managed identity everywhere; eliminating keys from pipelines
- Log Analytics, KQL for pipeline monitoring, cost telemetry
- Lab: end-to-end lineage from source system to Power BI report
- Reference architectures: Fabric-first, Databricks-first, hybrid — with trade-offs
- Migration: SSIS → ADF, on-prem SQL → Fabric, Synapse → Fabric
- Capstone presentation and review
- DP-700 exam guide walkthrough, 120 practice questions, two mocks
- Azure data engineer interview questions and resume framing
Hands-on projects
You leave with three portfolio projects you can demo in an interview — not toy notebooks.
Metadata-driven ingestion framework
One ADF pipeline + control table ingesting 120 source tables with watermarking, schema drift handling, retries and audit logging.
Fabric lakehouse with Direct Lake
OneLake medallion lakehouse, Fabric notebooks for transformation, semantic model and a Power BI report on Direct Lake mode.
Real-time telemetry
Event Hubs → Databricks Structured Streaming → Delta → Power BI, with alerting and late-data handling.
Tools and technologies covered
Who this course is for
- SSIS/Informatica developers moving to Azure
- Data engineers on the Microsoft stack
- Power BI developers going upstream
- DP-700 candidates
Prerequisites
- SQL; basic Python helpful but taught in-course
- Azure free account (₹12,000 / $200 credit) — setup walkthrough in session 1
Frequently asked questions
Microsoft retired the DP-203 Azure Data Engineer Associate exam and DP-700 (Fabric Data Engineer) is the current data engineering certification. We teach the DP-700 objectives as the certification path, and still cover ADF, Databricks and Synapse in depth because that is what production Azure estates actually run on. ~90% confident on retirement specifics — always confirm current status on Microsoft Learn before booking.
Both, and the course covers both. Fabric is where Microsoft-centric BI-driven organisations are heading; Azure Databricks remains the default for heavy engineering and ML workloads. Module 10 gives you a decision framework you can defend in an architecture review.
The free account credit covers most labs. Fabric labs use the free Fabric trial capacity. A few Synapse dedicated-pool labs cost a few dollars — we always pause/delete resources at the end of the session.
No, it is built for that transition. Module 2 explicitly maps SSIS concepts (control flow, data flow, packages, configurations) to their ADF and Fabric equivalents.
Venu Katragadda or a course advisor will call or WhatsApp you within one working day with the full syllabus, batch dates and fees. For anything urgent, WhatsApp +91-9247159150.
Free download · PDF
Download the full 100-hour syllabus
Every module, every hour, every lab and all three projects — the same document we hand to corporate clients. No email verification loop; the PDF downloads the moment you submit.
- 10 modules broken down topic by topic with hours
- The 3 portfolio projects in full
- Prerequisites, tools list and certification mapping
- Fees, EMI options, batch timings and the refund policy
What students say about Venu Katragadda
Verified Google reviews from Sreyobhilashi IT students. Read all 320+ reviews →
“Recently took Databricks classes with Venu to upskill in trending technologies, and the experience exceeded all expectations. While I initially sought guidance only on Databricks, Venu provided in-depth training across the entire ecosystem.”
Databricks · Cleared DE Professional Cert · Verified Google review
“This training has exceeded my expectations. Venu explains concepts clearly and uses hands-on examples that make the content easy to understand. I am learning a lot and would definitely recommend.”
Databricks Training · Verified Google review
“I recently completed the Data Engineering course on Databricks and AWS. Venu Sir delivers instruction at the next level, focusing on high-performance learning. He explains every concept clearly and thoroughly, accompanied by practical examples.”
Databricks & AWS Training · Verified Google review
Foundation course or masterclass?
We run two tiers. Most people should start with the foundation course on our sister site and step up later — this page is the advanced one.
Foundation · databrickstraining.in
Azure Data Engineering Training
₹22,000
- DP-203 focused of live instruction
- Covers the job-ready core of the stack
- Best if you are new to the platform or changing careers
- Same trainer, same teaching style
Masterclass · this page
Azure Data Engineering Masterclass
₹28,000
- 100 hours — roughly 25–30 extra hours of depth
- Internals, performance tuning and cost engineering modules
- Three reviewed portfolio projects instead of guided labs
- Architecture review and certification drill included
- Best if you already work with the stack and want senior-level depth
Not sure which fits? WhatsApp +91-9247159150 and Venu Katragadda will tell you straight — including when the cheaper one is the right answer.
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