Azure Data Engineering
Design the data systems behind better decisions. A focused 40-day Azure Data Engineering program covering the platform, the pipeline, and the analytical layer—from SQL and data lakes to Data Factory, Databricks, Synapse Analytics, and Power BI.
Will this be a fit for me?
Answer 4 quick questions and we'll tell you honestly — and point you to the track that fits.
Who it's for, what you'll master, and the free bonus vault
Who this is for
- ✓SQL developers and database engineers transitioning to modern cloud data platforms
- ✓Data analysts and BI specialists looking to build scalable ingestion and ETL pipelines
- ✓Software developers wanting hands-on mastery of Azure Data Factory, Databricks, and Synapse
- ✓Engineers preparing for the DP-203 Azure Data Engineer Associate certification
- ✓Anyone unsure yet — the first 4 classes are free, no commitment
What you'll master
- ✓Model the data: Warehouse fundamentals, OLTP vs OLAP, schemas, facts & dimensions, and SQL essentials
- ✓Store & govern it: Azure SQL Database, DTU/vCore scaling, blob storage, SAS tokens, and Data Lake Gen2
- ✓Move & transform it: Azure Data Factory (ADF) pipelines, Integration Runtime, triggers, and control flows
- ✓Scale & analyze it: Azure Databricks Spark clusters, Synapse Analytics, serverless SQL, and Power BI dashboards
Free bonus vault
Every enrolled student unlocks our premium resource vault: interview question banks, production project templates, and career kits. New resources added over time.
Explore the vault →Enroll with confidence
See the teaching for yourself first, then pay only when you're sure. One price, everything included — no hidden add-ons.
How enrolling works
- 1Request a callbackWe talk you through the program, batch and fit — no obligation.
- 2Attend 4 live classes freeSit in on real sessions. No card, no payment up front.
- 3Enroll only when convincedPay securely and unlock all 45 days + the bonus vault.
- Trained 6,000+ learners with a 90% placement rate across 50+ hiring partners
- Taught by an active UiPath Tech Lead, not a recycled recording
- 4.9★ Google rating from 170+ verified students
- Secure payment on this page — no external redirects
Can I pay in installments? Yes — talk to us and we'll work out an option that fits.
- All live classes + lifetime recordings access
- Premium bonus resource vault, free
- Same trainer, all classes, no substitutes
- 100% placement assistance till you're hired
- Pay securely, right here, no external redirect
New batches start regularly, so you're never left waiting — there's almost always a fresh cohort about to begin. Reach out and we'll place you in the next available batch.
Program blueprint
Follow the data, from source to insight. Explore the 5 structured learning phases below.
Follow the data, from source to insight
The curriculum is organized around how modern data systems are planned, built, operated, and analyzed. Each phase brings together platform concepts and applied labs.
Model the data
Build data-warehouse and SQL fluency before working in the cloud.
Store & govern it
Learn the core services that host, protect, and organize enterprise data.
Move & transform it
Design Data Factory pipelines with activities, triggers, runtime options, and production controls.
Scale & analyze it
Complete the platform with Databricks, Synapse Analytics, Power BI, and career readiness.
End-to-end data progression
Warehouse architecture, SQL, and business rules.
Azure SQL, Storage, secure access, and Data Lake Gen2.
ADF, Databricks, activities, triggers, and production control.
Synapse, Serverless SQL, Power BI, and career readiness.
Start with the systems that make data useful
Before a pipeline can move data, teams need to understand what data is for, how it is modeled, and where each platform component fits.
Data warehouse fundamentals
- Data warehouse introduction, definitions, architecture, and schemas.
- Facts and dimensions: identify the measures and descriptive business context that shape analytical models.
- OLTP vs. OLAP: compare transaction processing with analytical workloads.
SQL essentials for data work
- SQL introduction and installation.
- DDL, DML, and DRL command families.
- Inner, left outer, right outer, and full outer joins.
- Select, Case, Switch, and If statements for data selection and business-rule logic.
Service models
Understand IaaS, PaaS, SaaS, and serverless, then explore the Azure Management Portal.
Data engineering practice
Review database-engineering responsibilities, DevOps support for data automation, and the broader data-engineering process.
Azure data landscape
Survey relational and NoSQL databases, storage, ETL, Big Data, and Stream Analytics services.
Data Warehouse & Schemas
OLTP vs OLAP, Star & Snowflake schemas, facts, dimensions, and analytical modeling.
SQL Essentials & Joins
DDL/DML/DRL, multi-table joins, CASE/IF expressions, filtering, and aggregation logic.
Cloud Models & Azure Portal
IaaS, PaaS, SaaS, Serverless, and navigation of Azure Management Portal services.
Build the secure foundation beneath every pipeline
This module brings together the database, storage, security, and recovery capabilities that support reliable cloud data workloads.
Azure SQL Database
- Hosting options for SQL Server workloads in Azure.
- Create Logical SQL Server and SQL Database.
- Compare DTU and vCore tiers; scale capacity up and down.
- Point-in-time recovery and long-term backup retention.
- Geo-replication for disaster recovery.
Azure Storage
- Create a storage account.
- Create containers and blobs.
- Install and explore Azure Storage Explorer.
- Create SAS tokens and understand scoped, time-bound access.
Data Lake Gen2
- Azure Data Lake overview and architecture.
- Create a Data Lake Store Gen2 through the Portal.
- Manage data with Data Lake Store Gen2.
- Position lake storage for downstream integration and analytics.
Protected data access
Configure firewall rules to whitelist required IP addresses at server and database level. Manage sensitive data through Dynamic Data Masking and encryption—then connect storage and data-lake patterns to controlled, scalable access.
Azure SQL Database & Sizing
Provisioning, DTU vs vCore models, scaling, backup retention, and geo-replication.
Storage Accounts & SAS
Containers, blobs, Azure Storage Explorer tool, and scoped SAS token generation.
Data Lake Gen2 & Security
Hierarchical namespaces, data lake layout, IP firewall whitelisting, and data masking.
Turn disconnected data into a controlled pipeline
Azure Data Factory becomes the operating layer: it connects source and target systems, coordinates activity execution, and makes pipeline status observable.
Define
Create an ADF instance and tour the Pipeline, Data Flow, Monitor, Debug, Trigger, and Management Hub interfaces.
- Pipelines
- Linked Services
- Datasets
- Integration Runtime: Azure, Self-Hosted & SSIS
Execute
Build the first pipeline for a fictional company's data, then copy data from a Storage Account to Azure SQL Database.
- Lookup & Stored Procedure
- Filter & Get Metadata
- ForEach & Set Variable
- If Condition & Fail
Extend
Connect related services and reusable logic to make the pipeline responsive and scalable.
- Logic Apps + Outlook email
- Web Activity validation
- Parameters
- Execute Pipeline / nested pipeline
Lab flow
Create the service, build a first pipeline, explore the user experience, model a company’s data need, and move data from cloud storage to Azure SQL Database.
Control flow
Filter records, retrieve storage blobs, iterate through stored-procedure results, assign variables, branch on a flag, and intentionally raise a controlled pipeline failure.
ADF Setup & Runtimes
Creating ADF instance, Linked Services, Datasets, Azure vs Self-Hosted IR setup.
Copy & Lookup Activities
Copy Data from Blob to SQL, Lookup, Stored Procedure, Get Metadata, and Filter activities.
Control Flow & Logic Apps
ForEach, Set Variable, If Conditions, Fail activities, Web Activity, Logic Apps alerts.
Move from a working pipeline to a production-grade one
Students extend Data Factory into the patterns required for dependable operations, then introduce Databricks as scalable transformation compute.
Trigger, transform, operate & promote.
Trigger the right workload, transform it correctly, see what happened, and move it safely between environments.
Use scheduled jobs, Tumbling Window vs standard Schedule triggers, and blob-creation events.
Mapping Data Flows & Power Query: remove nulls, handle error rows, and shape data.
Monitor pipeline behavior, optimize performance, and multi-file ingestion into Azure SQL.
Prepare Data Factory pipelines for multiple environments for repeatable deployments.
Create Azure Databricks, launch a Spark cluster, transform data using Scala, and develop ETL notebooks.
Parameterize Databricks pipelines and invoke them directly from Azure Data Factory.
Triggers & Data Flows
Tumbling Window, event triggers, Mapping Data Flows, Power Query transformations.
Monitoring & CI/CD
Multi-file ingestion, error row routing, monitoring hubs, ARM templates, environment promotion.
Databricks & Spark ETL
Databricks cluster setup, Scala/Python notebooks, Spark DataFrames, ADF orchestration.
Bring the data platform together with Azure Synapse Analytics
The final technical module connects Lakehouse concepts, serverless analysis, pipeline orchestration, business reporting, and career preparation.
Understand the workspace
- Explore traditional analytics vs Data Lakehouse concepts.
- Dedicated, Serverless & Spark pools.
- Synapse Workspace lab & RBAC permissions.
- Control Node, Compute Node, DMS & sharding patterns.
Analyze the lake
- Overview of SQL Serverless Pools.
- Database and External Tables lab.
- Explore & analyze data with Serverless SQL.
- Build Power BI reports connected to Serverless SQL & Data Lakes.
Transform & monitor
- Overview of Synapse Pipelines and components.
- Transform data with Mapping Data Flows.
- Orchestrate, run & monitor pipelines.
- Quizzes reinforce each technical stage.
Make the work visible
- Power BI basics, DB/Excel connections & publishing.
- CV preparation and sample role explanation.
- Interview-question preparation.
- Explain an end-to-end project with confidence.
Use serverless SQL, external tables, and Data Lake data to investigate information without first moving it into a traditional database.
Build and publish Power BI reports, then use project stories, CV preparation, and interview practice to communicate the work.
Synapse Pools & External Tables
Dedicated vs Serverless SQL pools, querying data lake parquet/csv without loading.
Lakehouse Power BI Reporting
Connecting Power BI directly to Serverless SQL, creating dashboards, and publishing.
End-to-End Capstone & Interviews
Full-stack project walkthrough, CV alignment, DP-203 exam tips, and interview Q&A.
Learn from real architects
Azure Data Architect
Industry practitioner with hands-on experience designing petabyte-scale data lakes and enterprise pipelines on Azure.
You'll learn from Azure Data Architect — no hand-offs, no substitute trainers.
See a real class before you decide
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Watch standard templates and production-grade deployments discussed in class.
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What our students say
"Great learning experience. Concepts are taught patiently with real examples, doubts are cleared in every session, and the projects gave me real confidence for interviews."
"Very good institute for UiPath training. In-depth coverage from basics to REFramework with hands-on practice, and genuine placement guidance throughout."
"Excellent training and real-time knowledge. The sessions are completely practical and the trainer explains every concept with real project scenarios. Best place to learn RPA."
Questions? We have answers
What are the prerequisites for this course?
+Basic knowledge of SQL and general programming concepts is helpful. The course starts with data warehouse fundamentals and SQL essentials before progressing into cloud architecture.
Does this course prepare me for Microsoft certification?
+Yes. The curriculum directly aligns with the skills tested in the DP-203 (Data Engineering on Microsoft Azure) certification exam.
What tools and services will I get hands-on experience with?
+You will work with Azure SQL Database, Azure Blob Storage, Azure Data Lake Gen2, Azure Data Factory, Azure Databricks (Apache Spark), Azure Synapse Analytics, and Microsoft Power BI.
When does the next cohort begin?
+Cohorts run on a 40-day schedule (1 hour per day). Reach out via WhatsApp or submit a request to get the exact start date and schedule.
Enroll only when you're convinced.
Join our live classes for free until you're satisfied — no card, no commitment.