Who should learn Azure Data Engineering?

Data professionals, ETL/BI developers, fresh graduates and software engineers wanting to build cloud-native data pipelines on Azure.

Which tools are covered?

Azure Data Factory, Azure Synapse Analytics, Azure Databricks, Delta Lake, Microsoft Fabric (OneLake), Azure Data Lake Gen2 and Purview.

Is the training online or classroom?

Both, plus hybrid. Classroom at Chennai Thoraipakkam; live online and hybrid batches use the same faculty and labs.

Does this prepare me for DP-203?

Yes. The syllabus maps to DP-203 Data Engineer Associate and DP-600 Fabric Analytics Engineer. You get mapped mocks and last-mile revision.

Azure Data Engineering Training

About Course

Overview

Azure Data Engineering Training: Azure Data Engineering Training by SkilBrill is a 16-week hands-on programme covering Azure Data Factory, Synapse, Databricks, Microsoft Fabric, Delta Lake, and Spark — available across India in classroom, live online, and hybrid modes.

⚡ Quick Answer

Duration: 16 weeks
Mode: Classroom / Live online / Hybrid
Tools: Azure Data Factory, Synapse, Databricks, Microsoft Fabric, Delta Lake, Spark, Event Hubs
Certs: DP-203, Databricks Data Engineer Associate, Databricks Data Engineer Professional, DP-600
Prereqs: Basic SQL and Python.
Placement: 200+ hiring partners
Next batch: Rolling — call +91 8610964691
Avg. salary (India): ₹5.5–12 LPA (entry); ₹12–22 LPA (mid); ₹22–35 LPA (senior)

Role	Experience	India CTC (Annual)	Top Hiring Companies
Junior Data Engineer	0–2 yrs	₹5.5 LPA – ₹12 LPA	TCS, Cognizant, Wipro, HCL, Infosys
Azure Data Engineer	2–4 yrs	₹12 LPA – ₹22 LPA	Capgemini, Accenture, Mindtree, LTIMindtree
Senior Data Engineer	5–7 yrs	₹22 LPA – ₹35 LPA	Microsoft, AWS, IBM, Deloitte, EY
Data Engineering Manager / Lead	8–12 yrs	₹35 LPA – ₹65 LPA	PayPal, Freshworks, Zoho, Chargebee
Director of Data / Analytics	12+ yrs	₹65 LPA – ₹1.5 Cr	Citi, JPMorgan, Mastercard, Wells Fargo

Source: Aggregated from Glassdoor, AmbitionBox, LinkedIn Salary, and SkilBrill placement data (Q1–Q3 2026).

Frequently Asked Questions

What is Azure Data Engineering training?

Azure Data Engineering training teaches you how to design, build, and operate data pipelines and data platforms on Microsoft Azure. It covers Azure Data Factory (orchestration), Synapse Analytics (data warehouse), Databricks (lakehouse), Microsoft Fabric (unified SaaS), Delta Lake (storage), Apache Spark (processing), and real-time streaming. SkilBrill's 16-week programme prepares you for Azure Data Engineer and Senior Data Engineer roles.

How much does Azure Data Engineering training cost in India?

Azure Data Engineering training in India costs between ₹30,000 and ₹3,00,000. SkilBrill's 16-week programme is ₹55,000–₹95,000 and covers ADF, Synapse, Databricks, Microsoft Fabric, 3 real capstones, DP-203 and Databricks certification prep, and placement support.

Is Azure Data Engineering a good career in 2026?

Yes — Azure Data Engineering is the most in-demand data career in 2026. India has 25,000+ open Azure data roles. Average salaries grew 28% YoY in 2025. The DP-203 and Databricks Data Engineer certifications are among the most valuable in the data industry. SkilBrill has placed 300+ Azure data engineers in India.

ADF vs Synapse vs Databricks vs Fabric — which to learn?

For 2026, learn all four. ADF is the orchestrator (start here). Synapse is the enterprise data warehouse. Databricks is the lakehouse + ML platform. Microsoft Fabric is the unified SaaS that brings them all together with Power BI. Most enterprises in India use a combination. SkilBrill's 16-week programme covers all four.

How long is Azure Data Engineering training?

Azure Data Engineering training runs 8 to 24 weeks. SkilBrill's programme is 16 weeks covering ADF, Synapse, Databricks, Fabric, Delta Lake, Spark, and 3 real capstones, plus DP-203 and Databricks certification prep.

Step-by-Step Tutorials

Practical guides you will work through during the programme. Each tutorial maps to a lab session.

How to Build a Parameterized Data Factory Pipeline

Create a reusable Azure Data Factory pipeline that copies data from multiple source tables to a data lake using parameters.

Total time: 40 minutes

Create pipeline parameters
In Data Factory, create a new pipeline. Add parameters: sourceTable (string), targetPath (string), runDate (string). These will be passed at runtime.
Configure the source dataset
Create a dataset pointing to your source database. Parameterize the table name using: @dataset().sourceTable. Add the parameter at the dataset level.
Configure the sink dataset
Create a dataset pointing to your data lake. Parameterize the file path using: @concat(dataset().targetPath, '/', dataset().runDate, '.parquet').
Add a Copy activity
Drag a Copy activity to the pipeline. Set the source and sink datasets. Map the pipeline parameters to the dataset parameters in the activity settings.
Create a trigger
Add a tumbling window trigger. Configure it to run daily. Pass windowStart as the runDate parameter value.
Test the pipeline
Click Debug and provide sample parameter values. Verify the file appears in the target path with the correct data.

Certification Paths

These certifications are mapped to the syllabus. You will be exam-ready by the end of the programme.

Microsoft Certified: Data Engineer Associate (DP-203)

Provider
Microsoft

Exam
DP-203

Level
Associate

Fee (approx)
₹4,200

Prep Time
12 weeks

Topics
Data Factory, Synapse Analytics, Databricks, Delta Lake, Stream Processing

Microsoft Certified: Fabric Analytics Engineer Associate (DP-600)

Provider
Microsoft

Exam
DP-600

Level
Associate

Fee (approx)
₹4,200

Prep Time
8 weeks

Topics
Microsoft Fabric, OneLake, Direct Lake, Power BI, Data Pipelines

Azure Data Engineering Interview Questions

These are real interview questions asked by hiring teams at TCS, Cognizant, HCL, and Accenture for Azure Data Engineering roles. Practice them with a peer or mentor.

Explain the medallion architecture (bronze, silver, gold).

Bronze layer: Raw data ingested as-is from source systems. Stored in Delta Lake or Parquet. Immutable, append-only. Silver layer: Cleansed, deduplicated, and conformed data. Business rules applied. Still at row level. Gold layer: Aggregated, business-ready data. Star schemas, fact/dimension tables. Consumed by Power BI and analytics. Each layer adds value and reduces data volume. The pattern provides a clear lineage from raw to report.

Follow-up: How do you handle late-arriving data in the bronze layer?

What is the difference between Delta Lake and Parquet?

Parquet is a columnar file format. It is immutable: once written, you cannot update or delete rows. Delta Lake is built on Parquet but adds a transaction log. This enables ACID transactions, time travel, MERGE/UPDATE/DELETE operations, and schema enforcement. Delta Lake solves the small-file problem with OPTIMIZE and ZORDER. For modern data lakes, Delta is the default choice.

Follow-up: When would you use plain Parquet instead of Delta?

How does Azure Data Factory parameterization work?

Parameters make pipelines reusable. Define parameters at pipeline, dataset, or trigger level. Example: a pipeline that copies files from a source folder to a destination. Instead of hardcoding paths, pass them as parameters. Triggers can pass parameter values (e.g., windowStart for tumbling windows). This lets one pipeline handle multiple tables, dates, or environments without duplication.

Follow-up: How do you pass a parameter from a parent pipeline to a child pipeline?

What is Direct Lake mode in Microsoft Fabric?

Direct Lake is a query mode that reads Delta tables directly from OneLake without caching data in the semantic model. It combines the query performance of import mode with the real-time freshness of DirectQuery. Changes to the Delta table are immediately visible in Power BI. Direct Lake requires Fabric F64+ capacity and works best with optimized Delta tables (V-order, small partitions).

Follow-up: When does Direct Lake fall back to DirectQuery?

How do you handle slowly changing dimensions (SCD)?

SCD Type 1: Overwrite the old value. No history kept. Use for corrections. SCD Type 2: Add a new row with start/end dates. Full history preserved. Most common for dimension tables. SCD Type 3: Add a previous value column. Limited history. In Azure, use Delta Lake MERGE with CDC or Mapping Data Flow SCD transformation. The choice depends on business requirements for audit and restatement.

Follow-up: How do you design a dimension for an employee who changes departments twice in one year?

The Five Myths Cleared About Big Data

Big Data is not about volume. It is about velocity, variety, and veracity. And most companies do not have a big data problem. They have a data quality problem.

Myth 1: You need Hadoop. Reality: Cloud-native data lakes (S3, ADLS) replaced most on-prem Hadoop clusters.
Myth 2: More data is better. Reality: Dirty data poisons models. Focus on data quality first.
Myth 3: Real-time is always best. Reality: Batch is cheaper and sufficient for 80% of use cases.
Myth 4: Data engineers are just ETL developers. Reality: They design architectures, optimize costs, and bridge analytics to ML.
Myth 5: You need a data scientist to get value. Reality: Basic dashboards and reports deliver 90% of business value.

Join SkilBrill's Azure Data Engineering Training — a comprehensive programme covering the tools, concepts and real-world projects that employers look for. The master course is delivered through live online sessions with weekday, weekend and evening batch options.

Course Overview

Who Should Join This Course

Fresh graduates targeting data engineering roles
Software developers transitioning to data engineering
Database administrators moving to cloud data platforms
Data analysts looking to build production pipelines
Professionals preparing for Azure data engineering interviews

Prerequisites

Basic computer literacy
Fundamental understanding of databases and SQL
Basic Python programming is helpful but not mandatory
Willingness to work on daily hands-on labs

Learning Objectives

Design end-to-end data pipelines on Azure
Ingest, store, transform, and serve data using Azure Data Factory, Synapse, Databricks, and Fabric
Implement incremental loading, CDC, and metadata-driven pipelines
Build star/snowflake schemas and manage slowly changing dimensions
Secure pipelines with RBAC, Managed Identity, and Azure Key Vault
Monitor, troubleshoot, optimize performance and cost
Deploy production-grade architectures with Git, CI/CD, and DevOps
Crack Azure data engineering interviews with real projects

96 hrs across 14 modules (119 topics)

Tools and Technologies

Azure Portal, Azure Data Lake Storage Gen2, Azure Data Factory, Azure Synapse Analytics, Azure Databricks, Azure Event Hubs, Apache Spark, Microsoft Fabric, OneLake, Azure DevOps, Azure Key Vault, Azure Monitor, Git, VS Code, SQL Server Management Studio / Azure Data Studio, Python / PySpark, Terraform / Bicep

Career Opportunities After This Programme

Azure Data Engineer
Data Engineer
Cloud Data Engineer
Data Platform Engineer
ETL Developer
Analytics Engineer
BI Developer
Data Architect

Hands-on Labs

Hands-on lab on Data Engineering Foundations and Architecture — build and run a working example covering Introduction to Data Engineering and the Modern Data Stack, Data Pipeline Components: Ingestion, Storage, Transformation, Serving, Batch vs Streaming vs Lambda Architecture, Cloud Data Engineering on Azure: Overview and Roadmap.
Hands-on lab on SQL for Data Engineering — build and run a working example covering Advanced SQL: Joins, Window Functions, CTEs, Data Cleaning and Transformation with SQL, Indexing, Partitioning, and Query Optimization, SQL Interview Patterns for Data Engineers.
Hands-on lab on Python for Data Engineering — build and run a working example covering Python Essentials for Data Engineering, Working with APIs, Files, and Pandas, PySpark Basics and DataFrame Operations, Python Interview Patterns and Coding Practice.
Hands-on lab on Azure Data Lake Storage Gen2 — build and run a working example covering ADLS Gen2 Architecture and Hierarchical Namespace, Container, Directories, and File Management, Security: ACLs, RBAC, and Shared Access Signatures, Integrating ADLS Gen2 with ADF and Databricks.
Hands-on lab on Azure Data Factory — build and run a working example covering ADF Pipelines, Datasets, Linked Services, and Integration Runtime, Data Movement and Transformation Activities, Parameterization, Dynamic Expressions, and Metadata, Incremental Load and CDC Patterns in ADF.
Hands-on lab on Azure Synapse Analytics — build and run a working example covering Synapse Workspace, SQL Pools, and Spark Pools, Dedicated SQL Pool: Tables, Indexing, and Distribution, Synapse Pipelines and Data Flows, Serverless SQL and External Tables over Data Lake.
Hands-on lab on Azure Databricks and Apache Spark — build and run a working example covering Databricks Workspace, Clusters, and Notebooks, Spark Architecture: RDDs, DataFrames, and Catalyst Optimizer, Delta Lake: ACID, Time Travel, and Optimize, Databricks Unity Catalog and Governance.
Hands-on lab on Microsoft Fabric, Data Warehousing, and Data Modelling — build and run a working example covering Microsoft Fabric: Lakehouse, Warehouse, and OneLake, Data Warehouse Design: Star Schema, Snowflake Schema, Slowly Changing Dimensions and Surrogate Keys, Dimensional Modeling Best Practices.
Hands-on lab on ETL, ELT, CDC, and Metadata-Driven Pipelines — build and run a working example covering ETL vs ELT: When to Use Which, Change Data Capture with Azure SQL and Debezium Patterns, Metadata-Driven Pipeline Design, Incremental Data Loading Strategies.
Hands-on lab on Batch and Streaming Data Engineering — build and run a working example covering Batch Processing Patterns and Scheduling, Streaming Concepts: Windowing, Watermarking, Checkpointing, Azure Event Hubs: Producers, Consumers, and Partitions, Spark Structured Streaming on Azure.
Hands-on lab on Security, Governance, Quality, and Observability — build and run a working example covering Azure RBAC, Managed Identity, and Azure Key Vault, Data Security: Encryption, Masking, and PII Handling, Data Quality Frameworks and Great Expectations, Data Observability and Governance with Microsoft Purview.
Hands-on lab on DevOps, Monitoring, Performance, and Cost Optimization — build and run a working example covering Git, Branching Strategies, and Code Reviews, CI/CD with Azure DevOps for Data Pipelines, Pipeline Monitoring, Alerting, and Troubleshooting, Performance Tuning and Cost Optimization on Azure.
Hands-on lab on Real-World Projects and Production Architecture — build and run a working example covering Retail Data Engineering Pipeline: End-to-End Build, Customer 360 Data Platform: Design and Implementation, Real-Time IoT Data Pipeline with Event Hubs and Spark, Metadata-Driven Enterprise Pipeline Framework.
Hands-on lab on Interview Preparation and Career Development — build and run a working example covering Azure Data Engineering Interview Question Bank, SQL and Python Interview Rounds, Project Explanation and Portfolio Building, Resume, LinkedIn, and Job Search Strategy.

Real-World Projects

Retail Data Engineering Pipeline

Business Problem: A retail chain needs to consolidate sales, inventory, and customer data from multiple stores into a single analytics platform to enable demand forecasting and inventory optimization.

Architecture: Azure Data Lake Storage Gen2 (bronze/silver/gold) → Azure Data Factory (ingestion/orchestration) → Azure Synapse Analytics (SQL pool for reporting) → Power BI dashboards.

Data Sources: POS transactional files, ERP inventory exports, CRM customer profiles, web clickstream logs.

Ingestion: ADF pipelines ingest daily batch files and incremental changes using metadata-driven triggers.

Storage: Bronze layer stores raw files; Silver layer stores cleansed Parquet/Delta tables; Gold layer hosts star-schema aggregates.

Transformation: PySpark in Azure Databricks for cleansing, deduplication, SCD Type 2, and aggregate calculations.

Orchestration: Azure Data Factory with CI/CD in Azure DevOps and parameterized, metadata-driven pipelines.

Security: Azure RBAC, Managed Identity, Azure Key Vault for secrets, and column-level encryption for PII.

Monitoring: Azure Monitor, ADF pipeline alerts, and Databricks cluster logging.

Analytics: Power BI reports for sales trends, inventory turnover, and customer segmentation.

Expected Output: A production-ready medallion architecture pipeline that serves clean, reliable retail analytics.

Skills Demonstrated: Data lake design, ETL/ELT, PySpark, ADF orchestration, dimensional modeling, security, monitoring.

Interview Explanation: Explain the medallion architecture, why you chose Parquet/Delta, how you handled incremental loads, and how you secured PII.

Customer 360 Data Platform

Business Problem: A bank wants a unified customer profile by merging data from core banking, mobile app, and support channels to improve personalization and cross-sell.

Architecture: Event Hubs → Azure Data Lake Gen2 → Databricks (entity resolution and identity stitching) → Synapse Analytics → Power BI / customer APIs.

Data Sources: Core banking records, CRM tickets, mobile app events, call-center logs.

Ingestion: Streaming ingestion via Event Hubs for app events; batch ingestion for core systems.

Storage: Data Lake Gen2 zones with Delta Lake for change history and audit.

Transformation: Identity stitching, data quality rules, deduplication, and golden-record generation in Databricks.

Orchestration: ADF pipelines orchestrated by Databricks jobs and Azure DevOps release gates.

Security: Key Vault for credentials, tokenization of PII, and RBAC at storage and database levels.

Monitoring: Data quality dashboards, pipeline run history, and data lineage tracking.

Analytics: 360-degree customer dashboards and API endpoints for marketing systems.

Expected Output: A trusted golden customer record with lineage, quality scoring, and real-time updates.

Skills Demonstrated: Entity resolution, streaming + batch integration, Delta Lake, data governance, API serving.

Interview Explanation: Describe how you resolved identities, ensured data quality, and served both analytics and operational use cases.

Real-Time IoT Data Pipeline

Business Problem: A manufacturing firm needs to ingest telemetry from thousands of sensors, detect anomalies, and trigger maintenance alerts in near real time.

Architecture: IoT devices → Azure Event Hubs → Stream processing (Spark Structured Streaming in Databricks) → Delta Lake → Synapse / Power BI alerting.

Data Sources: MQTT/HTTP telemetry streams from factory sensors and edge gateways.

Ingestion: Event Hubs captures streaming events with partitioning and checkpointing.

Storage: Hot path writes to Delta Lake; aggregated metrics stored in Synapse for reporting.

Transformation: Windowed aggregations, anomaly detection with thresholds, and enrichment from asset master data.

Orchestration: Databricks jobs with ADF for batch backfill and DevOps for deployment.

Security: Managed Identity access to Event Hubs and storage; SAS tokens rotated via Key Vault.

Monitoring: Streaming query metrics, latency alerts, and dead-letter monitoring.

Analytics: Real-time dashboards for OEE, anomaly counts, and predictive maintenance triggers.

Expected Output: A low-latency pipeline that ingests, processes, and alerts on IoT telemetry.

Skills Demonstrated: Stream processing, Event Hubs, Spark Structured Streaming, Delta Lake, real-time alerting.

Interview Explanation: Explain partitioning, checkpointing, watermarking, and how you handled late-arriving data.

Metadata-Driven Enterprise Pipeline

Business Problem: A multinational enterprise has dozens of source systems and needs a scalable way to onboard new data sources without rebuilding pipelines each time.

Architecture: Metadata catalog → ADF dynamic pipelines → Data Lake Gen2 → Databricks/Synapse → enterprise data warehouse and reporting.

Data Sources: SQL databases, REST APIs, flat files, SaaS connectors across business units.

Ingestion: Metadata-driven ADF pipelines read source definitions from a control table and generate ingestion logic dynamically.

Storage: Standardized landing, raw, curated, and consumption zones in Data Lake Gen2.

Transformation: Dynamic notebooks apply rules stored in metadata (cleansing, type mapping, SCD handling).

Orchestration: ADF master pipeline orchestrates child pipelines; Git and Azure DevOps manage version control and releases.

Security: Service principals, Key Vault, and row-level security in the consumption layer.

Monitoring: Centralized logging, pipeline lineage, failure alerting, and SLA dashboards.

Analytics: Enterprise BI reports and self-service datasets powered by the curated layer.

Expected Output: A configurable framework that onboards new sources in hours rather than weeks.

Skills Demonstrated: Metadata-driven design, dynamic ADF, data governance, CI/CD, scalable architecture.

Interview Explanation: Explain the metadata model, how you abstracted source changes, and the CI/CD strategy for pipeline updates.

Career Roadmap

Role Progression

Azure Data Engineer
Data Engineer
Cloud Data Engineer
Data Platform Engineer
ETL Developer
Analytics Engineer
BI Developer
Data Architect

Step-by-Step Learning Path

Master Data Engineering Foundations and Architecture
Master SQL for Data Engineering
Master Python for Data Engineering
Master Azure Data Lake Storage Gen2
Master Azure Data Factory
Master Azure Synapse Analytics
Master Azure Databricks and Apache Spark
Master Microsoft Fabric, Data Warehousing, and Data Modelling
Master ETL, ELT, CDC, and Metadata-Driven Pipelines
Master Batch and Streaming Data Engineering
Master Security, Governance, Quality, and Observability
Master DevOps, Monitoring, Performance, and Cost Optimization
Master Real-World Projects and Production Architecture
Master Interview Preparation and Career Development
Build portfolio projects covering Retail Data Engineering Pipeline, Customer 360 Data Platform, Real-Time IoT Data Pipeline, Metadata-Driven Enterprise Pipeline
Prepare for technical interviews and update your resume and portfolio

Course Duration, Mode and Certification

The course runs for 16 weeks with 3 sessions per week (2 hours per session), totalling 96 classroom hours. Classroom, online, and hybrid modes are available where supported. You will receive a SkilBrill completion certificate and work on real-world projects, with dedicated interview preparation support at the end of the programme.

Why Train at SkilBrill

SkilBrill Training Institute focuses on making you employable. Trainers are working professionals, labs mirror real production environments, and the placement cell connects eligible candidates with hiring companies. Enquire today for the next batch start date and fee details.

Career and Job Support

SkilBrill provides practical career support to help you move from training into relevant roles. Services include:

Resume assistance
LinkedIn profile guidance
GitHub and project portfolio guidance
Technical interview preparation
Mock interviews
SQL interview preparation
Python interview preparation
Azure interview preparation
Project explanation preparation
Job-search guidance
Career roadmap
Placement assistance

Resume Building Guidance

Keep your resume to one page if you have less than five years of experience.
Use a clean format with clear sections: contact, objective, skills, projects, education and certifications.
Tailor your skills section to match the job description.
Quantify achievements where possible, for example "reduced regression time by 30%".
Include links to your GitHub, LinkedIn and portfolio if available.
Proofread carefully; spelling and grammar errors create a poor impression.

LinkedIn Profile Optimisation

Use a professional headshot and a headline that includes your target role.
Write a summary that highlights your skills, projects and career goals.
List relevant tools, frameworks and certifications in the Skills section.
Share posts or articles about what you are learning to show activity.
Connect with trainers, classmates and professionals in your target domain.
Request recommendations from mentors or project reviewers.

GitHub and Portfolio Guidance

Create well-named repositories for each major project.
Add a README file explaining the project, technologies, setup steps and screenshots.
Use meaningful commit messages and keep code organised.
Include a portfolio website or GitHub profile readme that links to your best work.
Keep sensitive data like passwords and API keys out of public repositories.
Regularly update repositories with improvements and new projects.

Job Search Strategy

Update your resume and LinkedIn profile before applying.
Apply to roles on LinkedIn, Naukri, Indeed and company career pages.
Customise each application to match the job description.
Prepare a short elevator pitch for phone screenings.
Practise technical and behavioural questions daily.
Follow up politely after interviews and ask for feedback.
Attend meetups, webinars and networking events in your domain.

Frequently Asked Questions

What is Azure Data Engineering?

Azure Data Engineering is the practice of designing, building, and maintaining scalable data pipelines and analytics solutions using Microsoft Azure services such as Azure Data Factory, Azure Synapse Analytics, Azure Databricks, and Microsoft Fabric.

What are the prerequisites for the Azure Data Engineering course?

You need basic computer literacy, a fundamental understanding of databases and SQL, and a willingness to work on hands-on labs. Basic Python knowledge is helpful but not mandatory.

Will I learn SQL and Python in this course?

Yes. The programme includes dedicated modules on SQL for Data Engineering and Python for Data Engineering, focused on the patterns used in production pipelines.

How long is the Azure Data Engineering course?

The programme runs for 16 weeks with three sessions per week (2 hours each), totalling 96 classroom hours including labs, assignments and projects. Weekday, weekend, and evening batches are available in online, classroom, and hybrid modes.

What Azure services will I work on?

You will work with Azure Data Lake Storage Gen2, Azure Data Factory, Azure Synapse Analytics, Azure Databricks, Azure Event Hubs, Azure Key Vault, Azure Monitor, Microsoft Fabric, and OneLake.

Does the course include real-world projects?

Yes. You will build projects such as a Retail Data Engineering Pipeline, a Customer 360 Data Platform, a Real-Time IoT Data Pipeline, and a Metadata-Driven Enterprise Pipeline.

What is the course mode?

The master programme is delivered online. City pages also offer classroom and hybrid options where supported, with live trainers and the same hands-on labs.

Are there any course fees?

Please contact SkilBrill for the latest fee structure and any available instalment options.

Will I get placement support?

Yes. Eligible learners receive resume assistance, LinkedIn and GitHub guidance, mock interviews, technical interview preparation, and placement assistance.

Does the course prepare me for Azure certifications?

The curriculum is aligned with Azure data engineering concepts and helps you prepare for interviews and certification exams such as DP-203, but certification exam costs are not included.

What career opportunities can I pursue after this course?

Graduates can apply for roles such as Azure Data Engineer, Data Engineer, Cloud Data Engineer, Data Platform Engineer, ETL Developer, Analytics Engineer, BI Developer, and Data Architect.

How is this different from Data Science or Data Analytics?

Data Engineering focuses on building reliable data pipelines, storage, and processing infrastructure. Data Science focuses on modeling and prediction, while Data Analytics focuses on insights and reporting.

Real-World Projects

Capstone Project

Build an end-to-end lakehouse on Azure: ingest raw CSV/JSON into Data Lake Gen2 (bronze), transform via ADF and Databricks into Delta tables (silver), and serve a star schema in Synapse/Fabric Direct Lake (gold) with CI/CD via Azure DevOps.

Production Scenario

Diagnose and resolve a slow Databricks job caused by small-file problem in a Delta table, then optimize with OPTIMIZE + ZORDER and MERGE schema evolution.