Azure Data Engineering Training in Chennai & Certification Course (96 Hours Master Program)

About Course

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

Every module includes guided labs on the Azure portal, Databricks notebooks, Synapse SQL pools, and Data Factory pipelines. You will ingest real datasets, build transformations, schedule orchestrations, and troubleshoot failures in a live Azure environment.

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.

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 convert your Azure Data Engineering skills into job offers. 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

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.

Is classroom training available in Chennai?

Yes. SkilBrill runs classroom batches in Chennai, alongside live online and hybrid modes — all three follow the same syllabus, labs and faculty.

Which companies hire for these skills in Chennai?

Learners in Chennai typically target a mix of MNCs and startups such as TCS, Cognizant, HCL, Wipro, Capgemini, Infosys. The programme's placement support applies equally to Chennai learners.

Related Courses

Azure Data Engineering Training

Azure Training in Chennai

Microsoft Fabric Training in Chennai

Cloud Computing

Data Science

Python Programming

Why Choose SkilBrill for Azure Data Engineering Training in Chennai?

SkilBrill’s Chennai centre sits on the OMR (Old Mahabalipuram Road) — the same IT corridor that hosts TCS Sholinganallur, Cognizant OMR, HCL Siruseri, Wipro MEPZ and Infosys Mahindra World City. That location is not a coincidence: our students learn inside the same IT ecosystem where they will be hired. Class batches are scheduled around Chennai’s traffic patterns, with early-morning and evening slots that work for working professionals in Sholinganallur, Siruseri, Thoraipakkam, Perungudi, Pallavaram, Tambaram, Guindy, and Velachery.

The Azure Data Engineering Training in Chennai programme is designed with input from hiring managers at the companies below, so the curriculum and capstone projects match what is being asked in real Chennai interviews this quarter.

Chennai companies that hire Data Engineering talent

• Cognizant OMR
• TCS Sholinganallur
• Wipro MEPZ
• HCL Siruseri
• Infosys Mahindra World City
• Capgemini Siruseri
• PayPal Chennai
• Freshworks Chennai

How to reach our Thoraipakkam centre

Our Chennai classroom is at No 22, 200 Feet Radial Road, Thoraipakkam, Chennai 600097 (latitude 12.938565, longitude 80.237708). The centre is a 5-minute walk from Thoraipakkam Metro Station on the Blue Line, with ample two-wheeler and car parking on site. Outstation learners can reach us in 25 minutes from Chennai International Airport via OMR, or 20 minutes from Chennai Central by MRTS and Metro.

For learners in other cities — Bangalore, Hyderabad, Coimbatore, Madurai, Kochi, Mumbai, Pune, Delhi, Gurgaon, Noida, Ahmedabad, Chandigarh, Trichy, Trivandrum — the same programme is available in live-online mode, with the same faculty, same lab access, and same placement support as the Chennai classroom.

Real Capstone Projects You Will Build

Every Chennai learner completes three capstone projects that mirror real production work, not toy demos. You will defend your project in a panel review with a working industry professional — the same format used in real hiring loops.

1. Capstone 1. Capstone 1: Build a medallion architecture (Bronze/Silver/Gold) on Azure Data Lake Gen2 using ADF and Synapse Spark pools for a Chennai fintech — process 5 years of transaction data and produce daily KPI dashboards in Power BI.
2. Capstone 2. Capstone 2: Design a real-time clickstream analytics pipeline using Event Hubs, Databricks Structured Streaming, and Delta Lake, then serve the gold layer via Synapse Serverless SQL.
3. Capstone 3. Capstone 3: Implement a complete ELT pipeline with dbt on Databricks + Microsoft Fabric, with Great Expectations data-quality tests, Purview catalog, and CI/CD in Azure DevOps.

Certifications You Will Be Ready For

The Azure Data Engineering Training in Chennai programme prepares you for the following industry-recognised certifications. SkilBrill covers the syllabus end-to-end and includes exam-prep mock tests, last-mile revision sessions, and a discount voucher where the vendor allows it.

Certification	Approx. Cost	How SkilBrill Prepares You
DP-203: Azure Data Engineer Associate Microsoft · Associate	₹4,200	Classroom + online mock exams, study plan, doubt-clearing, and last-mile revision.
Databricks Certified Data Engineer Associate Databricks · Associate	₹7,500	Classroom + online mock exams, study plan, doubt-clearing, and last-mile revision.
Databricks Certified Data Engineer Professional Databricks · Professional	₹15,000	Classroom + online mock exams, study plan, doubt-clearing, and last-mile revision.
Microsoft DP-600 Fabric Analytics Engineer Associate Microsoft · Associate	₹4,200	Classroom + online mock exams, study plan, doubt-clearing, and last-mile revision.

Exam fees are separate from the SkilBrill course fee. The SkilBrill certificate of completion is awarded on every successful cohort.

Top 25 Azure Data Engineering Training in Chennai Interview Questions and Answers (2026)

These are the questions Chennai hiring managers are asking right now in interviews for Data Engineering roles. Practise them out loud before every interview — the answers below are what experienced interviewers expect to hear.

Q1. What is Azure Data Factory?

Azure Data Factory (ADF) is a cloud-based ETL and data integration service. It allows you to compose data-driven workflows (pipelines) that ingest, transform, and load data from 90+ sources into Azure data stores.

Q2. What is the difference between Azure Data Factory and Synapse Pipelines?

Synapse Pipelines is essentially ADF embedded inside Synapse Analytics. It uses the same drag-and-drop authoring and 90+ connectors. The advantage of Synapse is the tight integration with serverless SQL pools and dedicated SQL pools.

Q3. What is Microsoft Fabric?

Microsoft Fabric is a unified analytics platform that brings together Synapse, Data Factory, Power BI, and OneLake into a single SaaS experience. It uses Delta Lake and OneLake as the data foundation.

Q4. Explain the medallion architecture.

The medallion architecture is a data design pattern with three layers: Bronze (raw, append-only, source data), Silver (cleaned, deduplicated, conformed), and Gold (curated, business-level, aggregated). It is widely used in Databricks and Microsoft Fabric.

Q5. What is Delta Lake?

Delta Lake is an open-source storage layer that brings ACID transactions, schema enforcement, time travel, and unified batch + streaming to Apache Spark. It is the default storage format in Databricks and Microsoft Fabric.

Q6. What is the difference between batch and stream processing?

Batch processing handles data in large, scheduled chunks (every hour, every night). Stream processing handles data event-by-event in near real time (sub-second to sub-minute latency). Streaming is needed for fraud detection, IoT, and clickstream analytics.

Q7. How do you handle slowly changing dimensions (SCD) in ADF?

Use a Mapping Data Flow with a SCD transformation. Choose Type 1 (overwrite), Type 2 (historical, with effective dates and surrogate key), or Type 3 (limited history in additional columns). Databricks Delta Lake offers MERGE INTO for SCD Type 2 in code.

Q8. What is Azure Data Lake Storage Gen2?

ADLS Gen2 is a hierarchical file system built on top of Azure Blob Storage, optimized for big-data workloads. It provides low-cost tiered storage, POSIX-compliant ACLs, and is the data lake foundation for ADF, Synapse, and Databricks.

Q9. What is the difference between serverless SQL pool and dedicated SQL pool in Synapse?

Serverless SQL pool is pay-per-query (by data processed) and is used for ad-hoc exploration and reporting on files in ADLS. Dedicated SQL pool (formerly SQL DW) is a provisioned MPP database for enterprise data warehousing with predictable performance.

Q10. What is a Spark cluster in Azure Databricks?

A Spark cluster is a set of compute nodes that run the Spark engine. Databricks supports all-purpose clusters (interactive) and job clusters (ephemeral, for production jobs). Choose instance types based on workload: memory-optimized for joins, compute-optimized for ETL, GPU for ML.

Q11. What is Unity Catalog in Databricks?

Unity Catalog is Databricks’ unified governance solution. It provides a single place to manage data assets, permissions, lineage, and audit across workspaces. It enforces fine-grained access control at the table, column, and row level.

Q12. How do you secure secrets in Azure Data Factory?

Use Azure Key Vault to store secrets and link them to ADF via a Key Vault connection. Reference the secret inside a Web activity or as a connection password. Never hardcode secrets in linked services or pipelines.

Q13. What is a tumbling window trigger in ADF?

A tumbling window trigger runs a pipeline on a fixed time window (e.g. hourly, daily) for a contiguous series of windows. It is the recommended pattern for reliable backfill and rerun of failed windows.

Q14. How do you implement incremental loading in ADF?

Use a watermark column (e.g. LastModifiedDate) or a change-data-capture flag. The pipeline reads the high-water mark from a control table, filters source rows greater than the watermark, loads the delta, and updates the watermark. ADF also supports Change Data Capture (CDC) in Mapping Data Flows.

Q15. What is the difference between a pipeline and a data flow in ADF?

A pipeline orchestrates activities (Copy, Databricks, Web, Stored Procedure) and is free of charge. A data flow is a visual Spark engine that runs transformations on a Spark cluster and is billed per vCore-hour. Use pipelines for orchestration; use data flows for visual transformations.

Q16. What is the difference between partitioning and bucketing in Spark?

Partitioning splits data into directories by a column value (e.g. year=2026/month=08/day=18). Bucketing distributes data into a fixed number of files by a hash of a column. Partitioning speeds up filtering; bucketing speeds up joins on the bucketed column.

Q17. How do you handle late-arriving data in streaming pipelines?

Use windowed aggregation with a watermark (allowed lateness) in Structured Streaming. For very late data, write to a quarantine Delta table and reconcile in a separate batch job. The choice depends on the business SLAs for the late data.

Q18. What is a star schema vs a snowflake schema?

In a star schema, a central fact table joins directly to denormalized dimension tables. In a snowflake schema, dimensions are normalized into multiple related tables. Star is simpler and faster for queries; snowflake saves storage and is more normalized.

Q19. How do you choose between ADF and Databricks for a transformation?

Use ADF for orchestration, simple transformations, copy activity, and integration with the rest of Azure. Use Databricks for complex transformations, Python/Scala code, ML, and large-scale Spark jobs. Most production architectures use ADF for orchestration and Databricks for transformation.

Q20. What is a data lakehouse?

A data lakehouse is a hybrid architecture that combines the low-cost storage and flexibility of a data lake with the ACID transactions, schema enforcement, and query performance of a data warehouse. Delta Lake, Apache Iceberg, and Apache Hudi are the leading open table formats.

Q21. How do you monitor data pipeline failures?

Use Azure Monitor + Log Analytics to capture ADF activity runs, Spark logs, and Databricks job runs. Set up alerts on failure rates, durations, and data quality checks. Build a dashboard that surfaces failed activities, retry counts, and SLA breaches.

Q22. What is the role of Purview in Azure data engineering?

Microsoft Purview is the unified data governance solution. It catalogs data assets across ADF, Synapse, Databricks, and Power BI, enforces access policies, and tracks data lineage. Use Purview to discover sensitive data and meet compliance requirements.

Q23. How do you test data pipelines?

Use a layered testing strategy: unit tests on transformation logic (pytest + chispa for Spark), integration tests on the pipeline (Assert activity in ADF), data quality tests (Great Expectations, Soda, dbt tests), and end-to-end tests on business KPIs.

Q24. What is the difference between ETL and ELT?

ETL transforms data before loading into the destination (typical for traditional data warehouses). ELT loads raw data first and transforms inside the destination (typical for cloud data warehouses and lakehouses with cheap compute). Modern architectures favor ELT.

Q25. How do you estimate cost for an Azure data engineering solution?

Cost components: ADF (per activity run and per Data Flow vCore-hour), Synapse SQL pool (per DWU-hour, paused when idle), Databricks (per DBU-hour), storage (per GB-month). Use the Azure Pricing Calculator and add buffer for development, testing, and DR.

Ready to start Azure Data Engineering Training in Chennai?

Next Chennai batch starts soon. Seats are limited to 18 per batch to keep quality high. Talk to a counsellor, share your background, and we will help you pick the right batch and the right mode (classroom / live online / hybrid).

📞 Call +91 8610964691 💬 WhatsApp Us 📩 Enroll Online

Visit us: No 22, 200 Feet Radial Road, Thoraipakkam, Chennai 600097, Tamil Nadu, India. Open Monday to Saturday, 9:00 AM to 7:00 PM.