About Course
Hyderabad’s HITEC City and Gachibowli host a growing number of data platform teams. This batch is tuned for learners who want hands-on Azure pipeline experience aligned with enterprise hiring patterns in Telangana.
The programme covers the same Azure Data Engineering curriculum delivered across India, with live online sessions, hands-on labs, and career support tailored for learners in .
Course Overview
This programme takes you from data engineering fundamentals to production-grade Azure pipelines. You will learn SQL and Python for data engineering, build medallion architectures in ADLS Gen2, orchestrate pipelines in Azure Data Factory, transform data with Synapse and Databricks, implement incremental loads and CDC, secure pipelines with RBAC and Key Vault, and deploy real-world projects using CI/CD.
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
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 Hyderabad?
No. SkilBrill's classroom runs only in Chennai. Learners in Hyderabad join the same live online / hybrid cohort taught by the Chennai faculty, with proctored labs and the same placement support.
Which companies hire for these skills in Hyderabad?
Learners in Hyderabad typically target a mix of MNCs and startups such as Microsoft, Deloitte, Cognizant, TCS. The programme's placement support applies equally to Hyderabad learners.
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