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Azure Data Engineer syllabus in Hyderabad
The Azure Data Engineer course syllabus covers SQL, Python, data warehousing, ETL/ELT, Azure Data Factory, ADLS Gen2, Databricks with PySpark, Delta Lake, Synapse, Microsoft Fabric and Power BI. The course runs about two to three months, includes hands-on projects, and prepares you for the DP-700 certification (which replaced the retired DP-203).
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Table of Contents
Why Read the Syllabus First?
Before joining any course, read the syllabus end to end. It tells you:
- What you will learn and in what order.
- Which tools are covered.
- Whether it is up to date (DP-700 and Microsoft Fabric, not the retired DP-203).
- What projects you will build.
Syllabus Overview: 5 Layers
- Foundations – SQL, Python, cloud and Azure basics
- Data concepts – data warehousing, data modelling, ETL/ELT
- Core Azure services – ADLS Gen2, Data Factory, Databricks, Synapse
- Modern platform – Microsoft Fabric and Power BI
- Job readiness – projects, DP-700 preparation, interviews
Module 1: Azure and Cloud Fundamentals
- IaaS, PaaS, SaaS and the shared-responsibility model
- Azure portal, subscriptions, resource groups, regions
- Storage accounts and basic networking
- Microsoft Entra ID (identity) and cost management
Module 2: SQL for Data Engineering
- Joins, aggregations and GROUP BY
- Window functions and CTEs
- Stored procedures
- Indexing basics and query tuning
- T-SQL as used in Synapse and Fabric warehouses
Module 3: Python for Data Engineers
- Data types, loops, functions, error handling
- Working with files, JSON and APIs
- pandas for data handling
- Introduction to PySpark
Module 4: Data Warehousing and Data Modelling
- OLTP vs OLAP
- Fact and dimension tables
- Star and snowflake schema
- Slowly Changing Dimensions (SCD)
- Normalisation vs denormalisation
- Lakehouse pattern
Module 5: ETL and ELT
- When to use ETL vs ELT
- Full vs incremental loads
- Change data capture (CDC)
- Making pipelines safe to re-run
- Error handling and logging
Module 6: Azure Data Factory
- Pipelines, activities, linked services and datasets
- Copy activity and Mapping Data Flows
- Triggers and scheduling
- Integration runtimes
- Parameters and metadata-driven pipelines
- Monitoring and alerts
Module 7: Azure Data Lake Storage Gen2
- Hierarchical namespace, containers and folders
- Access tiers (hot, cool, archive)
- Security with RBAC and ACLs
- Medallion folders: Bronze, Silver, Gold
- File formats: CSV, Parquet, Delta
Module 8: Azure Databricks, Spark and Delta Lake
- Workspace, clusters and notebooks
- PySpark DataFrames and transformations
- How Spark works: driver, executors, partitions, shuffles
- Performance tuning and caching
- Delta Lake: ACID, schema enforcement, time travel, MERGE
- Jobs and scheduling
Module 9: Azure Synapse Analytics
- Serverless and dedicated SQL pools
- Spark pools
- Synapse pipelines
- Connecting to the data lake and Power BI
Many companies still run analytics on Synapse, so it is still a useful job skill.
Module 10: Microsoft Fabric
- OneLake and shortcuts
- Lakehouse and Warehouse
- Fabric pipelines and Dataflows Gen2
- Spark notebooks
- Real-Time Intelligence with KQL
- Security and workspace roles
Module 11: Power BI for Data Engineers
- Semantic models (datasets) and relationships
- Connecting to Synapse and Fabric
- Import, DirectQuery and Direct Lake
- Basic report building
Real-Time Projects
- Batch pipeline: source → ADLS Gen2 → Databricks → warehouse.
- Medallion lakehouse: Bronze, Silver and Gold layers with Delta Lake.
- Streaming project: real-time data with Fabric or Spark.
- End-to-end Fabric project: ingestion to Power BI report.
- Capstone: one full project you can explain in interviews.
Certification Preparation
- DP-203 retired on 31 March 2025 – don’t study for it.
- DP-700 (Fabric Data Engineer Associate) is the main goal.
- DP-900 and AZ-900 are optional starting points.
- DP-600 is an optional next step for analytics engineering.
Check Microsoft Learn for the latest exam guide.
Who Is This Syllabus For?
- Beginners: spend extra time on SQL and Python before Azure tools.
- Freshers: add more projects and interview preparation.
- Working professionals: weekend or evening batches; focus on ADF, Databricks and Fabric; map your current SQL/ETL skills to Azure.
Skills You Will Have After the Course
- Write good SQL and Python.
- Design data models and lakehouses.
- Build pipelines in Azure Data Factory and Fabric.
- Process big data with Spark in Databricks.
- Secure, monitor and tune data solutions.
- Explain your projects confidently in interviews.
Conclusion
A good Azure Data Engineer syllabus builds skills in the right order: basics, data concepts, Azure services, Fabric and projects, and prepares you for DP-700. See how this runs as a course in our Azure Data Engineer Training in Hyderabad, or follow the Azure Data Engineer roadmap.
Frequently Asked Questions
Is DP-203 still available?
No. DP-203 was retired on 31 March 2025. Target DP-700 (Microsoft Fabric Data Engineer Associate) instead.
How long is the Azure Data Engineer course?
About two to three months of regular classes plus hands-on practice.
Do I need coding knowledge before joining?
No. The syllabus starts with SQL and Python basics.
Does the syllabus include Microsoft Fabric?
Yes. Fabric is covered along with Data Factory, Databricks and Synapse.
Are real projects included?
Yes. Batch, lakehouse, streaming and end-to-end Fabric projects are part of the course.
