Brolly Academy | Azure Data Engineering
Azure Data Engineer Roadmap
2026: A 6-Month Step-by-Step Plan for Beginners
The Azure data engineer roadmap has 10 steps: SQL, Python, Azure basics, Data Factory, Databricks, Fabric, DevOps, projects, DP-700 and interviews. A beginner who studies 2–3 hours a day can become job-ready in about 6 months.
Azure data engineer roadmap for 2026: 10 steps from SQL and Python to Azure Data Factory, Databricks, Fabric, projects and DP-700, with a 6-month study plan.
By Dinesh, Azure Data Engineering Trainer (9+ years) · Brolly Academy · Updated 20 October 2026
Table of Contents
1. What does an Azure data engineer do?
2. Azure data engineer roadmap at a glance
3. Step 1: Learn data basics and SQL
4. Step 2: Learn Python and PySpark basics
5. Step 3: Learn Azure fundamentals and storage
6. Step 4: Learn Azure Data Factory
7. Step 5: Learn Azure Databricks, Spark and Delta Lake
8. Step 6: Learn Microsoft Fabric and Azure Synapse
9. Step 7: Learn DevOps, governance and monitoring
10. Step 8: Build Azure data engineering projects
11. Step 9: Get certified with DP-700
12. Step 10: Prepare your resume and interviews
13. 6-month Azure data engineer study plan
Quick answer
To become an Azure data engineer, learn SQL and Python first, then Azure storage, Azure Data Factory, Databricks with PySpark and Microsoft Fabric. Build two or three end-to-end projects, take the DP-700 exam, and prepare for interviews. With 2–3 hours of study a day, most beginners finish this Azure data engineer roadmap in about 6 months.
Key takeaways
- Start with SQL: it is the skill tested most in every Azure data engineer interview.
- Core Azure tools: ADLS Gen2, Azure Data Factory, Azure Databricks and Microsoft Fabric.
- Timeline: about 6 months for a beginner, 3–4 months for someone who already knows SQL or ETL.
- Certification: DP-203 retired in March 2025; take DP-700 (Fabric) and optionally DP-750 (Databricks).
- Projects get jobs: one strong end-to-end project matters more than many short courses.
1. What does an Azure data engineer do?
Short answer: An Azure data engineer builds and runs the pipelines that move data from source systems into a cloud data platform, cleans and models it, and makes it ready for reports, analytics and AI.
A typical day includes:
- Ingesting data from databases, files and APIs with Azure Data Factory or Fabric pipelines.
- Storing data in Azure Data Lake Storage Gen2 or OneLake.
- Transforming data with PySpark and SQL in Azure Databricks or Microsoft Fabric.
- Modeling data into Bronze, Silver and Gold layers (the medallion architecture).
- Securing and monitoring pipelines with Microsoft Entra ID, Key Vault, Unity Catalog and Azure Monitor.
If you want to see what interviewers expect at the end of this roadmap, read our Azure data engineer interview questions guide.
2. Azure data engineer roadmap at a glance
Short answer: The roadmap has 10 steps across about 6 months. Each step builds on the one before it, so learn them in order.
These timings are typical for a beginner. If you already work with SQL, SSIS or another ETL tool, you can move through steps 1–4 much faster.
3. Step 1: Learn data basics and SQL
Short answer: SQL is the foundation of the Azure data engineer roadmap. Spend about 4 weeks on it, until you can write window functions and deduplication queries without notes.
What to learn:
- Databases, tables, primary and foreign keys, and normalization.
- SELECT, WHERE, GROUP BY, HAVING, ORDER BY and all join types.
- Window functions: ROW_NUMBER, RANK, LAG, LEAD and running totals.
- CTEs, subqueries, MERGE and basic query performance.
- Data warehouse basics: fact and dimension tables, star schema and slowly changing dimensions (SCD Type 1 and 2).
- File formats: CSV, JSON and Parquet.
Practice task: Load a sales dataset into Azure SQL Database or a free local database and answer 20 business questions, such as top 3 products per category and month-over-month revenue change.
4. Step 2: Learn Python and PySpark basics
Short answer: Learn enough Python to clean data and call APIs, then move to PySpark, the language you will use most in Databricks and Fabric. About 3 weeks is enough for the basics.
What to learn:
- Python data types, lists, dictionaries, loops, functions and error handling.
- Reading and writing files, working with JSON and calling REST APIs with requests.
- pandas basics for small datasets.
- PySpark DataFrames: select, filter, withColumn, groupBy, joins and window functions.
- Reading and writing CSV, JSON, Parquet and Delta with PySpark.
Practice task: Write a Python script that reads every page of a public paginated API and saves the results as JSON, then flatten that JSON into a table with PySpark.
5. Step 3: Learn Azure fundamentals and storage
Short answer: Learn how Azure is organized and how data is stored and secured. Two weeks is enough for the parts a data engineer needs.
What to learn:
- Subscriptions, resource groups, regions and the Azure portal.
- Azure Data Lake Storage Gen2: containers, folders, the hierarchical namespace and access tiers.
- Microsoft Entra ID: users, groups, service principals and managed identities.
- Access control: Azure RBAC roles and ACLs on folders.
- Azure Key Vault for passwords, keys and connection strings.
- Cost basics: pricing, budgets and shutting down unused resources.
Practice task: Create a free Azure account, set up an ADLS Gen2 account with bronze, silver and gold containers, store a secret in Key Vault and give a managed identity read access to one container.
Tip: Set a budget alert on day one so a forgotten resource never surprises you with a bill.
6. Step 4: Learn Azure Data Factory
Short answer: Azure Data Factory (ADF) is the main tool for moving and orchestrating data in Azure. Spend about 3 weeks building real pipelines, not just watching demos.
What to learn:
- Pipelines, activities, linked services, datasets and triggers.
- Integration runtimes: Azure, self-hosted and Azure-SSIS.
- Copy activity, Lookup, ForEach, If Condition and Execute Pipeline.
- Parameters, variables and dynamic content.
- Incremental loading with a watermark column.
- Metadata-driven pipelines that load many tables from one control table.
- Error handling, retries, alerts and monitoring runs.
Practice task: Build one ADF pipeline that reads a list of tables from a control table and copies each one from Azure SQL Database to the Bronze container, loading only new and changed rows.
7. Step 5: Learn Azure Databricks, Spark and Delta Lake
Short answer: Databricks is where most Azure data transformation happens. Spend about 4 weeks learning Spark, Delta Lake and the medallion architecture.
What to learn:
- Workspaces, clusters, notebooks and jobs.
- Spark architecture: driver, executors, partitions, transformations and actions.
- Delta Lake: ACID transactions, MERGE, time travel, OPTIMIZE and schema evolution.
- Medallion architecture: Bronze (raw), Silver (cleaned) and Gold (business-ready) layers.
- Auto Loader for incremental file ingestion.
- Unity Catalog: catalogs, schemas, tables, permissions and lineage.
- Performance basics: shuffles, skew, broadcast joins and caching.
- Lakeflow Declarative Pipelines (formerly Delta Live Tables) for managed pipelines.
Practice task: Use Auto Loader to ingest JSON files into Bronze, clean and deduplicate them into Silver with MERGE, and build an aggregated Gold table for reporting.
Want to go deeper? See our Databricks course and the DP-750 exam guide.
8. Step 6: Learn Microsoft Fabric and Azure Synapse
Short answer: Microsoft Fabric is Microsoft's newest data platform, and the DP-700 exam is based on it. Learn its core items in about 2 weeks, and know how Synapse fits in.
What to learn:
- OneLake: one data lake for the whole organization, with shortcuts to other storage.
- Lakehouse vs Warehouse: Spark and Delta tables vs a T-SQL data warehouse.
- Data pipelines, Dataflows Gen2 and notebooks in Fabric.
- Real-Time Intelligence: eventstreams and KQL databases.
- Direct Lake mode for Power BI.
- Azure Synapse Analytics basics: serverless and dedicated SQL pools, and why many companies still run them.
Practice task: Create a Fabric Lakehouse (a free trial capacity is often available), load files with a pipeline, transform them in a notebook and build a Power BI report in Direct Lake mode.
Our Microsoft Fabric course in Hyderabad covers this step in depth.
9. Step 7: Learn DevOps, governance and monitoring
Short answer: Companies want engineers who can ship pipelines safely. Learn Git, CI/CD, monitoring and data governance in about 2 weeks.
What to learn:
- Git basics: branches, commits, pull requests and code reviews.
- Git integration in ADF, Databricks and Fabric.
- CI/CD with Azure DevOps or GitHub Actions, and Databricks Asset Bundles.
- Development, test and production environments.
- Azure Monitor, Log Analytics and alerts for failed pipelines.
- Governance with Unity Catalog and Microsoft Purview: access, lineage and data classification.
Practice task: Connect your ADF and Databricks work to a GitHub repository, set up one automated deployment, and create an alert that emails you when a pipeline fails.
10. Step 8: Build Azure data engineering projects
Short answer: Projects prove you can do the job. Build 2–3 end-to-end projects and put the code, an architecture diagram and a short README on GitHub. For 12 detailed project ideas with datasets and code, see our Azure data engineer projects guide.
What a good project includes:
- A real public dataset, such as NYC taxi trips or an e-commerce dataset.
- Incremental loading and data quality checks, not only a one-time load.
- Error handling and a simple alert.
- A clear README with the architecture, decisions and results.
11. Step 9: Get certified with DP-700
Short answer: DP-203 retired on 31 March 2025. The main certification for Azure data engineers is now DP-700: Microsoft Certified Fabric Data Engineer Associate. DP-750 for Azure Databricks is a useful second exam.
The DP-700 exam costs ₹4,865 in India. Prepare with our DP-700 syllabus and DP-700 practice questions, and read the full Azure data engineer certification guide.
12. Step 10: Prepare your resume and interviews
Short answer: In the last 2 weeks, turn your projects into a strong resume and practise the questions interviewers actually ask.
What to do:
- Resume: list tools, then 2–3 projects with numbers (tables loaded, data volume, run time saved).
- GitHub and LinkedIn: link your project repositories and add the DP-700 badge.
- SQL and PySpark coding: practise window functions, deduplication and top-N per group daily.
- Scenario questions: prepare answers for a failed pipeline, late data, duplicates and slow Spark jobs.
- Project explanation: practise explaining your architecture in 2 minutes.
Use our 200 Azure data engineer interview questions to practise, check the Azure data engineer salary in India guide before HR rounds, and see where companies are hiring in our Azure data engineer jobs in Hyderabad guide.
13. 6-month Azure data engineer study plan
Short answer: Follow this month-by-month plan with 2–3 hours of study a day, and finish each month with a small hands-on task.
Weekly rhythm: 4 days of learning new topics, 2 days of hands-on practice, and 1 day to revise and write notes.
14. Azure data engineer roadmap by background
Short answer: Your starting point changes how long the roadmap takes and where you should focus.
For career switchers: link your past experience to data work. A tester can talk about data validation, and a support engineer can talk about monitoring and incident handling.
15. Common mistakes to avoid
Short answer: Most learners slow down because they skip SQL, collect certificates without projects, or study tools that are no longer used.
- Skipping SQL: every interview starts with SQL, so do not rush it.
- Only watching videos: build every pipeline yourself in a real Azure account.
- Studying DP-203 material: the exam retired in March 2025; use DP-700 resources instead.
- Too many tools at once: learn ADF and Databricks well before adding others.
- Copying tutorial projects: change the dataset and add your own features, such as incremental loads and alerts.
- Ignoring cost: always stop clusters and set budget alerts.
Follow this roadmap with a trainer and real projects
Our Azure Data Engineer Training in Hyderabad follows this roadmap step by step, from SQL to Databricks and Fabric, with real-time projects and DP-700 preparation, taught by Dinesh (9+ years). Book a free demo and see the syllabus.
16. How Brolly Academy helps
- Azure Data Engineer Training in Hyderabad: 2–2.5 months, from ₹9,999, covering SQL, Python, Azure Data Factory, Databricks, PySpark and Microsoft Fabric with real-time projects.
- Guided projects you can put on GitHub and explain in interviews.
- DP-700 preparation, mock interviews, resume building and placement support.
- Classroom, online and hybrid batches in English and Telugu, with a free demo.
- Explore all courses at Brolly Academy.
Frequently asked questions
What is the roadmap to become an Azure data engineer?
Learn SQL, then Python and PySpark, then Azure storage, Azure Data Factory, Azure Databricks and Microsoft Fabric. Add Git and CI/CD, build 2–3 projects, pass DP-700 and prepare for interviews.
How long does it take to become an Azure data engineer?
About 6 months for a beginner studying 2–3 hours a day. SQL or ETL developers can usually do it in 3–4 months.
Can a fresher become an Azure data engineer?
Yes. Freshers with strong SQL, PySpark, one end-to-end Azure project and the DP-700 certification can get entry-level data engineering roles.
What skills are required for an Azure data engineer?
SQL, Python, PySpark, Azure Data Lake Storage Gen2, Azure Data Factory, Azure Databricks, Delta Lake, Microsoft Fabric, data modeling, Git and CI/CD.
Is coding required for an Azure data engineer?
Yes. You need SQL and Python with PySpark. You do not need advanced software engineering, but you must be comfortable writing and debugging code.
Which certification is best for an Azure data engineer in 2026?
DP-700 (Microsoft Certified Fabric Data Engineer Associate). DP-203 retired in March 2025. DP-750 is a good second exam if you work mainly with Databricks.
Should I learn Databricks or Microsoft Fabric first?
Learn Databricks first if you want most current Azure jobs, then learn Fabric for DP-700 and newer projects. The Spark and Delta Lake skills carry over between them.
Can I learn Azure data engineering for free?
Yes, much of it. Microsoft Learn has free learning paths, and Azure and Fabric offer free trials. A structured course helps with projects, doubts and interview preparation.
Is Azure data engineering a good career in 2026?
Yes. Azure is widely used by IT services firms, global capability centres and product companies in India, and pay rises quickly with experience.
Do I need Power BI to become an Azure data engineer?
Basic Power BI helps, because data engineers prepare data for reports. You do not need to be a Power BI expert.
Sources and further reading
- Microsoft Learn — Get started with data engineering on Azure
- Microsoft Learn — Microsoft Certified: Fabric Data Engineer Associate (DP-700)
- Microsoft Learn — Azure Data Factory documentation
- Microsoft Learn — Azure Databricks documentation
- Microsoft Learn — What is Microsoft Fabric?
- Delta Lake — Delta Lake documentation
- Apache Spark — PySpark documentation
- Wikipedia — Microsoft Azure
Learn with Brolly Academy
Review the syllabus, practical projects and batch timings, and book a free demo before you enrol.
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