Azure Data Engineering Course in Vizag
Learn to move data from source systems into a cloud lakehouse and deliver validated datasets for reporting. This 3-month Azure Data Engineering course at Softenant Technologies in Visakhapatnam covers Azure Data Factory, storage, Databricks, PySpark, SQL, monitoring and practical pipeline design.
What you will learn
- Build parameterised Azure Data Factory ingestion pipelines with triggers, dependencies and retries.
- Transform sample data with Azure Databricks and PySpark using layered lakehouse design.
- Use identity, access permissions and monitoring to operate a controlled data workflow.
- Explain incremental loads, quality checks, failure recovery and the architecture of a cloud project.
Azure Data Factory, Azure Data Lake Storage Gen2, Azure Databricks, PySpark, SQL, Delta Lake concepts, Microsoft Entra ID, Key Vault, monitoring and Git. Microsoft Fabric is introduced as a related modern data-platform learning area.
From source data to a useful result
Files, APIs and SQL sources
Clean, model and enrich
Keys, counts and totals
Trusted tables and AI-ready data
A successful pipeline is repeatable and understandable. It should make errors visible, preserve source context and produce results that another person can verify.
Azure Data Engineering syllabus
Follow 20 modules from foundations to a documented end-to-end project. Exercises use sample or appropriately authorised data.
01. Azure data engineering foundations
Understand subscriptions, resource groups and the cloud data lifecycle. Identify source systems, storage, processing and analytical destinations in a practical Azure architecture.
02. SQL foundations and analytical queries
Practise joins, aggregations, CTEs and window functions. Define business keys and table grain, and independently check counts and metrics used by the pipeline.
03. Python for cloud data processing
Use functions, CSV/JSON handling, API requests, environments and exception handling. Prepare reusable scripts with explicit configuration and useful logging.
04. Azure storage and ADLS Gen2
Explore Blob Storage, hierarchical namespace concepts, containers, directories and file formats. Plan raw and curated zones with suitable naming and retention rules.
05. Identity, permissions and secrets
Study Microsoft Entra ID, managed identities, role-based access and Key Vault concepts. Explain which service needs access to which data without embedding secrets in code.
06. Azure Data Factory components
Understand factories, linked services, datasets, integration runtimes, activities and pipelines. Explain how each component supports data movement or orchestration.
07. ADF Copy Activity and source connections
Build a sample ingestion workflow from files or a SQL source. Configure source and sink mappings, discuss integration-runtime choices and validate the copied records.
08. ADF parameters and control flow
Use pipeline and dataset parameters, expressions, variables, lookup results and loop/condition concepts. Design a reusable workflow instead of duplicating a pipeline for every table.
09. Triggers, dependencies and scheduling
Compare scheduled and event-driven processing. Set activity dependencies and retry behaviour, inspect run history and explain a safe backfill plan.
10. Incremental ingestion and change capture
Define watermark boundaries, keys and source-change handling. Demonstrate how a rerun, late record or source delete is managed without silent duplication.
11. Azure Databricks workspace and compute
Understand notebooks, workspaces and compute choices. Organise practice code and distinguish storage, processing and workspace access responsibilities.
12. PySpark DataFrames and transformations
Read data with explicit schemas, clean fields, join reference tables and calculate grouped results. Test each transformation against a small expected-output example.
13. Spark performance and troubleshooting
Explore partitions, skew and execution plans. Compare a small workload before and after a design change and explain why indiscriminate caching or repartitioning may not help.
14. Delta Lake tables and merge patterns
Study Delta table concepts, updates, merge operations, schema evolution and history. Document business keys and verify the result of an incremental update.
15. Bronze, silver and gold lakehouse design
Build raw, cleaned and business-ready layers. Reconcile each layer with its inputs, preserve source context and explain which quality rules apply at each stage.
16. Azure SQL, Synapse and analytical serving
Compare relational databases, warehouses and lakehouses. Introduce Azure SQL and Azure Synapse analytical concepts and deliver a curated result for downstream reporting.
17. Microsoft Fabric and OneLake orientation
Introduce Fabric lakehouses, notebooks, OneLake and its Data Factory experience. Compare the platform with the Azure pipeline stack and identify where concepts overlap.
18. Monitoring, cost control and recovery
Read pipeline logs and investigate failures. Plan lab resource limits and cleanup, record operational checks and explain how to recover a failed or incomplete run.
19. Git, CI/CD and deployment concepts
Version notebooks and pipeline configuration. Separate environment-specific parameters, review deployment changes and introduce testing and release concepts with Git and Azure delivery workflows.
20. End-to-end Azure capstone and interviews
Connect ingestion, transformation and analytical serving. Present a diagram, sample-data description, quality evidence, access notes, recovery plan and project answers for interviews.
Practical projects and portfolio work
ADF ingestion pipeline
Copy sample source files into a data lake with reusable parameters and a schedule. Log each run and demonstrate handling of a missing or malformed input file.
Retail lakehouse with Databricks
Transform sample orders, customers and products into bronze, silver and gold tables. Reconcile totals and document joins, business keys and an incremental merge.
Incremental database-to-lake workflow
Use a sample SQL source, watermark and curated output. Prove that a repeated run does not duplicate rows and show how a delayed source record is handled.
Monitored end-to-end capstone
Connect ingestion, transformation and an analytical result. Include a diagram, sample-data description, validation evidence, access notes, estimated resource choices and cleanup steps.
For each project, keep the business question, architecture, inputs, code, validation evidence and limitations together. Practise explaining a failure, a correction and the reason for your design choices.
Who can join and how to prepare
This learning path is useful for students, freshers, analysts and software professionals who want to build data-pipeline skills. Basic computer use, introductory SQL and Python fundamentals provide helpful preparation. Contact Softenant to discuss your current level before choosing a batch.
Revise joins, functions, CSV/JSON handling and basic command-line use. If you are starting from zero, ask about the foundation work needed to keep pace with the practical modules.
Course fee, duration and batch details
Azure Data Engineering: ₹15,000 for 3 months. Confirm the current weekday/weekend timetable and classroom or instructor-led online availability before enrolling. Payment terms, taxes, installment options and lab arrangements are confirmed directly with Softenant.
Project and interview preparation
Prepare an honest resume description, a clear architecture diagram and answers about SQL, transformations, quality checks and recovery. A course completion certificate follows completion of training and project work. Resume preparation, mock interviews and job guidance support readiness; employment or salary is not guaranteed.
Frequently asked questions
What is the Azure Data Engineering fee and duration?
The course fee is Rs.15,000 and the duration is 3 months. Ask Softenant about the active timetable, payment terms and batch availability.
Does the syllabus include ADF, Databricks and PySpark?
Yes. It covers Azure Data Factory ingestion and orchestration, Azure Databricks and PySpark transformations, storage, lakehouse layers, SQL, quality checks and operations.
How does it differ from Azure with DevOps?
Azure Data Engineering focuses on data ingestion, storage, transformation and analytical delivery. Azure with DevOps focuses on cloud and application delivery workflows. These are separate courses with separately confirmed fees.
Do I need prior Azure experience?
Azure fundamentals are introduced, but basic SQL and Python help with practical exercises. Discuss your experience with Softenant so foundation preparation can be planned.
Are cloud credits and certification exams included?
Contact Softenant to confirm the lab-access arrangement, cloud usage costs and any certification-exam arrangements before enrolling.
Is Microsoft Fabric the same as Azure Data Factory?
No. Fabric is a broader analytics platform with its own Data Factory experience and lakehouse tools. The course introduces the relationship and differences while teaching the Azure pipeline stack.
Are classroom or online batches available?
Contact Softenant for the current classroom batch in Visakhapatnam, instructor-led online availability and timetable.
Is placement guaranteed?
No. Resume, interview and project preparation support career readiness; employment depends on skills, practice, selection processes and available roles.
Compare related learning paths
Learning references
Explore the official documentation for the technologies discussed in the syllabus.
Speak to Softenant about the next batch
Call +91 9393969628, email info@softenant.com or contact the team for the timetable and enrollment details.
Visit: Flat No.101, Geetha Mansion II, opposite Andhra Bank, Akkayyapalem, Visakhapatnam, Andhra Pradesh 530016.