Prepzee is accepting learners for its Data Engineering Job Oriented Program, an online training programme designed for people seeking practical skills across modern cloud and data engineering technologies.
The programme covers Python, Microsoft Azure, Microsoft Fabric, Azure Data Factory, Databricks, PySpark, Snowflake, Airflow, Kafka and AI-focused data engineering, with practical projects, certification preparation, interview support and résumé preparation included in the curriculum.
The upcoming live online classroom batch is scheduled from 19 September 2026 to 31 January 2027, with classes on Saturdays and Sundays from 8:00 AM to 11:00 AM IST. Prepzee currently displays a discounted price of $625, compared with the listed original price of $780.
Prepzee Data Engineering Program Overview
| Category | Details |
|---|---|
| Provider | Prepzee |
| Programme | Data Engineering Job Oriented Program |
| Format | Online |
| Training | 100+ hours of live training advertised |
| Hands-On Learning | 80+ hours of hands-on exercises advertised |
| Projects | 8+ projects and case studies advertised |
| Batch Start | 19 September 2026 |
| Batch End | 31 January 2027 |
| Schedule | Saturday–Sunday |
| Time | 8:00 AM–11:00 AM IST |
| Current Price Shown | $625 |
| Original Price Shown | $780 |
| Financing | No Cost EMI advertised |
| Core Technologies | Python, Azure, Microsoft Fabric, Databricks, PySpark, Snowflake, Airflow, Kafka |
| AI Topics | RAG pipelines, vector databases, unstructured data, AI data ingestion |
| Career Preparation | Mock interviews, certification preparation, ATS résumé support |
| Certificate | Prepzee states certification is provided after completing the course |
What Is the Prepzee Data Engineering Job Oriented Program?
Prepzee describes the programme as a structured data engineering learning path designed to help participants develop practical capabilities needed to build production-ready data pipelines.
The programme combines several major technologies rather than focusing on one cloud platform or data tool.
Learners receive exposure to:
- Microsoft Azure
- Microsoft Fabric
- Azure Data Factory
- Databricks
- PySpark
- Snowflake
- Airflow
- Kafka
- Python
- Data modeling
- Data warehouses
- Data lakes
- ETL pipelines
- Real-time data engineering
- AI data engineering
Prepzee also states that the programme supports preparation for certifications from Microsoft, Snowflake and Databricks.
More Than 100 Hours of Live Data Engineering Training
Prepzee advertises more than 100 hours of live training.
The provider says the programme follows a complete Data Engineer Roadmap designed around in-demand data engineering technologies.
The programme also advertises:
- 80+ hours of hands-on exercises
- 8+ projects and case studies
- 24/7 technical support
- Training from Microsoft Certified Trainers
- Lifetime live training access
These are provider-stated programme features.
Who Is This Data Engineering Programme For?
Prepzee identifies several potential audiences for the training.
The programme may be relevant if:
- You are an IT professional interested in cloud data engineering.
- You want to transition into the data industry.
- You are a DBA with database and SQL experience.
- You are a Data Analyst or Data Scientist interested in working with larger-scale data and data pipelines.
This makes the course particularly relevant to professionals seeking to move from analytics, databases or traditional IT into modern data engineering.
Module 1: Python for Data Engineering
The programme begins with programming foundations.
Topics include:
- Introduction to Python
- Python environment setup
- Running Python scripts
- Python syntax
- Variables and data types
- Lists
- Tuples
- Sets
- Dictionaries
- Conditional statements
- Loops
- Functions
- Python libraries
- NumPy
- Pandas
This provides the programming foundation needed for later cloud and data engineering modules.
Data Modeling and System Design
Students then move into data modeling and architecture.
Topics include:
- Structured data
- Semi-structured data
- Unstructured data
- OLTP versus OLAP
- Data warehouses
- Data lakes
- Data marts
- Fact and dimension tables
- Slowly Changing Dimensions
- Star schemas
- Snowflake schemas
- Scalable data pipeline modeling
- End-to-end data workflows
- ETL pipelines
- Data lineage
- Schema evolution
These concepts form an important foundation for designing production data systems.
Microsoft Azure Data Engineering
Microsoft Azure is one of the programme’s major technology areas.
Students explore cloud computing and Microsoft Azure before progressing into data engineering services.
Topics include:
- Microsoft Entra ID
- Service principals
- Managed identities
- Azure Storage Accounts
- Table Storage
- Blob Storage
- Queue Storage
- Azure Data Lake Storage Gen2
- REST APIs
- Azure SQL
- Azure Data Factory
- Linked Services
- Integration Runtime
- Azure Key Vault
This section is designed to develop practical Azure cloud data engineering knowledge.
Azure Data Factory
Azure Data Factory is specifically included.
Students learn how to build pipelines using ADF and work with concepts such as Linked Services and Integration Runtime.
ADF is taught within the wider context of building and automating cloud-based data workflows.
Microsoft Fabric Data Engineering
The programme also provides substantial exposure to Microsoft Fabric.
Topics include:
- Introduction to Microsoft Fabric
- Data ingestion
- Dataflows Gen2
- Real-Time Intelligence
- Lakehouse architecture
- Data management
- Data warehouses
- Loading and securing data warehouses
- Semantic models
The detailed course outline additionally includes orchestration, Delta Lake tables and CI/CD processes in Microsoft Fabric.
Databricks and PySpark
Large-scale data processing is taught through Databricks and Apache Spark.
Students cover:
- Spark execution
- RDDs
- DataFrames
- CSV and Parquet
- PySpark
- Spark SQL
- Joins
- Aggregations
- Window functions
- Spark UI
- Performance optimization
- Azure Databricks architecture
- Cluster management
- Unity Catalog
- Delta Lake
- Medallion architecture
- Batch pipelines
- Real-time pipelines
The Medallion architecture section covers the familiar Bronze, Silver and Gold data layers.
Snowflake and Cortex AI
Snowflake is another major platform covered by the programme.
Training includes:
- Snowflake architecture
- Snowflake data types
- Storage
- Compute
- Cloud services
- Data loading
- Data transformation
- Performance optimization
- Real-time ETL pipelines
- Integration with BI tools
- Cortex AI
- Cortex AI Search Service
- Cortex Analyst
- Document AI
The programme also mentions connecting Snowflake with tools such as Tableau and Power BI.
Apache Airflow
Learners receive training in workflow orchestration with Apache Airflow.
Topics include:
- Airflow architecture
- Core components
- DAGs
- Operators
- Task scheduling
- Data pipeline scheduling
- Airflow UI
- Airflow plugins
This section is particularly relevant to learners interested in orchestrating production data pipelines.
Apache Kafka and Real-Time Data Engineering
The programme also introduces Apache Kafka for real-time data pipelines.
Students learn about:
- Kafka architecture
- Producers
- Consumers
- Topics
- Kafka clusters
- Real-time data-processing use cases
This adds streaming data engineering to the programme alongside traditional batch processing.
Data Engineering for AI Systems
One of the more contemporary parts of the curriculum is Data Engineering for AI Systems.
The module covers:
- RAG architecture from a data-pipeline perspective
- Data movement from source systems to embeddings
- Vector databases
- LLM data pipelines
- Managing embeddings
- Unstructured data
- Data ingestion for AI applications
- Batch ingestion for AI
- Streaming ingestion for AI
This section focuses on the role data engineers can play in building the data infrastructure behind modern AI applications.
Understanding RAG Data Pipelines
RAG, or Retrieval-Augmented Generation, has become an important architecture for AI systems that need to retrieve external information.
Prepzee’s curriculum approaches RAG specifically from a data pipeline perspective.
Students explore how information moves from source systems through embeddings and vector databases toward large language models.
This makes the programme relevant not only to conventional cloud data engineering but also to professionals interested in AI infrastructure.
Interview and Résumé Preparation
The final training module includes career preparation.
Prepzee lists:
- Mock interview sessions
- Guidance for presenting projects and experience on a résumé
- Sample certification exam papers
- ATS-friendly résumé preparation
These services are designed to complement the programme’s technical curriculum.
Real-World Data Engineering Projects
Prepzee advertises multiple practical projects and case studies.
The source provides examples across several industries.
Project 1: Azure Insurance Data Platform
Students build a production-grade insurance data platform on Microsoft Azure.
The project uses Databricks for scalable processing and transformations and implements Bronze–Silver–Gold Medallion architecture.
Project 2: Real-Time Kafka Data Engineering System
Another project involves building an event-driven system inspired by Uber.
Using Apache Kafka, learners work with a scenario where a booking triggers activities such as:
- Driver allocation
- Payments
- Notifications
- Analytics
Project 3: Financial Data Platform and Risk Intelligence
Students build an enterprise-grade financial data platform using:
Apache Airflow + Snowflake + dbt
Transaction data from PostgreSQL is transformed for risk, fraud and analytics use cases.
Project 4: Healthcare Data Platform and AI Analytics
Students design an end-to-end healthcare data platform using Microsoft Fabric.
The project involves data from:
- Electronic health record systems
- IoT wearables
- Medical imaging
- Insurance claims
The resulting platform supports AI-related scenarios such as patient-risk prediction, fraud detection and operational optimization.
Project 5: Retail Data Platform
Another project involves building a production-grade retail analytics platform using Snowflake.
Students work with sales, customer and product information to create scalable analytics-ready datasets.
Career Paths After Data Engineering Training
Prepzee identifies several career areas associated with the programme.
These include:
- Data Engineer
- Data Integration Specialist
- Cloud Data Warehouse Engineer
- Cloud Data Engineer
- Data Consultant
- Microsoft Fabric Specialist
Completing the programme does not guarantee employment in any of these positions.
Core Data Engineering Skills Covered
Prepzee specifically highlights four important skills:
Data Transformation
Data Orchestration
Data Ingestion
Data Pipelines
These skills run throughout the wider curriculum.
Certification Preparation
Prepzee states that the programme helps learners prepare for three data engineering certification areas associated with:
- Microsoft
- Snowflake
- Databricks
The curriculum also includes certification preparation and sample exam papers.
Candidates should note that completing Prepzee’s programme should not automatically be interpreted as passing separate third-party certification examinations.
Each external certification provider sets its own exam and credential requirements.
Prepzee Course Completion Certification
Prepzee separately states that learners can:
“Get Certified after completing Data Engineer full course with Prepzee.”
This should be distinguished from Microsoft, Snowflake and Databricks certifications, which have their own credentialing processes.
Bonus Courses and Resources
The programme advertises bonuses with a stated combined value of ₹20,000.
These include:
- AWS Cloud Practitioner Course — stated value ₹5,000
- Linux Fundamentals Course — stated value ₹3,000
- Microsoft Fabric 700 Master Cheat Sheet — stated value ₹3,000
- Playbook of 97 Things Every Data Engineer Should Know — stated value ₹4,000
- Designing Data Intensive Applications PlayBook — stated value ₹4,000
These values and offers are provider-stated and may change.
How Much Does the Prepzee Data Engineering Program Cost?
For the batch shown, Prepzee displays:
Original price: $780
Current advertised price: $625
The page describes this as a 20% discount.
Because promotional prices can change, applicants should confirm the amount at checkout before making payment.
No Cost EMI
The programme also advertises:
No Cost EMI.
The supplied page does not provide all financing eligibility requirements, so prospective students should review the applicable terms before using this payment option.
When Does the Next Batch Start?
The advertised batch begins:
19 September 2026
and runs until:
31 January 2027.
Classes are scheduled on Saturdays and Sundays.
Class Schedule
The advertised schedule is:
Saturday and Sunday
8:00 AM to 11:00 AM IST (GMT+5:30).
International learners should convert this schedule to their local time before enrolling.
Does Prepzee Guarantee a Job?
The page includes a FAQ asking whether the job-support programme guarantees employment, but the supplied text does not include the answer to that FAQ.
Therefore, a job guarantee should not be claimed based on this source.
Prepzee does advertise career-transition statistics, placement support and learner testimonials, but these should not be interpreted as a guarantee of employment or salary growth for every participant.
Provider-Reported Career Outcomes
Prepzee displays several individual career-transition examples and reports figures such as 500+ career transitions and salary increases of up to 350%.
These are provider-reported marketing claims and individual outcomes can vary considerably.
Applicants should evaluate the technical curriculum, teaching quality, cost and career support independently rather than assuming that past learner outcomes will be replicated.
Is Prior Data Engineering Experience Required?
The programme is marketed to several groups, including existing IT professionals, DBAs, Data Analysts and Data Scientists.
The supplied page includes a FAQ about whether prior data engineering experience is required, but its answer is not included in the provided text.
No universal prior-experience requirement should therefore be stated based on this source.
Why This Programme May Interest Data Analysts
For Data Analysts seeking to transition toward engineering, the curriculum covers several areas beyond traditional analytics.
These include:
- Production data pipelines
- Data architecture
- Cloud infrastructure
- Distributed data processing
- Data orchestration
- Data lakes
- Lakehouses
- Real-time streaming
- CI/CD
- AI data infrastructure
Prepzee specifically identifies Data Analysts and Data Scientists who want to work with data at larger scale and manage data pipelines as one of its intended audiences.
How to Enroll
Prospective learners can review the programme information and enroll in the available live online classroom batch through Prepzee.
Before paying, candidates should verify:
- Current batch availability
- Final tuition price
- Class schedule
- Financing terms
- Trainer arrangements
- Certificate details
- External certification exam costs
- Career-support terms
- Refund policy
The displayed promotional price and time-limited offer may change.
Frequently Asked Questions
What is the Prepzee Data Engineering Job Oriented Program?
It is an online data engineering training programme covering technologies including Azure, Microsoft Fabric, Databricks, Snowflake, Airflow and Kafka.
When does the next batch start?
The supplied page shows a start date of 19 September 2026.
How long does the programme run?
The live online batch shown runs from 19 September 2026 through 31 January 2027.
How much does the course cost?
The page currently displays $625, reduced from a listed original price of $780.
Is the course online?
Yes. The programme is advertised as online.
Does it teach Python?
Yes. Python fundamentals, NumPy and Pandas are included.
Does it cover Microsoft Azure?
Yes. Azure is one of the main cloud platforms covered by the curriculum.
Does it cover Microsoft Fabric?
Yes. Microsoft Fabric receives extensive coverage, including ingestion, Dataflows Gen2, Real-Time Intelligence, Lakehouse architecture, warehouses and semantic models.
Is Databricks included?
Yes. Databricks, Spark, PySpark, Delta Lake, Unity Catalog and Medallion architecture are included.
Does the programme teach Snowflake?
Yes. Snowflake architecture, transformations, ETL, performance and Cortex AI are covered.
Are Airflow and Kafka included?
Yes. Both workflow orchestration with Airflow and real-time data processing with Kafka are included.
Does it cover AI data engineering?
Yes. The curriculum includes RAG data pipelines, embeddings, vector databases, unstructured data and ingestion strategies for AI applications.
Are projects included?
Yes. Prepzee advertises 8+ projects and case studies and provides examples involving Azure, Kafka, Snowflake, Airflow and Microsoft Fabric.
Does the programme include interview preparation?
Yes. The curriculum includes mock interviews, résumé guidance, certification exam materials and ATS résumé preparation.
Does Prepzee provide a certificate?
Prepzee states that learners can receive certification after completing its full Data Engineer course.
Does the programme guarantee employment?
A job guarantee is not established by the supplied source. The page contains a FAQ asking about job guarantees, but the corresponding answer is not present in the provided text.
Final Thoughts
The Prepzee Data Engineering Job Oriented Program 2026–2027 provides a broad technical curriculum for learners seeking practical exposure to modern data engineering.
Its strongest feature is the range of technologies covered. Students can develop skills across Python, Azure, Azure Data Factory, Microsoft Fabric, Databricks, PySpark, Snowflake, Airflow and Kafka, before moving into emerging areas such as RAG data pipelines, embeddings and vector databases for AI systems.
The upcoming live online batch is advertised from 19 September 2026 to 31 January 2027, with weekend classes and a current displayed price of $625.
For Data Analysts, DBAs, IT professionals and others seeking to move toward cloud data engineering, the combination of technical training, hands-on projects and career preparation may make the programme worth investigating.
Prospective students should nevertheless verify the final fee, promotional terms, external certification costs and career-support conditions before enrolling.
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