Flagship certification programFoundation + advanced AI track
Build the datafoundation.Engineer theintelligence.
Advanced Data Engineering with Applied GenAI & Agentic AI.
A structured, mentor-led journey from modern data engineering foundations to production-style RAG, agentic workflows and an integrated capstone, taught by Deepesh Mishra.
Live mentor-led learning · Weekend/evening sessions · Cohort dates shared by admissions
Program fee₹60,000EMI options available – ask our counsellor
Live mentorship.
Projects that connect the stack.
One connected discipline
From dependable data
to useful AI systems.
Build the layers in sequence: cloud data foundations, retrieval grounded in real information, agents that use tools, and a capstone that brings the pieces together.
Program structure
Two tracks.
One connected skill path.
The foundation builds modern data engineering capability. The advanced specialization applies GenAI and agents on top of that foundation.
Data Engineering Foundation
Build modern data engineering capability across cloud data lakes, pipelines, orchestration, streaming and data quality.
Applied GenAI & Agentic AI
Build LLM-powered applications on real data platforms, from grounded RAG to tool-using agents and a production-style capstone.
Build as you learn
Projects move from data to action.
Hands-on labs run through both tracks. Each capstone grows from a working data foundation toward an integrated AI application.
Scalable data pipelines
Ingest, transform and orchestrate data across cloud platforms.
Production RAG assistant
Ground model responses in searchable business knowledge.
Tool-using agent
Connect an agent to APIs, data and a defined workflow.
Integrated capstone
Bring data engineering, GenAI and agentic patterns together.
Applied work
Build the systems in the brochure.
Use these project formats to practise the ideas: data ingestion, grounded retrieval, useful agents and an integrated application.
PROJECT BUILD
Scalable data pipelines
Ingest, transform and orchestrate data across cloud platforms.
PROJECT BUILD
Production RAG assistant
Ground model responses in searchable business knowledge.
PROJECT BUILD
Tool-using agent
Connect an agent to APIs, data and a defined workflow.
PROJECT BUILD
Integrated capstone
Bring data engineering, GenAI and agentic patterns together.
The roadmap · two tracks
From data foundations
to agentic applications.
Follow the month-by-month foundation and then move into the advanced GenAI modules. The brochure describes the advanced track as a specialization; admissions can confirm its cohort schedule.
01Foundation · Month 1 · Weeks 1-4Foundations & cloud data lakesRefresh Python and SQL, core data concepts and cloud foundations.
- Python and SQL refresher
- Files, formats, joins and aggregations
- Azure Data Lake fundamentals
- Data modeling and first cloud environment
You buildSet up a cloud data environment and prepare a structured dataset.
02Foundation · Month 2 · Weeks 5-8Pipelines, PySpark & orchestrationProcess larger datasets and build orchestrated pipelines.
- PySpark for distributed processing
- Data structuring and cleaning
- Azure Data Factory pipelines
- Delta Lake and workflow orchestration
You buildBuild a repeatable pipeline with transformation and orchestration.
03Foundation · Month 3 · Weeks 9-12Multi-cloud, Databricks & streamingExpand into lakehouse patterns, streaming and data quality.
- AWS data services
- Databricks lakehouse architecture
- Spark Structured Streaming
- Great Expectations and dbt-style transformations
You buildDevelop a streaming-ready, quality-checked data workflow.
04Foundation · Month 4 · Weeks 13-16Foundation capstone & GenAI introductionBring the foundation together and prepare for the advanced track.
- Ingest, process, store and analyze a real dataset
- Introduction to GenAI-powered data applications
- End-to-end data engineering capstone
- Resume and interview preparation
You buildPresent an end-to-end data engineering capstone.
05Advanced track · Module 1Applied GenAI foundationsConnect language models to useful workflows and real data.
- LLM fundamentals and prompting
- Embeddings and vector databases
- Model connections to data pipelines
- Applied GenAI patterns
You buildCreate a grounded LLM workflow using real data.
06Advanced track · Module 2Retrieval-augmented generationBuild retrieval systems that ground responses in enterprise knowledge.
- RAG architecture
- Chunking and retrieval strategies
- LangChain pipelines
- Grounded responses and source context
You buildBuild a RAG assistant over a knowledge collection.
07Advanced track · Module 3Agentic AI systemsDesign multi-step agents that use tools and connect to enterprise data.
- Tool-using agent design
- LangGraph workflows
- CrewAI orchestration patterns
- Agent connections to enterprise data systems
You buildPrototype an agentic workflow with tools and clear task boundaries.
08Advanced track · Module 4Enterprise AI platforms & capstoneApply platform-native AI capabilities and integrate the complete system.
- Snowflake Cortex AI
- Databricks Mosaic AI
- Evaluation and integration
- End-to-end production-style capstone
You buildBuild and present an integrated Agentic AI data application.
Technologies & tools
The stack across both tracks.
Tools and platforms named in the program brochure, used for cloud data engineering, GenAI applications and agent workflows.
Who it serves
Built for people ready to grow their data and AI practice.
Designed for professionals already working with data, building data products, or moving from related analytics, software and database roles.
Ask about fit and prerequisites- Analytics and BI professionals moving beyond dashboards
- ETL and integration engineers modernizing their stack
- Software and backend developers expanding into data systems
- Database administrators moving toward data platforms and AI
Prerequisites
Start with core data comfort. Build forward from there.
Cloud knowledge is helpful but not mandatory. The brochure includes Python and SQL refreshers before the program work.
Helpful foundations
Basic understanding of databases; SQL is required.
Familiarity with a programming language; Python is preferred.
Comfort with data concepts such as files, formats, joins and aggregations.
Experience working with data in any role is useful.
Founder & program mentor
Deepesh Mishra
Founder, Datamarcos · Corporate Trainer · Solution Architect
Deepesh is a BITS Pilani alumnus with 18+ years of experience spanning AI/ML, Data Engineering and Cloud Architecture. The brochure describes his approach as practical: make concepts clear, build on real work, and continue mentoring beyond the classroom.
Training in action
Real sessions. Real learners.
Classroom and cohort imagery from the supplied Datamarcos program brochure.
Learner feedback
What learners say about Deepesh’s training.
Quotes reproduced from the supplied brochure, with each speaker and role identified there.
“Without you it wouldn't have been possible sir. Thank you so much for making me a Data Engineer.”Raghul KLearner, Data Engineering Track
“Learning under Deepesh Sir has been a great opportunity — all my doubts were dispelled and I honed my skills with hands-on activities and industry insights.”Ansh RanjanData Engineer Trainee, Hexaware Technologies
“His deep understanding, combined with a clear and engaging teaching style, made complex concepts easy to grasp.”Kumar GauravSenior Azure Data Engineer, PwC
Program guide · 2026
Read the full brochure.
Full program guide
Advanced Data Engineering with Applied GenAI and Agentic AI brochure
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Plan your data + AI learning path.
Ask about foundation-track dates, advanced specialization scheduling, fees and admissions. The program brochure lists both tracks; the team can confirm the current cohort plan.
Download the full program brochure7-day refund policy: not right for you? Ask within 7 days for a refund.
Data engineers who want a customer-facing AI role can continue with the Forward Deployed AI Engineer program.
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