Databricks Certified Professional Engineers with Enterperise Architect , Pyspark , Agentic AI , Generative AI , DBT, Azure & AWS

Databricks Engineers & Architect role learning with Generative AI & Agentic AI With Pyspark– (Supply Chain, Manufacturing, Chemical & Petrolium, Retailed and Automobiles area)

Course contained 4 modules:
Module -1: Databricks Engineering & Architect

Module – 1.1 Databricks + PySpark (Data Engineer → Enterprise Architect)
Module 1.2: Databricks Technical & Enterprise Architecture (Only for Architect Role)

Module -2 Enterprise Data & AI Master Curriculum with Databricks

Module-3: DBT (Data Build Tool Developer) With Databricks

Module-4: Enterprise Analytics & GenAI using Databricks + Snowflake

Module -5: Any one-use case will be discussed and worked as POC by students.

TRACK A – Generative AI Engineering & Enterprise AI Architecture

• AI Strategy for Enterprises: ROI-driven GenAI adoption, failure patterns of POCs, AI maturity models
• LLM Fundamentals: Transformers, MoE, reasoning models, tokenization, context windows
• Model Landscape: Open-source vs Closed-source, sovereign AI, domain models
• Prompt Engineering at Scale: Prompt-as-code, templates, versioning, testing, guardrails
• Retrieval-Augmented Generation (RAG): vector search, hybrid retrieval, GraphRAG, document chunking strategies
• Agentic AI Systems: tool agents, planner/executor patterns, memory, human-in-the-loop control
• Multi-Agent Architecture: orchestration, task routing, failure handling
• LLMOps & AgentOps: evaluation pipelines, hallucination detection, semantic monitoring
• AI Governance: security, access control, auditability, compliance, data sovereignty
• Cost & Performance Engineering: token optimization, caching, batch vs real-time inference
• Enterprise AI Reference Architectures across regulated industries
  
  

  Over the past several years, I have designed and delivered specialized Azure Databricks courses tailored for data engineers aspiring to transition into architect-level roles. My teaching approach emphasizes hands-on, real-time solutions that integrate Databricks with dbt (Data Build Tool) and Snowflake, ensuring learners gain both conceptual clarity and practical implementation skills.

  I begin by grounding participants in the Databricks Lakehouse architecture, explaining Medallion design patterns (Bronze, Silver, Gold) and how they enable scalable data pipelines. From there, I introduce PySpark fundamentals and advanced optimization techniques, ensuring engineers can confidently handle large-scale transformations. To bridge the gap toward architecture, I emphasize design thinking—how to structure pipelines for resilience, governance, and cost efficiency.

  A major focus of my teaching is dbt integration with Databricks. Learners build modular SQL transformations using dbt, while I demonstrate how to orchestrate these within Databricks workflows. Real-time labs include creating incremental models, leveraging dbt macros, and implementing CI/CD pipelines with Azure DevOps. This ensures engineers understand not just how to write transformations, but how to embed them into enterprise-grade workflows.

  Equally important is Snowflake integration. I guide learners through hybrid architectures where Databricks handles heavy transformations while Snowflake serves as the analytical warehouse. Real-world scenarios include streaming ingestion via Kafka into Databricks, transformation with dbt, and publishing curated datasets into Snowflake for BI consumption. I also cover Unity Catalog governance, role-based access control, and data masking policies, preparing engineers to design secure, compliant solutions.

  My teaching style is highly interactive: I use case studies from enterprise projects (e.g., retail demand forecasting, financial risk modeling, IoT telemetry pipelines) to illustrate architectural trade-offs. Each session culminates in a capstone project, where learners design an end-to-end pipeline integrating Databricks, dbt, and Snowflake. This project simulates real-world challenges such as schema evolution, late-arriving data, and performance tuning.

  By the end of the course, participants not only master Databricks engineering but also acquire the architectural mindset—understanding how to design scalable, secure, and cost-effective solutions across multi-cloud environments. My goal is to transform data engineers into solution architects capable of leading enterprise data modernization initiatives with confidence.