Abhishek Agrawal

Class Location

Online class via Zoom

Student's home

Tutor's Home

Years of Experience in Microsoft Azure Training

10

Teaching Experience in detail in Microsoft Azure Training

I'm an Azure Data Engineer offering 10+ years of teaching experience and 6+ years IT experience in data engineering, data warehousing, machine learning, and business intelligence realm. I have completed my engineering (B.Tech) from NIT RAIPUR. -All Classes with Lab sessions and Daily Practice. -End to End Project Implementation using Azure. -Trained people in becoming Microsoft certified cloud developer from scratch. -Get online recording of classes with lifetime access. -Helped experienced IT people to secure good job in Azure cloud technology. -Providing support in interview preparation and Resume Building. -Support for fresher's to secure good jobs as Business Analyst, Data Analyst, Data Engineer in 2-3 Month of Training -Helped people to move from SSIS, SSRS, SSAS to Azure Cloud Enviornment. -Providing Microsoft CertificationTraining and Support. -Practice is key to success. -I believe in Practical Implementation instead of theoretical knowledge.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Azure Data Factory Training

10

Teaching Experience in detail in Azure Data Factory Training

I'm an Azure Data Engineer offering 10+ years of teaching experience and 6+ years IT experience in data engineering, data warehousing, machine learning, and business intelligence realm. I have completed my engineering (B.Tech) from NIT RAIPUR. -All Classes with Lab sessions and Daily Practice. -End to End Project Implementation using Azure. -Trained people in becoming Microsoft certified cloud developer from scratch. -Get online recording of classes with lifetime access. -Helped experienced IT people to secure good job in Azure cloud technology. -Providing support in interview preparation and Resume Building. -Support for fresher's to secure good jobs as Business Analyst, Data Analyst, Data Engineer in 2-3 Month of Training -Helped people to move from SSIS, SSRS, SSAS to Azure Cloud Enviornment. -Providing Microsoft CertificationTraining and Support. -Practice is key to success. -I believe in Practical Implementation instead of theoretical knowledge.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Azure Databricks Courses

10

Teaching Experience in detail in Azure Databricks Courses

I'm an Azure Data Engineer offering 10+ years of teaching experience and 6+ years IT experience in data engineering, data warehousing, machine learning, and business intelligence realm. I have completed my engineering (B.Tech) from NIT RAIPUR. -All Classes with Lab sessions and Daily Practice. -End to End Project Implementation using Azure. -Trained people in becoming Microsoft certified cloud developer from scratch. -Get online recording of classes with lifetime access. -Helped experienced IT people to secure good job in Azure cloud technology. -Providing support in interview preparation and Resume Building. -Support for fresher's to secure good jobs as Business Analyst, Data Analyst, Data Engineer in 2-3 Month of Training -Helped people to move from SSIS, SSRS, SSAS to Azure Cloud Enviornment. -Providing Microsoft CertificationTraining and Support. -Practice is key to success. -I believe in Practical Implementation instead of theoretical knowledge.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Interview Skills Training

10

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Snowflake Training

10

Teaching Experience in detail in Snowflake Training

Snowflake with AI – Comprehensive Data Engineering Course Course Duration: 70+ Hours | 45 Days Mode: Online Interactive LIVE Classes via Zoom Includes: Live Training, Practical Labs, Sample Data Models, Case Studies, Interview Preparation & Resume Guidance This comprehensive Snowflake with AI course covers Snowflake Data Engineering, DBT, AWS, Azure Data Factory (ADF), Oracle SQL, and Snowflake Cortex AI. The course is designed to help learners build practical skills through hands-on exercises and real-time projects. Course Syllabus Oracle SQL – Complementary Recordings RDBMS fundamentals, DDL, DML, DQL & TCL Operators, NULL handling, ORDER BY, GROUP BY & HAVING Constraints and different types of Joins Set Operators, Rank & Dense Rank Views and Indexes Snowflake Snowflake Architecture, Editions & Cloud Platforms SnowSQL, Roles & Users, Privileges Virtual Warehouses, Resource Monitors & Cost Management Databases, Schemas, Tables & Snowflake Cache Time Travel, Fail-safe & Zero-Copy Cloning Internal & External Stages Data Loading/Unloading and AWS-to-Snowflake Integration Snowpipe & Continuous Data Loading Semi-structured Data: JSON & XML Streams & Tasks, CDC and SCD Data Sharing, Secure Objects & Reader Accounts UDFs, Procedures & Analytical Functions Query Performance Tuning, Clustering & Micro-partitions Materialized Views & Search Optimization Column-Level & Row-Level Masking Policies Date & Timestamp Handling DBT DBT Cloud Setup & Snowflake Integration GitHub & Version Control Models, Dependencies & Materializations Sources, Seeds & Freshness Generic & Singular Tests Snapshots & Incremental Strategies Macros, Hooks, Exposures & Documentation BI Dashboard Integration Azure Data Factory (ADF) Azure Blob Storage ADF Pipeline Configuration Loading Data from Azure Blob Storage to Snowflake Snowflake Cortex AI Cortex Analyst for Natural Language Analytics Document AI for PDF & document understanding LLM Functions for summarization and classification Cortex Agents for AI-powered workflow orchestration Practical & Career Support Real-time project using Snowflake, DBT & ADF Basic Unix Commands Interview Questions Resume Building Career Opportunities Snowflake Developer, Data Engineer, Cloud Data Engineer, Data Warehouse Developer, Analytics Engineer, DBT Developer, ETL Developer, Snowflake Consultant, Data Platform Engineer and Data Analytics Engineer.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Full Stack Agentic AI

10

Teaching Experience in detail in Full Stack Agentic AI

I have experience in guiding and training learners on Full Stack Agentic AI concepts, with a focus on practical, real-world applications. My training covers the fundamentals of AI agents, agent workflows, RAG, prompt engineering, and building production-oriented AI applications. I have experience explaining and demonstrating technologies such as LangChain, LangGraph, FastAPI, AWS Bedrock, Lambda, Step Functions, DynamoDB, OpenSearch, and S3. I also help learners understand how to design agent-based workflows, integrate LLMs with tools and APIs, implement memory and state management, and build end-to-end AI applications. My teaching approach focuses on hands-on learning through real-time examples and projects, helping learners understand both the concepts and their practical implementation. I also support learners with troubleshooting, project discussions, interview-oriented questions, and guidance on applying Agentic AI concepts in real-world scenarios.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Full stack generative AI

10

Teaching Experience in detail in Full stack generative AI

Learn to build production-ready AI agents using AWS, LangChain, LangGraph, FastAPI, and Amazon Bedrock. Course Modules Agentic AI & Enterprise Patterns – Agents vs Workflows vs RAG, LangChain, LangGraph, and enterprise architecture. AWS Bedrock – Bedrock Runtime, Claude/Titan models, tool calling, reasoning, and Guardrails. FastAPI – Async APIs, streaming responses, request validation, and agent APIs. LangChain Fundamentals – PromptTemplate, Chains, Tools, Memory, and building a simple agent. LangGraph Fundamentals – Nodes, Edges, State, Conditional Routing, and single-agent workflows. LangChain + LangGraph – Agent integration, separation of concerns, failure isolation, and enterprise patterns. Agent Tool Design – Tool contracts, retries, timeouts, and idempotency. AWS Agent Tools – Lambda, Step Functions, SNS, DynamoDB, Rules Engine, and event-driven orchestration. Error Handling & Observability – Retries, fallbacks, compensation flows, CloudWatch metrics, logging, and tracing. RAG & Agent Memory – OpenSearch Vector Memory, embeddings, hybrid search, retrieval strategies, confidence scoring, and hallucination reduction. Security & Governance – IAM, API authentication, PII masking, audit logging, and compliance. Analytics – S3 and Snowflake for audit storage, agent metrics, cost, and latency dashboards. Hands-on Projects 🟢 Project 1: Intelligent Support Agent – Stateful retail support agent for order tracking, refunds, escalations, approvals, and notifications. 🔵 Project 2: Enterprise Multi-Agent Platform – Planner, Executor, Validator, and Human Approval agents with multi-agent messaging, long-term memory, human-in-the-loop workflows, and failure recovery. Final Deliverables ✔ End-to-end deployed AI agent system ✔ Enterprise GitHub repository ✔ Architecture diagrams ✔ Audit & observability dashboards ✔ Job-ready Agentic AI skills

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in AI Training

10

Teaching Experience in detail in AI Training

Learn to build production-ready AI agents using AWS, LangChain, LangGraph, FastAPI, and Amazon Bedrock. Course Modules: Agentic AI & Enterprise Patterns: Agents vs. Workflows vs. RAG, LangChain, LangGraph, and enterprise architecture. AWS Bedrock: Bedrock Runtime, Claude/Titan models, tool calling, reasoning, and Guardrails. FastAPI: Async APIs, streaming responses, request validation, and agent APIs. LangChain Fundamentals: PromptTemplate, Chains, Tools, Memory, and building a simple agent. LangGraph Fundamentals: Nodes, Edges, State, Conditional Routing, and single-agent workflows. LangChain + LangGraph: Agent integration, separation of concerns, failure isolation, and enterprise patterns. Agent Tool Design: Tool contracts, retries, timeouts, and idempotency. AWS Agent Tools: Lambda, Step Functions, SNS, DynamoDB, Rules Engine, and event-driven orchestration. Error Handling & Observability: Retries, fallbacks, compensation flows, CloudWatch metrics, logging, and tracing. RAG & Agent Memory: OpenSearch Vector Memory, embeddings, hybrid search, retrieval strategies, confidence scoring, and hallucination reduction. Security & Governance: IAM, API authentication, PII masking, audit logging, and compliance. Analytics: S3 and Snowflake for audit storage, agent metrics, cost, and latency dashboards. Hands-on Projects: Project 1: Intelligent Support Agent: Stateful retail support agent for order tracking, refunds, escalations, approvals, and notifications. Project 2: Enterprise Multi-Agent Platform: Planner, Executor, Validator, and Human Approval agents with multi-agent messaging, long-term memory, human-in-the-loop workflows, and failure recovery. Final Deliverables: - End-to-end deployed AI agent system - Enterprise GitHub repository - Architecture diagrams - Audit & observability dashboards - Job-ready Agentic AI skills