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Technical Support Engineer / AWS DevOps - New Batch going to start on 15th April 2026. 100% Job Guarantee.
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Azure Data Engineer With Gen AI

100% Placement Guarantee 100% Refundable Job Guarantee Course

Course Overview

DP -900 AZURE DATA FUNDAMENTALS 

Syllabus - Week 1

  • Explore core data concepts.
  • Explore relational data on Azure.
  • Explore non-relational data on Azure.
  • Explore Analytics workload on Azure.
  • DP 900 Certification.

Azure Data Factory

Syllabus - Week 2 - 4

  • Master Data Pipelines & Data Flow using ADF.
  • Handle variety of data source like: SQL server, Rest API, CSV/JSON , Parquet,ADLS.
  • Incremental & batch pipeline.
  • 4 Types of Trigger Implementation.
  • Parameterization.
  • Most Important: On-Prem to cloud Migration end to end.
  • 10 Practical ADF Implementation.
  • 10 Practical Assessment scenarios.
  • Pipeline Monitoring Debugging and Performance Optimization.
  • Real time project discussion and Master on ADF.

DP 203/DP 600/DP 700- Preparation

Syllabus - Week 5 -6

  • Azure Data Factory
  • Azure Synapse Analytics
  • Azure Stream Analytics
  • Azure Event Hubs
  • Azure Data Lake Storage
  • Azure Databricks
  • Fabric

Python for Data Science/Engineering

  • Syllabus - Week 6 - 7
  • Introduction of Python
  • Installing Python IDES – Python IDLE and Anaconda
  • Writing Your First Python Program
  • Data-types in Python
  • Variables in Python – Declaration and Use
  • Typecasting in Python
  • Operators in Python – Assignment, Logical, Arithmetic etc.
  • Taking User Input (Console)
  • Conditional Statements – If else and Nested If else and elif
  • Python Collections (Arrays) – List, Tuple, Sets and Dictionary
  • Loops in Python – For Loop, While Loop & Nested Loops
  • String Manipulation – Basic Operations, Slicing & Functions and Methods
  • User Defined Functions – Defining, Calling, Types of Functions, Arguments
  • Lambda Function
  • Importing Modules – Math Module.

Syllabus - Object Oriented Programming in Python

  • Basics of Object Oriented Programming
  • Creating Class and Object
  • Constructors in Python – Parameterized and Non-parameterized
  • Inheritance in Python
  • In built class methods and attributes
  • Multi-Level and Multiple Inheritance
  • Method Overriding and Data Abstraction
  • Encapsulation and Polymorphism.

Advanced version of SOL

Syllabus - Week 8

  • 1. Select
  • 2. Distinct
  • 3. IS NULL
  • 4. WHERE
  • 5. LIKE
  • 6. Order by
  • 7. Limit
  • 8. Case
  • 9. Rank and Dense Rank
  • 10. Union and Union all
  • 11. Exists
  • 12. Count and its variant
  • 13. Max, Min, Sum, Avg, etc
  • 14. Round, Trunc, Substr
  • 15. Alter, Update, Delete
  • 16. Create, Insert
  • 17. Listagg
  • 18. Level/Connect By
  • 1. And
  • 2. Or
  • 3. Greater Than
  • 4. Less Than
  • 1. Self join
  • 2. Inner join
  • 3. Cross join
  • 4. Left and Right outer join
  • 5. Full Outer Join
  • 1. CTE expression
  • 2. Independent and Correlated query
  • 3. Different keys
  • 4. Recursive CТЕ
  • 5. Pivot
  • 1. ROLL UP
  • 2. Cumulative Sum
  • 3. Lead
  • 4. Lag
  • 5. NTILE
  • 6. ROW_NUMBER
  • i) @LeetCode
  • ii) @HackerRank

DSA(easy to medium)

Syllabus - Week 9

  • ARRAYS
  • STRINGS
  • LINKED LIST
  • STACK
  • SORTING
  • SEARCHING
  • HASHING
  • TREES
  • QUEUE

Pyspark

Syllabus - Week 10

  • Environment Setup
  • Spark RDD
  • spark Caching
  • Common Transformations and Actions
  • Spark Functions
  • Key-Value Pairs
  • Aggregate Functions
  • Working with Aggregate Functions
  • Joins in Spark
  • Spark DataFrame
  • Spark Shared Variables
  • Custom Accumulator
  • Spark and Fault Tolerance
  • Broadcast variables
  • Numeric RDD Operations
  • Per-Partition Operations

Azure Data Bricks

Syllabus - Week 11-12

  • Understand data and Spark Architectures
  • Cluster Creation, Notebook Analysis
  • Azure Databricks Workspace creation, delta tables, spark SQL, cluster
  • management...
  • RDDs / DataFrame /Spark SQL
  • Data lake
  • Delta lake
  • Data Lakehouse architecture
  • In depth spark performance optimization
  • Partitioning bucketing caching, AQE and many more techniques
  • Spark Streaming in Depth
  • Databricks Project Implementation

Fabric Training Detailed

  • Introduction to Microsoft Fabric
  • Workspace Setup
  • Data Lakehouse: Deep Understanding
  • Data Ingestion and Data Processing Part 1
  • Data Ingestion and Data Processing Part 2 with Spark
  • Data Warehousing
  • Data Pipeline and Stream Data
  • End-to-End Project Part 1
  • End-to-End Project Part 2

REAL TIME PROJECT

Syllabus - Week 13

  • END TO END Project Implementation
  • Fabric Project
  • Gen -AI Project

INTERVIEW PREPARATION

Syllabus - Week 14 - 20

  • Interview Questions Preparation
  • Companies based interview
  • technique Package negotiation secret
  • skills Build Confidence Resume
  • Preparation/ Linkedin

GEN -AI

  • Introduction to Generative AI and Foundation Models (GPT, DALL·E, etc.)
  • Key concepts: LLMs, Transformers, and use cases across industries
  • Prompt Engineering: Techniques, best practices, and prompt types
  • Exploring tools: ChatGPT, Bard, Claude, and Hugging Face
  • Text generation, summarization, and Q&A with LLMs
  • Image and code generation with Gen AI models
  • Integration with Python and APIs (OpenAI, LangChain basics)
  • Business use cases: content creation, chatbots, automation
  • Responsible AI: bias, privacy, and ethical considerations
  • Final mini project + roadmap to Gen AI career paths


Refund Policy

NON REFUNDLE

Expected Salary Range
₹550,000 - ₹1,500,000
Per Annum (LPA)
Course Duration
6 to 8 Month Days
Online Training Fees
₹10,000

100% Refundable if placement not guaranteed

Offline Training Fees
₹10,000
Quick Info
  • Placement Guarantee: Yes
  • Refundable: Yes
  • Duration: 6 to 8 Month Days

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