Hi All

🟢Want to become Data Engineer — If Yes then I recommend this Place to you Give a Try ⤵️

Join the free WhatsApp group for schedule + seats and become a Microsoft Fabric Data Engineer:
👉 https://chat.whatsapp.com/LCOyVT6Ll6n1zvF7jUwP0H
⏰ 8:00–9:00 AM IST | Mon–Fri | 6 weeks

👨‍🏫 Aditya Chandak, Microsoft Certified Trainer (11+ yrs)

You'll build: OneLake & Lakehouse, Data Factory pipelines, SQL + PySpark, Power BI on Fabric, and one end-to-end project.

One hour a day before work starts. That's the whole ask.

🚨 Do you know where *Data Engineering is heading in 2026?*

🔥 *Microsoft Fabric is becoming one of the most in-demand Data Engineering technologies,* with growing enterprise adoption and demand for Fabric Data Engineers. (Microsoft Learn)

💰 Build the right skills and target  *30–80 LPA+ opportunities in India + high-paying Data Engineering opportunities globally*.


Want to become a *Microsoft Fabric Data Engineer?*

📲 Join our FREE WhatsApp Community for updates, opportunities & learning:

https://chat.whatsapp.com/Eb4ffcrFsdW2BNTAgC6VpB?s=sh&p=i&mlu=4&amv=2

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🚨 Free 3-hour live class this Sunday
📊 Data Modelling — we design a full food-delivery schema on screen
📅 30 Aug · 🕛 12 PM IST · 💻 Zoom · 💸 Free
🔑 Keys, ERD, 1NF→3NF, live Q&A
👉 prepnplaced.com/webinar/data-modelling

One-liner for status/DM

📊 Free 3-hr live Data Modelling class, Sun 30 Aug, 12 PM IST 👉 prepnplaced.com/webinar/data-modelling 💸 Free seat, notes included.

🏏 FREE LIVE: PySpark on Cricket Data — End-to-End in Databricks

👉 https://www.prepnplaced.com/webinar/pyspark-cricket-project

Saturday, 29 August · 12:00 PM IST · 2 hours · Zoom · Free
Most PySpark practice stops at .show(). This session runs all the way to a Delta table.

What we build in 2 hours:

✅ Load raw ball-by-ball match data into Databricks
✅ groupBy, window functions, joins and UDFs
✅ Charts: top scorers, run rate, win margins
✅ Tuning: cache(), partitions, shuffle settings
✅ Save the processed output to Delta
✅ Live Q&A at the end

Who it’s for: anyone preparing for Data Engineering roles who needs a finished project to point at. Basic Python or SQL is enough, no prior Spark work needed.

You get: live hands-on session, live Q&A, and session notes + resources by email. Recording optional at ₹99.
Host: Durgesh Yadav — Founder @ PrepNPlaced, Sr. Data Engineer @ 7-Eleven, ex-Target

Reserve your free seat 👇
https://www.prepnplaced.com/webinar/pyspark-cricket-project

Get Gitlab End to End Notes for Data Engineering - https://www.linkedin.com/posts/yadavdurgesh711_dataengineering-gitlab-cicd-activity-7499440282107596800-u4AI?utm_source=share&utm_medium=member_ios&rcm=ACoAACzhe4oBmzvkaOq0H_uTda6krr_d7DSxObs

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Join Netflix Webinar on System Design by me

Click the Link - https://us06web.zoom.us/j/82084947108?pwd=ctzqc2t9EzNXfyVvlR7JzIwv7LM8zc.1

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|── Foundations (Business + Analytics Thinking)
| ├── What is Data Analysis?
| ├── Types of Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
| ├── Business Metrics (Revenue, Profit, Growth, Retention, CAC, LTV)
| ├── KPI vs Metrics
| ├── Data-driven Decision Making
| ├── Problem Solving Framework
| └── Asking Business Questions
|
|── Excel (Core Tool – Still Widely Used)
| ├── Basics (Cells, Sheets, Formatting)
| ├── Formulas (SUM, IF, COUNT, AVERAGE)
| ├── Lookup Functions (VLOOKUP, XLOOKUP, INDEX-MATCH)
| ├── Pivot Tables & Pivot Charts
| ├── Data Cleaning (Text functions, Remove duplicates)
| ├── Conditional Formatting
| ├── Basic Dashboards
| └── Excel Automation (Basic Macros)
|
|── Python for Data Analysis
| ├── Python Basics (Variables, Data Types)
| ├── Control Flow (if, for, while)
| ├── Functions
| ├── Error Handling (try-except)
| ├── Data Structures (List, Tuple, Set, Dictionary)
| ├── List & Dict Comprehensions
| ├── NumPy (Arrays, Vectorization)
| ├── Pandas (DataFrames, Cleaning, Transformation)
| ├── GroupBy & Aggregations
| ├── Merge, Join, Pivot
| ├── Time Series Basics
| ├── Data Visualization (Matplotlib, Seaborn)
| └── Automation Scripts
|
|── SQL (Core Skill – Must Have)
| ├── SELECT, WHERE, ORDER BY
| ├── Joins (INNER, LEFT, RIGHT, FULL)
| ├── GROUP BY & Aggregations
| ├── CASE WHEN
| ├── Subqueries
| ├── CTEs
| ├── Window Functions
| ├── Data Cleaning in SQL
| └── Query Optimization
|
|── Data Visualization & BI Tools
| ├── Power BI
| │ ├── Data Loading
| │ ├── Data Modeling
| │ ├── Relationships
| │ ├── DAX (Measures, CALCULATE, Time Intelligence)
| │ ├── Dashboard Design
| │ └── Publishing & Sharing
| │
| ├── Tableau (Optional)
| │ ├── Worksheets & Dashboards
| │ ├── Calculated Fields
| │ ├── Filters & Parameters
| │ └── Storytelling
| │
| └── Dashboard Best Practices
| ├── UX/UI Design
| ├── KPI Visualization
| └── Storytelling with Data
|
|── Statistics for Data Analysts
| ├── Descriptive Statistics (Mean, Median, Mode)
| ├── Variance & Standard Deviation
| ├── Distribution Basics
| ├── Correlation
| ├── A/B Testing Basics
| ├── Hypothesis Testing
| └── Confidence Intervals
|
|── Data Cleaning & Preparation
| ├── Handling Missing Values
| ├── Removing Duplicates
| ├── Data Type Conversion
| ├── Outlier Detection
| ├── Data Validation
| └── Data Standardization
|
|── Data Analysis Techniques
| ├── Trend Analysis
| ├── Cohort Analysis
| ├── Funnel Analysis
| ├── Retention Analysis
| ├── Segmentation (RFM Analysis)
| └── Root Cause Analysis
|
|── Data Engineering Basics (High Demand 🔥)
| ├── OLTP vs OLAP
| ├── Data Warehousing Concepts
| ├── Fact & Dimension Tables
| ├── Star Schema
| ├── Snowflake Schema
| ├── ETL vs ELT
| ├── Data Pipelines
| ├── dbt (Data Transformation) ⭐️
| └── Apache Airflow (Basics)
|
|── Cloud & Modern Data Stack (2026 Must 🚀)
| ├── Cloud Platforms
| │ ├── AWS (S3, Redshift Basics)
| │ ├── Google BigQuery ⭐️
| │ └── Azure Synapse
| │
| ├── Data Platforms
| │ ├── Snowflake ⭐️
| │ ├── BigQuery
| │ ├── Amazon Redshift
| │ └── Databricks (Basics)
| │
| └── Data Storage Concepts
| ├── Data Lakes
| ├── Data Warehouses
| └── Lakehouse Architecture
|
|── AI & Automation for Analysts (Game Changer 🔥)
| ├── ChatGPT for SQL & Python
| ├── Copilot for Coding
| ├── Prompt Engineering Basics
| ├── Automated Reporting
| ├── Smart Dashboards
| └── AI-assisted Data Analysis
|
|── Real-World Data Analyst Workflow
| ├── Data Collection (SQL, APIs, Files)
| ├── Data Cleaning
| ├── Data Analysis
| ├── Visualization
| ├── Insight Generation
| └── Stakeholder Communication
|
|── Projects (MOST IMPORTANT)
| ├── Beginner
| │ ├── Sales Analysis
| │ └── Customer Segmentation
| │
| ├── Intermediate
| │ ├── E-commerce Dashboard
| │ ├── Retention Analysis
| │ └── KPI Dashboard
| │
| ├── Advanced
| │ ├── End-to-End Data Pipeline
| │ ├── Real-Time Dashboard
| │ └── Business Case Study
|

👉WhatsApp: https://whatsapp.com/channel/0029VaFZ2LbKGGGRCU0lnd46

👉Telegram: https://t.me/dataanalyticsbuddy

Till then keep learning & keep exploring 🙌☺️

🚀 DE Cohort 4 — Orientation Session

Hey Everyone! 👋

Please join us tomorrow at 7:00 AM IST for the Data Engineering Cohort 4 Orientation Session.

🔗 Session Link:
https://us06web.zoom.us/j/82084947108?pwd=ctzqc2t9EzNXfyVvlR7JzIwv7LM8zc.1

✨ What We’ll Cover:
• Understand the complete DE Cohort 4 journey
• Explore the curriculum, learning roadmap & projects
• Understand how the cohort is structured
• Know what you can expect throughout the program
• Learn how we prepare you for Data Engineering roles in product-based companies
• Get clarity on the learning process, mentorship and career support

🌱 This orientation is a great opportunity to understand how the cohort can help you build the skills, projects and interview readiness required to move toward product-based companies.

📚 Explore the Cohort:
https://www.prepnplaced.com/courses

🔥 DE Cohort 4 starts with the right foundation. See you all at 7 AM!

Let's build, learn and grow together. 🚀

Visit prepnplaced.com Today & Learn from Us ❤️

Hi All, I have Created a Free Platform where you People can get best Job as a Fresher Click on the Link - https://www.prepnplaced.com/apprenticeships

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Regards,
Durgesh Yadav

Visit prepnplaced.com/courses & Enroll Today for your Next Perfect Switch 🚀