𝐈𝐟 𝐲𝐨𝐮'𝐫𝐞 𝐚 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐛𝐢𝐠 𝐝𝐚𝐭𝐚 - 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐢𝐬 𝐲𝐨𝐮𝐫 𝐛𝐞𝐬𝐭 𝐟𝐫𝐢𝐞𝐧𝐝.⁣
⁣
Whether you're building data pipelines, transforming terabytes of logs, or cleaning data for analytics, PySpark helps you scale Python across distributed systems with ease.⁣
⁣
Here are a few PySpark fundamentals every Data Engineer should be confident with:⁣
⁣
𝟏. 𝐑𝐞𝐚𝐝𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭𝐥𝐲⁣
⁣
spark.read.csv(), json(), parquet()⁣
⁣
Choose the right format for performance.⁣
⁣
𝟐. 𝐂𝐨𝐫𝐞 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧𝐬⁣
⁣
map, flatMap, filter, union⁣
⁣
Understand how these shape your RDDs or DataFrames.⁣
⁣
𝟑. 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐭 𝐬𝐜𝐚𝐥𝐞⁣
⁣
groupBy, agg, .count()⁣
⁣
Use them to build clean summaries and insights from raw data.⁣
⁣
𝟒. 𝐂𝐨𝐥𝐮𝐦𝐧 𝐦𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧𝐬⁣
⁣
withColumn() is a go-to tool for feature engineering or adding derived columns.⁣
⁣
Data Engineering is about building scalable, reliable, and efficient systems-and PySpark makes that possible when you're working with huge datasets.

React ♥️ for more

𝟯 𝗚𝗮𝗺𝗲-𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲😍

Want to break into Data Science or Tech?

Python is the #1 skill you need — and starting is easier than you think.🧑‍💻✨️

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3JemBIt

Your career upgrade starts today — no excuses!✅️

Roadmap to Become a Data Engineer in 10 Stages

Stage 1 → SQL & Database Fundamentals
Stage 2 → Python for Data Engineering (Pandas, PySpark)
Stage 3 → Data Modelling & ETL/ELT Design (Star Schema, CDC, DWH)
Stage 4 → Big Data Tools (Apache Spark, Kafka, Hive)
Stage 5 → Cloud Platforms (Azure / AWS / GCP)
Stage 6 → Data Orchestration (Airflow, ADF, Prefect, DBT)
Stage 7 → Data Lakes & Warehouses (Delta Lake, Snowflake, BigQuery)
Stage 8 → Monitoring, Testing & Governance (Great Expectations, DataDog)
Stage 9 → Real-Time Pipelines (Kafka, Flink, Kinesis)
Stage 10 → CI/CD & DevOps for Data (GitHub Actions, Terraform, Docker)

👉 You don’t need to learn everything at once.
👉 Build around one stack, skip a few steps if you’re just starting out.
👉 Master fundamentals first, then move to the cloud.

The key is consistency → take it step by step and grow your skill set!

𝟒 𝐁𝐞𝐬𝐭 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 𝐢𝐧 𝟐𝟎𝟐𝟓 𝐭𝐨 𝐒𝐤𝐲𝐫𝐨𝐜𝐤𝐞𝐭 𝐘𝐨𝐮𝐫 𝐂𝐚𝐫𝐞𝐞𝐫😍

In today’s data-driven world, Power BI has become one of the most in-demand tools for businesses〽️📊

The best part? You don’t need to spend a fortune—there are free and affordable courses available online to get you started.💥🧑‍💻

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4mDvgDj

Start learning today and position yourself for success in 2025!✅️

FREE RESOURCES TO LEARN DATA ENGINEERING
👇👇

Big Data and Hadoop Essentials free course

https://bit.ly/3rLxbul

Data Engineer: Prepare Financial Data for ML and Backtesting FREE UDEMY COURSE
[4.6 stars out of 5]

https://bit.ly/3fGRjLu

Understanding Data Engineering from Datacamp

https://clnk.in/soLY

Data Engineering Free Books

https://ia600201.us.archive.org/4/items/springer_10.1007-978-1-4419-0176-7/10.1007-978-1-4419-0176-7.pdf

https://www.darwinpricing.com/training/Data_Engineering_Cookbook.pdf

Big Data of Data Engineering Free book

https://databricks.com/wp-content/uploads/2021/10/Big-Book-of-Data-Engineering-Final.pdf

https://aimlcommunity.com/wp-content/uploads/2019/09/Data-Engineering.pdf

The Data Engineer’s Guide to Apache Spark

https://t.me/datasciencefun/783?single

Data Engineering with Python

https://t.me/pythondevelopersindia/343

Data Engineering Projects -

1.End-To-End From Web Scraping to Tableau  https://lnkd.in/ePMw63ge

2. Building Data Model and Writing ETL Job https://lnkd.in/eq-e3_3J

3. Data Modeling and Analysis using Semantic Web Technologies https://lnkd.in/e4A86Ypq

4. ETL Project in Azure Data Factory - https://lnkd.in/eP8huQW3

5. ETL Pipeline on AWS Cloud - https://lnkd.in/ebgNtNRR

6. Covid Data Analysis Project - https://lnkd.in/eWZ3JfKD

7. YouTube Data Analysis 
   (End-To-End Data Engineering Project) - https://lnkd.in/eYJTEKwF

8. Twitter Data Pipeline using Airflow - https://lnkd.in/eNxHHZbY

9. Sentiment analysis Twitter:
    Kafka and Spark Structured Streaming -  https://lnkd.in/esVAaqtU

ENJOY LEARNING 👍👍

𝟰 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝘂𝗹𝗲𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀😍

Generative AI is no longer just a buzzword—it’s a career-maker🧑‍💻📌

Recruiters are actively looking for candidates with prompt engineering skills, hands-on AI experience, and the ability to use tools like GitHub Copilot and Azure OpenAI effectively.🖥

𝐋𝐢𝐧𝐤👇:-

http://pdlink.in/4fKT5pL

If you’re looking to stand out in interviews, land AI-powered roles, or future-proof your career, this is your chance

📌 🚀 How to Build a Personal Brand as a Data Analyst

Want to stand out in the competitive job market? Build your personal brand using these strategies:

✅ 1. Share Your Work Publicly – Post SQL/Python projects on LinkedIn, Medium, or GitHub.

✅ 2. Engage with Data Communities – Follow & contribute to Kaggle, DataCamp, or Analytics Vidhya.

✅ 3. Write About Data – Share blog posts on real-world data insights & case studies.

✅ 4. Present at Meetups/Webinars – Gain visibility & network with industry experts.

✅ 5. Optimize LinkedIn & GitHub – Highlight your skills, certifications, and projects.


💡 Start with one personal branding activity this week.

Q: How do you import data from various sources (Excel, SQL Server, CSV) into Power BI?

A: Here’s how to handle multi-source imports in Power BI Desktop:

1. Excel:

° Go to Home > Get Data > Excel

° Select your file & sheets or tables



2. CSV:

° Choose Get Data > Text/CSV

° Browse and load the file



3. SQL Server:

° Select Get Data > SQL Server

° Enter server/database name

° Use a query or select tables directly



4. Combine Sources:

° Use Power Query to transform, merge, or append tables

° Create relationships in the Model view


Pro Tip:
Use consistent data types and naming to make transformations smoother across sources!

ChatGPT Prompt to learn any skill
👇👇
I am seeking to become an expert professional in [Making ChatGPT prompts perfectly]. I would like ChatGPT to provide me with a complete course on this subject, following the principles of Pareto principle and simulating the complexity, structure, duration, and quality of the information found in a college degree program at a prestigious university. The course should cover the following aspects: Course Duration: The course should be structured as a comprehensive program, spanning a duration equivalent to a full-time college degree program, typically four years. Curriculum Structure: The curriculum should be well-organized and divided into semesters or modules, progressing from beginner to advanced levels of proficiency. Each semester/module should have a logical flow and build upon the previous knowledge. Relevant and Accurate Information: The course should provide all the necessary and up-to-date information required to master the skill or knowledge area. It should cover both theoretical concepts and practical applications. Projects and Assignments: The course should include a series of hands-on projects and assignments that allow me to apply the knowledge gained. These projects should range in complexity, starting from basic exercises and gradually advancing to more challenging real-world applications. Learning Resources: ChatGPT should share a variety of learning resources, including textbooks, research papers, online tutorials, video lectures, practice exams, and any other relevant materials that can enhance the learning experience. Expert Guidance: ChatGPT should provide expert guidance throughout the course, answering questions, providing clarifications, and offering additional insights to deepen understanding. I understand that ChatGPT's responses will be generated based on the information it has been trained on and the knowledge it has up until September 2021. However, I expect the course to be as complete and accurate as possible within these limitations. Please provide the course syllabus, including a breakdown of topics to be covered in each semester/module, recommended learning resources, and any other relevant information

(Tap on above text to copy)

🚀 PyTorch vs TensorFlow – Which Should YOU Choose?

If you’re starting in AI or planning to build real-world apps, this is the big question.

👉 PyTorch – simple, feels like Python, runs instantly. Perfect for learning, experiments, and research.
👉 TensorFlow – built by Google, comes with a full production toolkit (mobile, web, cloud). Perfect for apps at scale.

✨ Developer Experience: PyTorch is beginner-friendly. TensorFlow has improved with Keras but still leans towards production use.
📊 Research vs Production: 75% of research papers use PyTorch, but TensorFlow powers large-scale deployments.

💡 Think of it like this:
PyTorch = Notebook for experiments ✍️
TensorFlow = Office suite for real apps 🏢

So the choice is simple:

Learning & Research → PyTorch

Scaling & Deployment → TensorFlow

Amazon Interview Process for Data Scientist position

📍Round 1- Phone Screen round
This was a preliminary round to check my capability, projects to coding, Stats, ML, etc.

After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day).

📍 𝗥𝗼𝘂𝗻𝗱 𝟮- 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗕𝗿𝗲𝗮𝗱𝘁𝗵:
In this round the interviewer tested my knowledge on different kinds of topics.

📍𝗥𝗼𝘂𝗻𝗱 𝟯- 𝗗𝗲𝗽𝘁𝗵 𝗥𝗼𝘂𝗻𝗱:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.

📍𝗥𝗼𝘂𝗻𝗱 𝟰- 𝗖𝗼𝗱𝗶𝗻𝗴 𝗥𝗼𝘂𝗻𝗱-
This was a Python coding round, which I cleared successfully.

📍𝗥𝗼𝘂𝗻𝗱 𝟱- This was 𝗛𝗶𝗿𝗶𝗻𝗴 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 where my fitment for the team got assessed.

📍𝗟𝗮𝘀𝘁 𝗥𝗼𝘂𝗻𝗱- 𝗕𝗮𝗿 𝗥𝗮𝗶𝘀𝗲𝗿- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.

So, here are my Tips if you’re targeting any Data Science role:
-> Never make up stuff & don’t lie in your Resume.
-> Projects thoroughly study.
-> Practice SQL, DSA, Coding problem on Leetcode/Hackerank.
-> Download data from Kaggle & build EDA (Data manipulation questions are asked)

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

ENJOY LEARNING 👍👍