📘 SQL Challenges for Data Analytics – With Explanation 🧠

(Beginner ➡️ Advanced)

1️⃣ Select Specific Columns

SELECT name, email FROM users;

This fetches only the name and email columns from the users table.

✔️ Used when you don’t want all columns from a table.


2️⃣ Filter Records with WHERE

SELECT * FROM users WHERE age > 30;

The WHERE clause filters rows where age is greater than 30.

✔️ Used for applying conditions on data.


3️⃣ ORDER BY Clause

SELECT * FROM users ORDER BY registered_at DESC;

Sorts all users based on registered_at in descending order.
✔️ Helpful to get latest data first.


4️⃣ Aggregate Functions (COUNT, AVG)

SELECT COUNT(*) AS total_users, AVG(age) AS avg_age FROM users;

Explanation:
- COUNT(*) counts total rows (users).
- AVG(age) calculates the average age.
✔️ Used for quick stats from tables.


5️⃣ GROUP BY Usage

SELECT city, COUNT(*) AS user_count FROM users GROUP BY city;

Groups data by city and counts users in each group.

✔️ Use when you want grouped summaries.


6️⃣ JOIN Tables

SELECT users.name, orders.amount  
FROM users  
JOIN orders ON users.id = orders.user_id;

Fetches user names along with order amounts by joining users and orders on matching IDs.
✔️ Essential when combining data from multiple tables.


7️⃣ Use of HAVING

SELECT city, COUNT(*) AS total  
FROM users  
GROUP BY city  
HAVING COUNT(*) > 5;

Like WHERE, but used with aggregates. This filters cities with more than 5 users.
✔️ **Use HAVING after GROUP BY.**


8️⃣ Subqueries

SELECT * FROM users  
WHERE salary > (SELECT AVG(salary) FROM users);

Finds users whose salary is above the average. The subquery calculates the average salary first.

✔️ Nested queries for dynamic filtering9️⃣ CASE Statementnt**

SELECT name,  
  CASE  
    WHEN age < 18 THEN 'Teen'  
    WHEN age <= 40 THEN 'Adult'  
    ELSE 'Senior'  
  END AS age_group  
FROM users;

Adds a new column that classifies users into categories based on age.
✔️ Powerful for conditional logic.

🔟 Window Functions (Advanced)

SELECT name, city, score,  
  RANK() OVER (PARTITION BY city ORDER BY score DESC) AS rank  
FROM users;

Ranks users by score *within each city*.

SQL Learning Series: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v/1075

𝟮𝟱+ 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂𝗿 𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 😍

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📖 Data Engineering Roadmap 2025

𝟭. 𝗖𝗹𝗼𝘂𝗱 𝗦𝗤𝗟 (𝗔𝗪𝗦 𝗥𝗗𝗦, 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗦𝗤𝗟, 𝗔𝘇𝘂𝗿𝗲 𝗦𝗤𝗟)

💡 Why? Cloud-managed databases are the backbone of modern data platforms.

✅ Serverless, scalable, and cost-efficient
✅ Automated backups & high availability
✅ Works seamlessly with cloud data pipelines

𝟮. 𝗱𝗯𝘁 (𝗗𝗮𝘁𝗮 𝗕𝘂𝗶𝗹𝗱 𝗧𝗼𝗼𝗹) – 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗘𝗟𝗧

💡 Why? Transform data inside your warehouse (Snowflake, BigQuery, Redshift).

✅ SQL-based transformation – easy to learn
✅ Version control & modular data modeling
✅ Automates testing & documentation

𝟯. 𝗔𝗽𝗮𝗰𝗵𝗲 𝗔𝗶𝗿𝗳𝗹𝗼𝘄 – 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻

💡 Why? Automate and schedule complex ETL/ELT workflows.

✅ DAG-based orchestration for dependency management
✅ Integrates with cloud services (AWS, GCP, Azure)
✅ Highly scalable & supports parallel execution

𝟰. 𝗗𝗲𝗹𝘁𝗮 𝗟𝗮𝗸𝗲 – 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗔𝗖𝗜𝗗 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲𝘀

💡 Why? Solves data consistency & reliability issues in Apache Spark & Databricks.
✅ Supports ACID transactions in data lakes
✅ Schema evolution & time travel
✅ Enables incremental data processing

𝟱. 𝗖𝗹𝗼𝘂𝗱 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲𝘀 (𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲, 𝗕𝗶𝗴𝗤𝘂𝗲𝗿𝘆, 𝗥𝗲𝗱𝘀𝗵𝗶𝗳𝘁)

💡 Why? Centralized, scalable, and powerful for analytics.
✅ Handles petabytes of data efficiently
✅ Pay-per-use pricing & serverless architecture

𝟲. 𝗔𝗽𝗮𝗰𝗵𝗲 𝗞𝗮𝗳𝗸𝗮 – 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴

💡 Why? For real-time event-driven architectures.
✅ High-throughput

𝟳. 𝗣𝘆𝘁𝗵𝗼𝗻 & 𝗦𝗤𝗟 – 𝗧𝗵𝗲 𝗖𝗼𝗿𝗲 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴

💡 Why? Every data engineer must master these!

✅ SQL for querying, transformations & performance tuning
✅ Python for automation, data processing, and API integrations

𝟴. 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 – 𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗔𝗜

💡 Why? The go-to platform for big data processing & machine learning on the cloud.

✅ Built on Apache Spark for fast distributed computing

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Lol 🤣

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?

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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😍

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

FREE RESOURCES TO LEARN DATA ENGINEERING
👇👇

Big Data and Hadoop Essentials free course

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[4.6 stars out of 5]

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Understanding Data Engineering from Datacamp

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Data Engineering Free Books

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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😍

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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!