Use my uploaded image as the exact identity and facial reference. Create a realistic cinematic photo of me sitting in the driver's seat of a modern car, facing the camera with a calm, confident expression.
Keep my facial features, hairstyle, skin tone, and overall appearance recognizable and natural.
Match the reference scene: close-up framing from inside the car, visible steering wheel at the bottom, beige leather seat/interior, dark tinted surroundings, strong natural sunlight and realistic shadows. Use a slightly low front-facing camera angle, authentic photography, sharp facial details, subtle cinematic contrast, and realistic skin texture.
Output must be exactly 3:4 portrait. No text, Ul elements, logos, watermarks, icons, or extra people.

drive link : https://drive.google.com/file/d/1tDt-DYB2jrddHIoaaMqIAUqWrmxYKae4/view?usp=sharing

✅SQL Roadmap: Step-by-Step Guide to Master SQL 🧠💻

Whether you're aiming to be a backend dev, data analyst, or full-time SQL pro — this roadmap has got you covered 👇

📍 1. SQL Basics
⦁  SELECT, FROM, WHERE
⦁  ORDER BY, LIMIT, DISTINCT 
   Learn data retrieval & filtering.

📍 2. Joins Mastery
⦁  INNER JOIN, LEFT/RIGHT/FULL OUTER JOIN
⦁  SELF JOIN, CROSS JOIN 
   Master table relationships.

📍 3. Aggregate Functions
⦁  COUNT(), SUM(), AVG(), MIN(), MAX() 
   Key for reporting & analytics.

📍 4. Grouping Data
⦁  GROUP BY to group
⦁  HAVING to filter groups 
   Example: Sales by region, top categories.

📍 5. Subqueries & Nested Queries
⦁  Use subqueries in WHERE, FROM, SELECT
⦁  Use EXISTS, IN, ANY, ALL 
   Build complex logic without extra joins.

📍 6. Data Modification
⦁  INSERT INTO, UPDATE, DELETE
⦁  MERGE (advanced) 
   Safely change dataset content.

📍 7. Database Design Concepts
⦁  Normalization (1NF to 3NF)
⦁  Primary, Foreign, Unique Keys 
   Design scalable, clean DBs.

📍 8. Indexing & Query Optimization
⦁  Speed queries with indexes
⦁  Use EXPLAIN, ANALYZE to tune 
   Vital for big data/enterprise work.

📍 9. Stored Procedures & Functions
⦁  Reusable logic, control flow (IF, CASE, LOOP) 
   Backend logic inside the DB.

📍 10. Transactions & Locks
⦁  ACID properties
⦁  BEGIN, COMMIT, ROLLBACK
⦁  Lock types (SHARED, EXCLUSIVE) 
   Prevent data corruption in concurrency.

📍 11. Views & Triggers
⦁  CREATE VIEW for abstraction
⦁  TRIGGERS auto-run SQL on events 
   Automate & maintain logic.

📍 12. Backup & Restore
⦁  Backup/restore with tools (mysqldump, pg_dump) 
   Keep your data safe.

📍 13. NoSQL Basics (Optional)
⦁  Learn MongoDB, Redis basics
⦁  Understand where SQL ends & NoSQL begins.

📍 14. Real Projects & Practice
⦁  Build projects: Employee DB, Sales Dashboard, Blogging System
⦁  Practice on LeetCode, StrataScratch, HackerRank

📍 15. Apply for SQL Dev Roles
⦁  Tailor resume with projects & optimization skills
⦁  Prepare for interviews with SQL challenges
⦁  Know common business use cases

💡 Pro Tip: Combine SQL with Python or Excel to boost your data career options.

💬 Double Tap ♥️ For More!

Infosys Interview Experience | Position: Data Engineer | Experience: 𝟯+ 𝘆𝗲𝗮𝗿𝘀.

𝗥𝗼𝘂𝗻𝗱 𝟭 - 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝟭 (𝗣𝘆𝗦𝗽𝗮𝗿𝗸 | 𝗦𝗤𝗟 | 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴)
1️⃣ Explain your end-to-end data pipeline — from ingestion to data transformation and loading.
2️⃣ How do you optimize PySpark jobs to handle large data volumes efficiently?
3️⃣ What are the differences between repartition(), coalesce(), and repartitionByRange() in Spark?
4️⃣ Write a PySpark job to calculate the top 3 products sold in each region.
5️⃣ SQL question: Find the nth highest salary using different approaches.
6️⃣ Explain how you handle null values, duplicates, and bad records in ETL processing.

𝗥𝗼𝘂𝗻𝗱 𝟮 - 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝟮 (𝗔𝗭𝗨𝗥𝗘 𝗗𝗔𝗧𝗔 𝗙𝗔𝗖𝗧𝗢𝗥𝗬 | 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 | 𝗗𝗲𝗹𝘁𝗮 𝗟𝗮𝗸𝗲)
1️⃣ How do you design an ADF pipeline for incremental data loads between databases?
2️⃣ What are triggers in ADF, and how do you schedule pipelines efficiently?
3️⃣ How does Delta Lake handle ACID transactions and schema evolution?
4️⃣ What are the advantages of using Data Lakehouse architecture over a traditional warehouse?
5️⃣ Explain SCD Type 1, Type 2, and Type 3 — which one have you implemented and why?
6️⃣ How do you monitor and handle pipeline failures or retries in ADF or Databricks?

𝗥𝗼𝘂𝗻𝗱 𝟯 - 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝗶𝗮𝗹 / 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹
1️⃣ Describe a situation where you faced a data pipeline failure in production. How did you troubleshoot and fix it?
2️⃣ How do you communicate technical issues to non-technical stakeholders?
3️⃣ What’s your approach when a deadline is tight and data validation is still pending?
4️⃣ How do you manage work priorities across multiple projects?
5️⃣ Tell me about a time you optimized cost or performance in a cloud-based data solution.