🔥 New YouTube Video Alert

Can You Solve This Deloitte SQL Interview Question?

You’re given a products table where the category is available only for the first product, while the following rows contain NULL.

Beverages → Coca Cola
NULL → Pepsi
NULL → Sprite
NULL → Fanta

Snacks → Lays
NULL → Doritos
NULL → Kurkure

🎯 Challenge:
How would you fill those NULL category values with the correct category?

💡 Hint: Think about Window Functions 👀

This is a great SQL problem to test your understanding of NULL handling + data transformation.

👇 What would you use?

🅰️ LAG()
🅱️ LEAD()
🅲️ LAST_VALUE()
🅳️ ROW_NUMBER()

🎬 Solution video
Fill Missing Categories in SQL | Hard Deloitte Interview Question
https://youtu.be/IX9NBK3Yx-M
🚀

Check it out now and let me know in comments ,What did you learn?

#SQL #SQLInterview #Deloitte #SQLChallenge #DataEngineering #Snowflake

🚀 Azure and ETL data engineering roles worth exploring

If you are working with SQL, Spark, Airflow, Databricks, or cloud pipelines, these roles align well with practical data engineering workflows.

💼 Role: Azure Data Engineer
🏢 Company: Tata Consultancy Services
📍 Location: Bengaluru
🌎 Work mode: onsite
✨ Why it is worth checking: Strong Azure stack with ADF, Databricks, Synapse, PySpark, ETL/ELT, and data warehousing exposure.
🔗 https://artha.link/@adityabansal095/jobs/azure-data-engineer-tata-consultancy-bengaluru-abb0b3eb

💼 Role: Senior Data Engineer
🏢 Company: Satwic
📍 Location: India
🌎 Work mode: remote
✨ Why it is worth checking: Good fit for engineers working on Azure migrations, Synapse, Databricks, Spark, and large-scale data pipelines.
🔗 https://artha.link/@adityabansal095/jobs/senior-data-engineer-satwic-inc-india-8610633d

💼 Role: Data Engineer
🏢 Company: TIGI HR
📍 Location: Gurugram
🌎 Work mode: remote
✨ Why it is worth checking: Covers Airflow, ADF, SQL, Python, ETL workflows, and data warehouse projects.
🔗 https://artha.link/@adityabansal095/jobs/data-engineer-tigi-hr-gurugram-e0612e8f

Share with someone preparing for their next data engineering move.

#DataEngineering #AzureDataEngineer #SQL #Databricks #ETL #Airflow #CloudDataEngineering

This is me @adityabansal095

You can ping me directly here if you need any assistance or any help.

Power BI Roadmap: Step-by-Step Guide to Master Power BI 📊💻

Whether you're aiming to be a data analyst, business intelligence pro, or dashboard expert - this roadmap has you covered 👇

📍 1. Power BI Basics
⦁ Get familiar with Power BI Desktop interface
⦁ Connect to data sources (Excel, CSV, databases)
⦁ Learn Basic visualizations: tables, charts, slicers

📍 2. Data Transformation & Modeling
⦁ Use Power Query Editor to clean & shape data
⦁ Create relationships between tables
⦁ Understand data types & formats

📍 3. DAX Fundamentals
⦁ Master calculated columns & measures
⦁ Learn core functions: SUM, CALCULATE, FILTER, RELATED
⦁ Use variables and time intelligence functions

📍 4. Advanced Visualizations
⦁ Build interactive reports and dashboards
⦁ Use bookmarks, buttons & drill-throughs
⦁ Customize visuals & layouts for storytelling

📍 5. Data Refresh & Gateway
⦁ Set up scheduled refresh with data gateways
⦁ Understand live vs import modes
⦁ Manage refresh performance

📍 6. Row-Level Security (RLS)
⦁ Learn to restrict data access by user roles
⦁ Implement roles & test security in reports

📍 7. Power BI Service & Collaboration
⦁ Publish reports to Power BI Service
⦁ Share dashboards and collaborate with teams
⦁ Use workspaces, apps, and permissions

📍 8. Power BI Mobile & Embedded
⦁ Optimize reports for mobile devices
⦁ Embed Power BI visuals in apps or websites

📍 9. Performance Optimization
⦁ Use Performance Analyzer to tune reports
⦁ Optimize data models & DAX queries
⦁ Best practices for large datasets

📍 10. Power BI API & Automation
⦁ Use Power BI REST API for automation
⦁ Integrate with Power Automate & Azure services

📍 11. Real Projects & Practice
⦁ Build sample dashboards: Sales, Marketing, Finance
⦁ Join challenges on platforms like Enterprise DNA, Radacad

📍 12. Certification & Career Growth
⦁ Prepare for DA-100 / PL-300 certification
⦁ Build portfolio & LinkedIn presence
⦁ Apply for BI Analyst & Power BI Developer roles

💡 Pro Tip: Combine Power BI skills with SQL and Python for a powerful data career combo!

Pandas Interview Q&A (Frequently Asked)

📊 Q1. What is Pandas and why is it used?

👉 Python library for data manipulation & analysis
👉 Provides powerful data structures like DataFrame & Series
👉 Used for cleaning, transforming, and analyzing data

📊 Q2. What is the difference between Series and DataFrame?

👉 Series → 1D labeled array (single column)
👉 DataFrame → 2D tabular structure (rows & columns)
👉 DataFrame is a collection of multiple Series

📊 Q3. How do you handle missing values in Pandas?

👉 isnull() / notnull() to detect missing values
👉 fillna() to replace missing data
👉 dropna() to remove missing records

📊 Q4. What is the difference between loc[] and iloc[]?

👉 loc[] → Label-based indexing
👉 iloc[] → Integer position-based indexing
👉 Use loc for named indexes, iloc for numeric positions

📊 Q5. What is groupby() in Pandas?

👉 Used for splitting data into groups
👉 Apply aggregation functions (sum, mean, count)
👉 Essential for data summarization

📊 Q6. What is the difference between merge() and concat()?

👉 merge() → SQL-like joins (inner, left, right, outer)
👉 concat() → Stacks data vertically or horizontally
👉 Use merge for relational data combining

📊 Q7. How do you filter data in Pandas?

👉 Use boolean conditions (df[df['col'] > value])
👉 Multiple conditions using & and |
👉 Helps in extracting specific insights

📊 Q8. What is apply() function?

👉 Applies a function across rows or columns
👉 Used for custom transformations
👉 More flexible than built-in functions

🚀 Data engineering roles worth checking for SQL learners



💼 Role: Data Engineer
🏢 Company: EXL
📍 Location: India, IN
🌎 Work mode: remote
✨ Why it is worth checking: Strong exposure to AWS Glue, Redshift, Athena, PySpark, ETL/ELT pipelines, and data warehousing concepts.
🔗 https://artha.link/@adityabansal095/jobs/data-engineer-exl-india-ab6b2cd2

💼 Role: Data Engineer
🏢 Company: Newtap Finance
📍 Location: Bengaluru, Karnataka, IN
🌎 Work mode: onsite
✨ Why it is worth checking: Hands-on work with Airflow, EMR, Spark, Athena, CDC ingestion, and lakehouse architecture.
🔗 https://artha.link/@adityabansal095/jobs/data-engineer-newtap-finance-bengaluru-36d7cd70

💼 Role: Data Engineer – Analytics & Aws
🏢 Company: Health Catalyst
📍 Location: Hyderabad, Telangana, IN
🌎 Work mode: remote
✨ Why it is worth checking: Good mix of data engineering and analytics using AWS Glue, Snowflake, SQL, Python, Kafka, and Airflow.
🔗 https://artha.link/@adityabansal095/jobs/data-engineer-analytics-aws-health-catalyst-hyderabad-14f9084d

Save this for later or share with someone preparing for data engineering interviews.

#DataEngineering #SQL #AWS #Snowflake #Airflow #ETL #Python #Jobs #JobAlert

Most people still get confused between RANK, DENSE_RANK and ROW_NUMBER.

I made a short video explaining the exact difference with a simple example.

Took only 30 seconds.

Watch it here: https://youtube.com/shorts/vptXC6QRjOE

Save it if you have interviews coming up.

𝐒𝐐𝐋 𝐂𝐚𝐬𝐞 𝐒𝐭𝐮𝐝𝐢𝐞𝐬 𝐟𝐨𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰:

1. Danny’s Diner:
Restaurant analytics to understand the customer orders pattern.
Link: https://8weeksqlchallenge.com/case-study-1/

2. Pizza Runner
Pizza shop analytics to optimize the efficiency of the operation
Link: https://8weeksqlchallenge.com/case-study-2/

3. Foodie Fie
Subscription-based food content platform
Link: https://lnkd.in/gzB39qAT

4. Data Bank: That’s money
Analytics based on customer activities with the digital bank
Link: https://lnkd.in/gH8pKPyv

5. Data Mart: Fresh is Best
Analytics on Online supermarket
Link: https://lnkd.in/gC5bkcDf

6. Clique Bait: Attention capturing
Analytics on the seafood industry
Link: https://lnkd.in/ggP4JiYG

7. Balanced Tree: Clothing Company
Analytics on the sales performance of clothing store
Link: https://8weeksqlchallenge.com/case-study-7

8. Fresh segments: Extract maximum value
Analytics on online advertising
Link: https://8weeksqlchallenge.com/case-study-8

🚀 Data engineering roles with strong cloud stack exposure

If you are working on SQL, ETL pipelines, Databricks, Snowflake, or PySpark projects, these roles are worth checking.

💼 Role: Aws Data Engineer
🏢 Company: Tata Consultancy Services
📍 Location: Bengaluru, Noida, Gandhi Nagar, Pune, Chennai
✨ Why it is worth checking: Strong focus on Databricks on AWS, Delta Lake, Structured Streaming, CDC, and Medallion Architecture.
🔗 https://artha.link/@adityabansal095/jobs/aws-data-engineer-tata-consultancy-gandhinagar-92feef28

💼 Role: Azure Data Engineer
🏢 Company: Recro
📍 Location: Hyderabad, Bangalore, Pune
🌎 Work mode: hybrid
✨ Why it is worth checking: Good match if you are building Azure data pipelines with ADF, SQL, Python, PySpark, and Databricks.
🔗 https://artha.link/@adityabansal095/jobs/azure-data-engineer-recro-pune-district-75c72d64

💼 Role: Azure Data Engineer
🏢 Company: EXL
📍 Location: Noida, Gurgaon, Pune, Bangalore, Hyderabad, Chennai
🌎 Work mode: remote
✨ Why it is worth checking: Useful for engineers working with ADF, DBT, Snowflake, and ETL/ELT workflows.
🔗 https://artha.link/@adityabansal095/jobs/azure-data-engineer-exl-noida-6c18eb62

Save this for later or send it to someone job hunting.

#DataEngineering #SQL #Databricks #Snowflake #AzureDataFactory #PySpark #ETL

💥𝐌𝐲 𝐟𝐫𝐢𝐞𝐧𝐝 𝐣𝐮𝐬𝐭 𝐠𝐨𝐭 𝐚 𝟏𝟐 𝐋𝐏𝐀 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐫𝐨𝐥𝐞 𝐚𝐭 𝐏𝐰𝐂.

🎯What stood out in his interviews wasn’t basic SELECT and WHERE.
He was comfortable with JOINs, CTEs, window functions, and actual problem-solving questions.

That’s the gap I see with a lot of people preparing right now.
They know the syntax.

But when the question gets a bit messy, they freeze.

🧠If you’re preparing for data roles, focus less on memorising commands and more on solving real problems.

𝐒𝐭𝐚𝐫𝐭 𝐟𝐫𝐨𝐦 𝐬𝐜𝐫𝐚𝐭𝐜𝐡. 𝐋𝐞𝐚𝐫𝐧. 𝐁𝐮𝐢𝐥𝐝. 𝐑𝐞𝐩𝐞𝐚𝐭.

Don’t mean to offend anyone
But if anyone does not find value in this post or things shared in this channel

Please leave or please ping me on what needs to be improved

Data Science Interview Prep Guide

1️⃣ Core Data Science Concepts
• What is Data Science vs Data Analytics vs ML
• Descriptive, diagnostic, predictive, prescriptive analytics
• Structured vs unstructured data
• Data-driven decision making
• Business problem framing

2️⃣ Statistics Probability (Non-Negotiable)
• Mean, median, variance, standard deviation
• Probability distributions (normal, binomial, Poisson)
• Hypothesis testing p-values
• Confidence intervals
• Correlation vs causation
• Sampling bias

3️⃣ Data Cleaning EDA
• Handling missing values outliers
• Data normalization scaling
• Feature engineering
• Exploratory data analysis (EDA)
• Data leakage detection
• Data quality validation

4️⃣ Python SQL for Data Science
• Python (NumPy, Pandas)
• Data manipulation transformations
• Vectorization performance optimization
• SQL joins, CTEs, window functions
• Writing business-ready queries

5️⃣ Machine Learning Essentials
• Supervised vs unsupervised learning
• Regression vs classification
• Model selection baseline models
• Overfitting, underfitting
• Bias–variance tradeoff
• Hyperparameter tuning

6️⃣ Model Evaluation Metrics
• Accuracy, precision, recall, F1
• ROC AUC
• Confusion matrix
• RMSE, MAE, log loss
• Metrics for imbalanced data
• Linking ML metrics to business KPIs

7️⃣ Real-World Deployment Knowledge
• Feature stores
• Model deployment (batch vs real-time)
• Model monitoring drift
• Experiment tracking
• Data model versioning
• Model explainability (business-friendly)

8️⃣ Must-Have Projects
• Customer churn prediction
• Fraud detection
• Sales or demand forecasting
• Recommendation system
• End-to-end ML pipeline
• Business-focused case study

9️⃣ Common Interview Questions
• Walk me through an end-to-end DS project
• How do you choose evaluation metrics?
• How do you handle imbalanced data?
• How do you explain a model to leadership?
• How do you improve a failing model?

🔟 Pro Tips
✔️ Always connect answers to business impact
✔️ Explain why, not just how
✔️ Be clear about trade-offs
✔️ Discuss failures learnings
✔️ Show structured thinking

📊 Data engineering and BI roles worth exploring

If you are building SQL, ETL, analytics, or AI engineering skills, these opportunities align well with real-world data workflows and production systems.

💼 Role: Lead Ai Engineer (agentic Systems)
🏢 Company: S&P Global
📍 Location: Haryana, Gurugram, IN
✨ Why it is worth checking: Strong mix of Python, ETL, Databricks, Snowflake, Kubernetes, LangGraph, and production-grade AI systems.
🔗 https://artha.link/@adityabansal095/jobs/lead-ai-engineer-agentic-systems-s-p-global-haryana-402f311a

💼 Role: Data Analyst / Engineer
🏢 Company: Kyndryl
📍 Location: Karnataka, Bangalore, IN
✨ Why it is worth checking: Good fit if you work with SQL, Python, Snowflake, ETL/ELT, ODI, Power BI, and enterprise data warehousing.
🔗 https://artha.link/@adityabansal095/jobs/data-analyst-engineer-kyndryl-karnataka-42df3c00

💼 Role: Business Intelligence Engineer, Aop - Row Apex
🏢 Company: ASSPL - Karnataka
📍 Location: Hyderabad, Telangana, IN
✨ Why it is worth checking: Covers BI reporting, SQL, Python, ETL pipelines, Tableau/Quicksight, and analytics frameworks at scale.
🔗 https://artha.link/@adityabansal095/jobs/business-intelligence-engineer-aop-row-asspl-karnataka-hyderabad-28f3112f

Save this for later or share with someone preparing for data engineering interviews.

#DataEngineering #SQL #ETL #BusinessIntelligence #Snowflake #Python #Analytics

Hey Engineers

#jobalert #dailyjobalert

🚨 Hiring: Snowflake + SQL Developer | Immediate Joiners
🚨We are looking for experienced professionals with strong expertise in Snowflake and SQL for an exciting opportunity.
🔹 Role: Snowflake + SQL
🔹 Total Experience: 6–12 Years
🔹 Snowflake Experience: 3+ Years
🔹 Primary Skill: Snowflake
🔹 Secondary Skill: SQL
🔹 Notice Period: Immediate Joiners Only
🔹 Location: Hyderabad – Priority | PAN India
Interview mode : F2F Drive 10 OCT 2026 PAN India

🔑 Key Skills amp; Responsibilities:• Strong hands-on experience in Snowflake and SQL• Good knowledge of ETL/ELT processes and data transformation• Strong understanding of Data Warehousing and Data Modeling• Experience with Dimensions, Facts, and Change Data Capture (CDC)• Hands-on experience with Snowflake Architecture, Data Warehousing amp; Snowpipe• Develop and optimize complex SQL queries for transformation, aggregation, and analysis• Design and implement scalable data pipelines and architectures in Snowflake• Experience working with large historical snapshot tables and feature-based datasets• Knowledge of Python for data analysis and ad hoc query development is an advantage• Understanding of data security, compliance, and performance optimization📩 Interested candidates can contact 9392791250
Mail Id: asmithar@intellicsglobal.com.

Role: Data Engineer - Snowflake
Location: Remote - PAN INDIA
Exp: 8+ Years
Job Type: Full Time
Mandatory skills: Snowflake, Apache Airflow, Python, ETL, SQL
NOTE: Need Only Immediate Joiners

Interested Candidate please share me your Updated Resume to
lidiya.r@vdartinc.com