π― New SQL Interview Question on YouTube!
A real Deloitte Interview Question that tests: β’ Window functions β’ Logical thinking under pressure β’ Your ability to connect dates correctly π βοΈ
DELOITTE HARD SQL Interview Question in Hindi | Find Start and End Dates for Consecutive rows
https://youtu.be/ZPcV0OmSaIo
Most candidates struggle hereβ¦ Can YOU solve it? π€
Watch now π
π₯
#SQL #InterviewPrep #DataEngineering #LearnSQL #SQLServer #Programming #SQLPractice #TechInterview #YouTubeChannel #SQLCommunity #SQLJunction #AdityaBansal
A real Deloitte Interview Question that tests: β’ Window functions β’ Logical thinking under pressure β’ Your ability to connect dates correctly π βοΈ
DELOITTE HARD SQL Interview Question in Hindi | Find Start and End Dates for Consecutive rows
https://youtu.be/ZPcV0OmSaIo
Most candidates struggle hereβ¦ Can YOU solve it? π€
Watch now π
π₯
#SQL #InterviewPrep #DataEngineering #LearnSQL #SQLServer #Programming #SQLPractice #TechInterview #YouTubeChannel #SQLCommunity #SQLJunction #AdityaBansal
YouTube
DELOITTE HARD SQL Interview Question in Hindi | Find Start and End Dates for Consecutive rows
π― SQL Interview Challenge β Can You Solve This?
In this video, we take a real-world problem involving event logs and use SQL window functions (ROW_NUMBER) to group consecutive records by date. This is a popular interview question used in top tech and dataβ¦
In this video, we take a real-world problem involving event logs and use SQL window functions (ROW_NUMBER) to group consecutive records by date. This is a popular interview question used in top tech and dataβ¦
π SQL SET Operators: The Quick Guide π
Stop trying to solve everything with JOINs! If you need to combine data vertically, these are your best friends:
πΉ UNION Combines results and removes duplicates. Perfect for a clean, unique list. π§Ή
πΉ UNION ALL Combines everything, including duplicates. It's much faster because the DB doesn't have to scan for duplicates! β‘οΈ
πΉ INTERSECT Finds the common ground. Only returns rows that exist in both datasets. π€
πΉ EXCEPT / MINUS The "difference" finder. Shows what's in your first query but NOT in the second. π«
π‘ Pro-Tip for Interviews: Always remember: For SET operators to work, your queries must have the same number of columns and matching data types!
https://www.linkedin.com/posts/adityabansal095_i-used-to-think-sql-joins-were-the-only-way-share-7414127739210797057-plAi
#SQL #DataScience #Database #CodingTips #DataAnalytics #InterviewPrep #TechSkills #SQLServer
Stop trying to solve everything with JOINs! If you need to combine data vertically, these are your best friends:
πΉ UNION Combines results and removes duplicates. Perfect for a clean, unique list. π§Ή
πΉ UNION ALL Combines everything, including duplicates. It's much faster because the DB doesn't have to scan for duplicates! β‘οΈ
πΉ INTERSECT Finds the common ground. Only returns rows that exist in both datasets. π€
πΉ EXCEPT / MINUS The "difference" finder. Shows what's in your first query but NOT in the second. π«
π‘ Pro-Tip for Interviews: Always remember: For SET operators to work, your queries must have the same number of columns and matching data types!
https://www.linkedin.com/posts/adityabansal095_i-used-to-think-sql-joins-were-the-only-way-share-7414127739210797057-plAi
#SQL #DataScience #Database #CodingTips #DataAnalytics #InterviewPrep #TechSkills #SQLServer
FAANG SQL Challenge: Can you solve "The Delimiter Trap"? π§©
I just dropped a new breakdown of a classic interview problem that trips up even Senior Data Engineers.
The Task: Extract and calculate reward points from a single "messy" column (e.g., `'MM:CI:CD'`).
Why you should watch:
β Learn the cleanest way to use
β Master Conditional Aggregation (no more spaghetti code!).
β Avoid the #1 over-engineering mistake candidates make in FAANG interviews.
Stop struggling with messy dataβmaster the logic in 5 minutes.
Watch the full solution here:
πππ
https://youtu.be/Pg5F8_ViXE8
Which one is faster for thisβ`STRING_SPLIT` or
#SQL #InterviewPrep #DataEngineering #SQLJunction #SQLInterview
I just dropped a new breakdown of a classic interview problem that trips up even Senior Data Engineers.
The Task: Extract and calculate reward points from a single "messy" column (e.g., `'MM:CI:CD'`).
Why you should watch:
β Learn the cleanest way to use
STRING_SPLIT.β Master Conditional Aggregation (no more spaghetti code!).
β Avoid the #1 over-engineering mistake candidates make in FAANG interviews.
Stop struggling with messy dataβmaster the logic in 5 minutes.
Watch the full solution here:
πππ
https://youtu.be/Pg5F8_ViXE8
Which one is faster for thisβ`STRING_SPLIT` or
CROSS APPLY? Let me know in the YouTube comments! π#SQL #InterviewPrep #DataEngineering #SQLJunction #SQLInterview
YouTube
FAANG SQL Interview Challenge | Calculate Total Club Points
π― SQL Challenge β Can You Solve This?
In this video, weβll solve a real SQL interview-style problem involving reward points and club memberships.
Table Create Statements:
Create Table Club (
Club_Id int,
Member_Id int,
EDU varchar(30))
Insert into Clubβ¦
In this video, weβll solve a real SQL interview-style problem involving reward points and club memberships.
Table Create Statements:
Create Table Club (
Club_Id int,
Member_Id int,
EDU varchar(30))
Insert into Clubβ¦
π1
π¨ BIG SQL DROP INCOMING π¨
Iβm working on a 3-HOUR SQL + Data Analysis marathon where we solve 50+ real SQL questions on one powerful dataset β end to end, no skips, no shortcuts πͺ
If you want to:
π₯ level up SQL thinking
π₯ crack interviews with confidence
π₯ understand real-world data analysis
This video is for YOU.
Dropping soon on YouTube π
#SQLPractice #SQL #DataAnalyst #dataengineer
Iβm working on a 3-HOUR SQL + Data Analysis marathon where we solve 50+ real SQL questions on one powerful dataset β end to end, no skips, no shortcuts πͺ
If you want to:
π₯ level up SQL thinking
π₯ crack interviews with confidence
π₯ understand real-world data analysis
This video is for YOU.
Dropping soon on YouTube π
#SQLPractice #SQL #DataAnalyst #dataengineer
π₯5π1
π Itβs LIVE! The SQL Marathon is finally out π₯
Iβve published a 3-hour SQL + Data Analytics video where I solve 50 real SQL questions using one YouTube Analytics dataset β step by step.
If youβre preparing for:
β’ SQL interviews
β’ Data Engineering / Analytics roles
β’ Real-world SQL problem solving
This video will seriously level you up πͺ
π Watch here: https://youtu.be/BmhKUI-PGrc
Let me know in the comments β how many questions could you solve on your own? π
β SQL Junction π
#DataAnalyst #SQLPractice #SQL #YouTube #SQLInterview #DataAnalytics
Iβve published a 3-hour SQL + Data Analytics video where I solve 50 real SQL questions using one YouTube Analytics dataset β step by step.
If youβre preparing for:
β’ SQL interviews
β’ Data Engineering / Analytics roles
β’ Real-world SQL problem solving
This video will seriously level you up πͺ
π Watch here: https://youtu.be/BmhKUI-PGrc
Let me know in the comments β how many questions could you solve on your own? π
β SQL Junction π
#DataAnalyst #SQLPractice #SQL #YouTube #SQLInterview #DataAnalytics
YouTube
YouTube Data Analytics Project: 50 SQL Interview Questions Solved | 3 Hour SQL Marathon
In this 3-hour SQL marathon, I solve 50+ real SQL interview questions using ONE realistic YouTube Analytics dataset β step by step, from basics to advanced analytics.
This video is perfect if youβre:
1) Preparing for SQL interviews
2) Learning Data Engineeringβ¦
This video is perfect if youβre:
1) Preparing for SQL interviews
2) Learning Data Engineeringβ¦
π₯3
π§ Amazon SQL Interview Challenge
This is an interesting challenge ,please take a look
Ever tried applying Pythagoras theorem in SQL? π
Youβre given triangle sides stored row-wise and your task is to identify which CombinationID forms a right-angled triangle.
π Rule to apply:
HΒ² = PΒ² + BΒ² (longest side matters!)
If this showed up in an interview, how would you approach it?
https://www.linkedin.com/posts/adityabansal095_sql-amazon-amazoninterview-activity-7422150064292282368-fT9O
#SQL #SQLInterview #AmazonInterview #DataEngineering #Analytics #SQLChallenge
This is an interesting challenge ,please take a look
Ever tried applying Pythagoras theorem in SQL? π
Youβre given triangle sides stored row-wise and your task is to identify which CombinationID forms a right-angled triangle.
π Rule to apply:
HΒ² = PΒ² + BΒ² (longest side matters!)
If this showed up in an interview, how would you approach it?
https://www.linkedin.com/posts/adityabansal095_sql-amazon-amazoninterview-activity-7422150064292282368-fT9O
#SQL #SQLInterview #AmazonInterview #DataEngineering #Analytics #SQLChallenge
π Weekend Upskilling Idea: Build a Real Data Portfolio
If youβre stuck on what dataset to use for your next project β this list will save you hours.
Pick one dataset aligned with the industry you want to work in and build something meaningful around it π
π Retail
Online Retail Sales (500k+ transactions)
Great for customer segmentation & sales trends
π https://lnkd.in/d6H26umG
π₯ Healthcare
WHO Global Health Data (194 countries)
Perfect for time series & geo analysis
π https://lnkd.in/dMfCuDqM
π° Finance
World Bank Open Data (GDP, inflation, trade)
Ideal for economic insights & forecasting
π https://data.worldbank.org
π Transportation
NYC Taxi Trips (millions of rides)
Demand forecasting & pricing analysis
π https://lnkd.in/deVUEmmC
π΅ Entertainment
Spotify Top Songs (audio features)
Clustering & recommendation systems
π https://lnkd.in/d6u8BjxD
π₯ HR & Workforce
Employee Attrition Dataset
Churn prediction & retention analysis
π https://lnkd.in/dTgGKfDG
π£ Marketing
Marketing Campaign Data
Campaign ROI & customer profiling
π https://lnkd.in/d_4MFM_j
π Education
Student Performance Dataset
Predictive modeling & insights
π https://lnkd.in/dFv24vAs
π E-Commerce
Amazon Product Reviews
Sentiment analysis & rating prediction
π https://lnkd.in/dZMvTDNj
π Logistics
Supply Chain Dataset
Cost optimization & efficiency analysis
π https://lnkd.in/d_4MFM_j
π‘ Weekend reminder:
You donβt need to finish a full project in one go.
Just start β explore the data, ask questions, write a few queries.
Small effort this weekend = big confidence boost later π
Save this list and pick one dataset to work on.
#dataengineer #SQL #careers #projects #learning #upskill
If youβre stuck on what dataset to use for your next project β this list will save you hours.
Pick one dataset aligned with the industry you want to work in and build something meaningful around it π
π Retail
Online Retail Sales (500k+ transactions)
Great for customer segmentation & sales trends
π https://lnkd.in/d6H26umG
π₯ Healthcare
WHO Global Health Data (194 countries)
Perfect for time series & geo analysis
π https://lnkd.in/dMfCuDqM
π° Finance
World Bank Open Data (GDP, inflation, trade)
Ideal for economic insights & forecasting
π https://data.worldbank.org
π Transportation
NYC Taxi Trips (millions of rides)
Demand forecasting & pricing analysis
π https://lnkd.in/deVUEmmC
π΅ Entertainment
Spotify Top Songs (audio features)
Clustering & recommendation systems
π https://lnkd.in/d6u8BjxD
π₯ HR & Workforce
Employee Attrition Dataset
Churn prediction & retention analysis
π https://lnkd.in/dTgGKfDG
π£ Marketing
Marketing Campaign Data
Campaign ROI & customer profiling
π https://lnkd.in/d_4MFM_j
π Education
Student Performance Dataset
Predictive modeling & insights
π https://lnkd.in/dFv24vAs
π E-Commerce
Amazon Product Reviews
Sentiment analysis & rating prediction
π https://lnkd.in/dZMvTDNj
π Logistics
Supply Chain Dataset
Cost optimization & efficiency analysis
π https://lnkd.in/d_4MFM_j
π‘ Weekend reminder:
You donβt need to finish a full project in one go.
Just start β explore the data, ask questions, write a few queries.
Small effort this weekend = big confidence boost later π
Save this list and pick one dataset to work on.
#dataengineer #SQL #careers #projects #learning #upskill
β€2
π Data Analytics Essentials β Skills Every Analyst Must Master
If you want to grow faster as a Data Analyst, itβs not just about tools.
You need the right mix of technical + business skills.
Hereβs a quick breakdown π
π§ Technical Skills
1οΈβ£ SQL
β’ Joins, GROUP BY, Window Functions
β’ Handling NULLs & duplicates
π‘ Example: Use LEFT JOIN to identify churn vs non-churn users.
2οΈβ£ Excel
β’ Pivot Tables, Lookups, IF logic
β’ Fast data cleaning
π‘ Example: Summarize 50K rows instantly using pivots.
3οΈβ£ Power BI / Tableau
β’ Data modeling, measures, filters
β’ Build dashboards that answer one clear question.
π Example: Track sales drop by region & month.
4οΈβ£ Python
β’ pandas for data cleaning
β’ matplotlib / seaborn for quick visuals
π‘ Example: Cohort-wise revenue analysis.
5οΈβ£ Statistics Basics
β’ Mean vs Median, Variance, Correlation
β οΈ Example: Median often explains salary distribution better than average.
βΈ»
π€ Soft Skills (Equally Important)
1οΈβ£ Business Thinking β Always ask why before how.
2οΈβ£ Communication β Explain insights in simple terms.
3οΈβ£ Problem Framing β Convert vague questions into clear metrics.
4οΈβ£ Attention to Detail β Small data errors break trust.
5οΈβ£ Stakeholder Management β Listen first, challenge with data.
βΈ»
π Lesson:
Tools alone wonβt make you a great analyst.
The real edge comes from combining technical skills with business thinking.
π₯ Pick one skill from this list and improve it.
#DataAnalytics #SQL #Python #PowerBI #AnalyticsSkills
If you want to grow faster as a Data Analyst, itβs not just about tools.
You need the right mix of technical + business skills.
Hereβs a quick breakdown π
π§ Technical Skills
1οΈβ£ SQL
β’ Joins, GROUP BY, Window Functions
β’ Handling NULLs & duplicates
π‘ Example: Use LEFT JOIN to identify churn vs non-churn users.
2οΈβ£ Excel
β’ Pivot Tables, Lookups, IF logic
β’ Fast data cleaning
π‘ Example: Summarize 50K rows instantly using pivots.
3οΈβ£ Power BI / Tableau
β’ Data modeling, measures, filters
β’ Build dashboards that answer one clear question.
π Example: Track sales drop by region & month.
4οΈβ£ Python
β’ pandas for data cleaning
β’ matplotlib / seaborn for quick visuals
π‘ Example: Cohort-wise revenue analysis.
5οΈβ£ Statistics Basics
β’ Mean vs Median, Variance, Correlation
β οΈ Example: Median often explains salary distribution better than average.
βΈ»
π€ Soft Skills (Equally Important)
1οΈβ£ Business Thinking β Always ask why before how.
2οΈβ£ Communication β Explain insights in simple terms.
3οΈβ£ Problem Framing β Convert vague questions into clear metrics.
4οΈβ£ Attention to Detail β Small data errors break trust.
5οΈβ£ Stakeholder Management β Listen first, challenge with data.
βΈ»
π Lesson:
Tools alone wonβt make you a great analyst.
The real edge comes from combining technical skills with business thinking.
π₯ Pick one skill from this list and improve it.
#DataAnalytics #SQL #Python #PowerBI #AnalyticsSkills
π Essential SQL Commands Every Data Professional Should Know
Whether youβre preparing for SQL interviews or working with databases daily, these commands form the backbone of SQL.
Hereβs a quick refresher π
πΉ SELECT β Retrieve data from a table
πΉ INSERT β Add new records into a table
πΉ UPDATE β Modify existing data
πΉ DELETE β Remove specific records
π¦ Database Structure
πΉ CREATE β Create new database objects (tables, databases)
πΉ ALTER β Modify the structure of an existing object
πΉ DROP β Delete an entire table or database
π Filtering & Analysis
πΉ WHERE β Filter records based on conditions
πΉ GROUP BY β Group rows with similar values
πΉ HAVING β Filter aggregated results
π Working with Multiple Tables
πΉ JOIN β Combine data from multiple tables
β‘ Performance
πΉ INDEX β Speeds up data retrieval in large tables
π Tip:
Mastering these fundamentals makes solving complex SQL problems much easier.
π₯ Try this today:
Pick one command and write a small query to practice it.
#SQL #LearnSQL #DataEngineering #DataAnalytics
Whether youβre preparing for SQL interviews or working with databases daily, these commands form the backbone of SQL.
Hereβs a quick refresher π
πΉ SELECT β Retrieve data from a table
πΉ INSERT β Add new records into a table
πΉ UPDATE β Modify existing data
πΉ DELETE β Remove specific records
π¦ Database Structure
πΉ CREATE β Create new database objects (tables, databases)
πΉ ALTER β Modify the structure of an existing object
πΉ DROP β Delete an entire table or database
π Filtering & Analysis
πΉ WHERE β Filter records based on conditions
πΉ GROUP BY β Group rows with similar values
πΉ HAVING β Filter aggregated results
π Working with Multiple Tables
πΉ JOIN β Combine data from multiple tables
β‘ Performance
πΉ INDEX β Speeds up data retrieval in large tables
π Tip:
Mastering these fundamentals makes solving complex SQL problems much easier.
π₯ Try this today:
Pick one command and write a small query to practice it.
#SQL #LearnSQL #DataEngineering #DataAnalytics
β€1π1
π¨ New Video is LIVE! (SQL Interview Alert) π¨
Most candidates fail these SQL questionsβ¦
Not because theyβre hard β
But because they test how you think, not just syntax π
Iβve compiled Top 5 Hard SQL Interview Questions
that are actually asked in real interviews.
π Can YOU solve them all?
π₯ Watch now:
π TOP 5 HARD SQL INTERVIEW QUESTIONS | MOST CANDIDATES FAIL THESE QUESTIONS
https://youtu.be/mptGQeGlMWc
Comment honestly after watching:
How many did you get right? (1β5) π
βΈ»
π₯ If youβre preparing for:
β’ Data Analyst roles
β’ Data Engineering interviews
β’ SQL rounds
You donβt want to miss this.
#sql #dataengineer #datanalysis #sqlinterviewquestions
Most candidates fail these SQL questionsβ¦
Not because theyβre hard β
But because they test how you think, not just syntax π
Iβve compiled Top 5 Hard SQL Interview Questions
that are actually asked in real interviews.
π Can YOU solve them all?
π₯ Watch now:
π TOP 5 HARD SQL INTERVIEW QUESTIONS | MOST CANDIDATES FAIL THESE QUESTIONS
https://youtu.be/mptGQeGlMWc
Comment honestly after watching:
How many did you get right? (1β5) π
βΈ»
π₯ If youβre preparing for:
β’ Data Analyst roles
β’ Data Engineering interviews
β’ SQL rounds
You donβt want to miss this.
#sql #dataengineer #datanalysis #sqlinterviewquestions
YouTube
TOP 5 HARD SQL INTERVIEW QUESTIONS | MOST CANDIDATES FAIL THESE QUESTIONS
In this video, I cover the Top 5 Hard SQL Interview Questions that test your real understanding of SQL, not just syntax.
These are the kinds of questions asked in real interviews at top companies, and they require strong problem-solving and analytical thinking.β¦
These are the kinds of questions asked in real interviews at top companies, and they require strong problem-solving and analytical thinking.β¦
π₯3
Reposting
π¨ New Video is LIVE! (SQL Interview Alert) π¨
Most candidates fail these SQL questionsβ¦
Not because theyβre hard β
But because they test how you think, not just syntax π
Iβve compiled Top 5 Hard SQL Interview Questions
that are actually asked in real interviews.
π Can YOU solve them all?
π₯ Watch now:
π TOP 5 HARD SQL INTERVIEW QUESTIONS | MOST CANDIDATES FAIL THESE QUESTIONS
https://youtu.be/mptGQeGlMWc
Comment honestly after watching:
How many did you get right? (1β5) π
βΈ»
π₯ If youβre preparing for:
β’ Data Analyst roles
β’ Data Engineering interviews
β’ SQL rounds
You donβt want to miss this.
#sql #dataengineer #datanalysis #sqlinterviewquestions
π¨ New Video is LIVE! (SQL Interview Alert) π¨
Most candidates fail these SQL questionsβ¦
Not because theyβre hard β
But because they test how you think, not just syntax π
Iβve compiled Top 5 Hard SQL Interview Questions
that are actually asked in real interviews.
π Can YOU solve them all?
π₯ Watch now:
π TOP 5 HARD SQL INTERVIEW QUESTIONS | MOST CANDIDATES FAIL THESE QUESTIONS
https://youtu.be/mptGQeGlMWc
Comment honestly after watching:
How many did you get right? (1β5) π
βΈ»
π₯ If youβre preparing for:
β’ Data Analyst roles
β’ Data Engineering interviews
β’ SQL rounds
You donβt want to miss this.
#sql #dataengineer #datanalysis #sqlinterviewquestions
YouTube
TOP 5 HARD SQL INTERVIEW QUESTIONS | MOST CANDIDATES FAIL THESE QUESTIONS
In this video, I cover the Top 5 Hard SQL Interview Questions that test your real understanding of SQL, not just syntax.
These are the kinds of questions asked in real interviews at top companies, and they require strong problem-solving and analytical thinking.β¦
These are the kinds of questions asked in real interviews at top companies, and they require strong problem-solving and analytical thinking.β¦
β€1
π¨ New Video is LIVE β A Concept Most SQL Developers Get Wrong! π¨
CTE, Temp Table, hSubqueryβ¦
Most people know them.
But very few know when to actually use what π
In this video, Iβve explained:
β Independent vs Correlated Subquery
β CTE vs Temp Table
β Which one executes first
β Which approach is best for interviews & real projects
If youβre preparing for:
β’ SQL interviews
β’ Data Analyst / Data Engineer roles
β’ Real-world SQL work
π This video will genuinely clear your confusion.
π₯ Watch now:
π Most SQL Developers Get This Wrong π€― | CTE vs Temp Table vs Subquery Explained
https://youtu.be/d5Amr7D-5tY
β οΈ Challenge for you:
After watching, tell me in comments β
Which one do you use the most: CTE / Temp Table / Subquery?
Letβs see who really understands SQL π₯
#sql #dataengineer #datanalysis #sqlinterviewquestions
CTE, Temp Table, hSubqueryβ¦
Most people know them.
But very few know when to actually use what π
In this video, Iβve explained:
β Independent vs Correlated Subquery
β CTE vs Temp Table
β Which one executes first
β Which approach is best for interviews & real projects
If youβre preparing for:
β’ SQL interviews
β’ Data Analyst / Data Engineer roles
β’ Real-world SQL work
π This video will genuinely clear your confusion.
π₯ Watch now:
π Most SQL Developers Get This Wrong π€― | CTE vs Temp Table vs Subquery Explained
https://youtu.be/d5Amr7D-5tY
β οΈ Challenge for you:
After watching, tell me in comments β
Which one do you use the most: CTE / Temp Table / Subquery?
Letβs see who really understands SQL π₯
#sql #dataengineer #datanalysis #sqlinterviewquestions
YouTube
Most SQL Developers Get This Wrong π€― | CTE vs Temp Table vs Subquery Explained
In this video, I explain the difference between CTE, Temp Table, Correlated Subquery, and Independent Subquery using the same average salary example.
Topics covered:
Independent subquery
Correlated subquery
CTE
Temp table
When to use what
SQL interviewβ¦
Topics covered:
Independent subquery
Correlated subquery
CTE
Temp table
When to use what
SQL interviewβ¦
β€2
Top Data Analyst Projects That Impress Recruiters ππΌ
1. Sales Data Analysis
β Analyze monthly/quarterly sales trends
β Segment by product, region, and sales reps
β Tools: Excel, SQL, Power BI/Tableau
2. Customer Retention Dashboard
β Churn analysis and retention KPIs
β Use cohort analysis, funnel visualization
β Tools: Python, Tableau
3. E-commerce Data Exploration
β Study user behavior, conversion rate
β Analyze cart abandonment, top-selling products
β Tools: SQL, Python (Pandas, Matplotlib)
4. HR Data Insights
β Track hiring trends, attrition, diversity metrics
β Build dashboards showing tenure, department stats
β Tools: Excel, Power BI
5. Financial Data Modeling
β Actual vs. forecasted revenue/costs
β Include profitability ratios and variance analysis
β Tools: Excel, Power BI, SQL
6. Web Traffic Analysis
β Analyze Google Analytics or log data
β Focus on user paths, bounce rates, session duration
β Tools: Python, SQL
7. Survey Data Insights
β Clean raw survey data, visualize trends
β Sentiment analysis on feedback (optional NLP)
β Tools: Excel, Python, Tableau
Tips:
β’ Explain the business impact of your insights
β’ Show your workflow: data cleaning β analysis β visualization
β’ Host projects on GitHub or portfolio site
Stay Tunedβ€οΈπ
#Dataanalyst #SQL #InterviewPrep
1. Sales Data Analysis
β Analyze monthly/quarterly sales trends
β Segment by product, region, and sales reps
β Tools: Excel, SQL, Power BI/Tableau
2. Customer Retention Dashboard
β Churn analysis and retention KPIs
β Use cohort analysis, funnel visualization
β Tools: Python, Tableau
3. E-commerce Data Exploration
β Study user behavior, conversion rate
β Analyze cart abandonment, top-selling products
β Tools: SQL, Python (Pandas, Matplotlib)
4. HR Data Insights
β Track hiring trends, attrition, diversity metrics
β Build dashboards showing tenure, department stats
β Tools: Excel, Power BI
5. Financial Data Modeling
β Actual vs. forecasted revenue/costs
β Include profitability ratios and variance analysis
β Tools: Excel, Power BI, SQL
6. Web Traffic Analysis
β Analyze Google Analytics or log data
β Focus on user paths, bounce rates, session duration
β Tools: Python, SQL
7. Survey Data Insights
β Clean raw survey data, visualize trends
β Sentiment analysis on feedback (optional NLP)
β Tools: Excel, Python, Tableau
Tips:
β’ Explain the business impact of your insights
β’ Show your workflow: data cleaning β analysis β visualization
β’ Host projects on GitHub or portfolio site
Stay Tunedβ€οΈπ
#Dataanalyst #SQL #InterviewPrep
β€1
π₯ New YouTube Video Alert
Can You Solve This Deloitte SQL Interview Question?
Youβre given a products table where the category is available only for the first product, while the following rows contain NULL.
π― Challenge:
How would you fill those NULL category values with the correct category?
π‘ Hint: Think about Window Functions π
This is a great SQL problem to test your understanding of NULL handling + data transformation.
π What would you use?
π °οΈ LAG()
π ±οΈ LEAD()
π ²οΈ LAST_VALUE()
π ³οΈ ROW_NUMBER()
π¬ Solution video
Fill Missing Categories in SQL | Hard Deloitte Interview Question
https://youtu.be/IX9NBK3Yx-M
π
Check it out now and let me know in comments ,What did you learn?
#SQL #SQLInterview #Deloitte #SQLChallenge #DataEngineering #Snowflake
Can You Solve This Deloitte SQL Interview Question?
Youβre given a products table where the category is available only for the first product, while the following rows contain NULL.
Beverages β Coca ColaNULL β PepsiNULL β SpriteNULL β FantaSnacks β LaysNULL β DoritosNULL β Kurkureπ― Challenge:
How would you fill those NULL category values with the correct category?
π‘ Hint: Think about Window Functions π
This is a great SQL problem to test your understanding of NULL handling + data transformation.
π What would you use?
π °οΈ LAG()
π ±οΈ LEAD()
π ²οΈ LAST_VALUE()
π ³οΈ ROW_NUMBER()
π¬ Solution video
Fill Missing Categories in SQL | Hard Deloitte Interview Question
https://youtu.be/IX9NBK3Yx-M
π
Check it out now and let me know in comments ,What did you learn?
#SQL #SQLInterview #Deloitte #SQLChallenge #DataEngineering #Snowflake
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Fill Missing Categories in SQL | Hard Deloitte Interview Question
In this SQL interview question, we have a product table where the category is available for the first product but appears as NULL for the following products.
π― The challenge:
Fill the missing category values by carrying forward the last non-NULL categoryβ¦
π― The challenge:
Fill the missing category values by carrying forward the last non-NULL categoryβ¦
π1
π 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.
πΌ Role: Azure Data Engineer
π’ Company: Tata Consultancy Services
π Location: Bengaluru
π Work mode: onsite
β¨ Why it is worth checking: Strong Azure stack with ADF, Databricks, Synapse, PySpark, ETL/ELT, and data warehousing exposure.
π https://artha.link/@adityabansal095/jobs/azure-data-engineer-tata-consultancy-bengaluru-abb0b3eb
πΌ Role: Senior Data Engineer
π’ Company: Satwic
π Location: India
π Work mode: remote
β¨ Why it is worth checking: Good fit for engineers working on Azure migrations, Synapse, Databricks, Spark, and large-scale data pipelines.
π https://artha.link/@adityabansal095/jobs/senior-data-engineer-satwic-inc-india-8610633d
πΌ Role: Data Engineer
π’ Company: TIGI HR
π Location: Gurugram
π Work mode: remote
β¨ Why it is worth checking: Covers Airflow, ADF, SQL, Python, ETL workflows, and data warehouse projects.
π 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
If you are working with SQL, Spark, Airflow, Databricks, or cloud pipelines, these roles align well with practical data engineering workflows.
πΌ Role: Azure Data Engineer
π’ Company: Tata Consultancy Services
π Location: Bengaluru
π Work mode: onsite
β¨ Why it is worth checking: Strong Azure stack with ADF, Databricks, Synapse, PySpark, ETL/ELT, and data warehousing exposure.
π https://artha.link/@adityabansal095/jobs/azure-data-engineer-tata-consultancy-bengaluru-abb0b3eb
πΌ Role: Senior Data Engineer
π’ Company: Satwic
π Location: India
π Work mode: remote
β¨ Why it is worth checking: Good fit for engineers working on Azure migrations, Synapse, Databricks, Spark, and large-scale data pipelines.
π https://artha.link/@adityabansal095/jobs/senior-data-engineer-satwic-inc-india-8610633d
πΌ Role: Data Engineer
π’ Company: TIGI HR
π Location: Gurugram
π Work mode: remote
β¨ Why it is worth checking: Covers Airflow, ADF, SQL, Python, ETL workflows, and data warehouse projects.
π 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
β€4
π Data engineering roles worth checking for SQL learners
πΌ Role: Data Engineer
π’ Company: EXL
π Location: India, IN
π Work mode: remote
β¨ Why it is worth checking: Strong exposure to AWS Glue, Redshift, Athena, PySpark, ETL/ELT pipelines, and data warehousing concepts.
π https://artha.link/@adityabansal095/jobs/data-engineer-exl-india-ab6b2cd2
πΌ Role: Data Engineer
π’ Company: Newtap Finance
π Location: Bengaluru, Karnataka, IN
π Work mode: onsite
β¨ Why it is worth checking: Hands-on work with Airflow, EMR, Spark, Athena, CDC ingestion, and lakehouse architecture.
π https://artha.link/@adityabansal095/jobs/data-engineer-newtap-finance-bengaluru-36d7cd70
πΌ Role: Data Engineer β Analytics & Aws
π’ Company: Health Catalyst
π Location: Hyderabad, Telangana, IN
π Work mode: remote
β¨ Why it is worth checking: Good mix of data engineering and analytics using AWS Glue, Snowflake, SQL, Python, Kafka, and Airflow.
π 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
πΌ Role: Data Engineer
π’ Company: EXL
π Location: India, IN
π Work mode: remote
β¨ Why it is worth checking: Strong exposure to AWS Glue, Redshift, Athena, PySpark, ETL/ELT pipelines, and data warehousing concepts.
π https://artha.link/@adityabansal095/jobs/data-engineer-exl-india-ab6b2cd2
πΌ Role: Data Engineer
π’ Company: Newtap Finance
π Location: Bengaluru, Karnataka, IN
π Work mode: onsite
β¨ Why it is worth checking: Hands-on work with Airflow, EMR, Spark, Athena, CDC ingestion, and lakehouse architecture.
π https://artha.link/@adityabansal095/jobs/data-engineer-newtap-finance-bengaluru-36d7cd70
πΌ Role: Data Engineer β Analytics & Aws
π’ Company: Health Catalyst
π Location: Hyderabad, Telangana, IN
π Work mode: remote
β¨ Why it is worth checking: Good mix of data engineering and analytics using AWS Glue, Snowflake, SQL, Python, Kafka, and Airflow.
π 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
π1
π 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.
πΌ Role: Aws Data Engineer
π’ Company: Tata Consultancy Services
π Location: Bengaluru, Noida, Gandhi Nagar, Pune, Chennai
β¨ Why it is worth checking: Strong focus on Databricks on AWS, Delta Lake, Structured Streaming, CDC, and Medallion Architecture.
π https://artha.link/@adityabansal095/jobs/aws-data-engineer-tata-consultancy-gandhinagar-92feef28
πΌ Role: Azure Data Engineer
π’ Company: Recro
π Location: Hyderabad, Bangalore, Pune
π Work mode: hybrid
β¨ Why it is worth checking: Good match if you are building Azure data pipelines with ADF, SQL, Python, PySpark, and Databricks.
π https://artha.link/@adityabansal095/jobs/azure-data-engineer-recro-pune-district-75c72d64
πΌ Role: Azure Data Engineer
π’ Company: EXL
π Location: Noida, Gurgaon, Pune, Bangalore, Hyderabad, Chennai
π Work mode: remote
β¨ Why it is worth checking: Useful for engineers working with ADF, DBT, Snowflake, and ETL/ELT workflows.
π 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
If you are working on SQL, ETL pipelines, Databricks, Snowflake, or PySpark projects, these roles are worth checking.
πΌ Role: Aws Data Engineer
π’ Company: Tata Consultancy Services
π Location: Bengaluru, Noida, Gandhi Nagar, Pune, Chennai
β¨ Why it is worth checking: Strong focus on Databricks on AWS, Delta Lake, Structured Streaming, CDC, and Medallion Architecture.
π https://artha.link/@adityabansal095/jobs/aws-data-engineer-tata-consultancy-gandhinagar-92feef28
πΌ Role: Azure Data Engineer
π’ Company: Recro
π Location: Hyderabad, Bangalore, Pune
π Work mode: hybrid
β¨ Why it is worth checking: Good match if you are building Azure data pipelines with ADF, SQL, Python, PySpark, and Databricks.
π https://artha.link/@adityabansal095/jobs/azure-data-engineer-recro-pune-district-75c72d64
πΌ Role: Azure Data Engineer
π’ Company: EXL
π Location: Noida, Gurgaon, Pune, Bangalore, Hyderabad, Chennai
π Work mode: remote
β¨ Why it is worth checking: Useful for engineers working with ADF, DBT, Snowflake, and ETL/ELT workflows.
π 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
π 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.
πΌ Role: Lead Ai Engineer (agentic Systems)
π’ Company: S&P Global
π Location: Haryana, Gurugram, IN
β¨ Why it is worth checking: Strong mix of Python, ETL, Databricks, Snowflake, Kubernetes, LangGraph, and production-grade AI systems.
π https://artha.link/@adityabansal095/jobs/lead-ai-engineer-agentic-systems-s-p-global-haryana-402f311a
πΌ Role: Data Analyst / Engineer
π’ Company: Kyndryl
π Location: Karnataka, Bangalore, IN
β¨ Why it is worth checking: Good fit if you work with SQL, Python, Snowflake, ETL/ELT, ODI, Power BI, and enterprise data warehousing.
π https://artha.link/@adityabansal095/jobs/data-analyst-engineer-kyndryl-karnataka-42df3c00
πΌ Role: Business Intelligence Engineer, Aop - Row Apex
π’ Company: ASSPL - Karnataka
π Location: Hyderabad, Telangana, IN
β¨ Why it is worth checking: Covers BI reporting, SQL, Python, ETL pipelines, Tableau/Quicksight, and analytics frameworks at scale.
π 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
If you are building SQL, ETL, analytics, or AI engineering skills, these opportunities align well with real-world data workflows and production systems.
πΌ Role: Lead Ai Engineer (agentic Systems)
π’ Company: S&P Global
π Location: Haryana, Gurugram, IN
β¨ Why it is worth checking: Strong mix of Python, ETL, Databricks, Snowflake, Kubernetes, LangGraph, and production-grade AI systems.
π https://artha.link/@adityabansal095/jobs/lead-ai-engineer-agentic-systems-s-p-global-haryana-402f311a
πΌ Role: Data Analyst / Engineer
π’ Company: Kyndryl
π Location: Karnataka, Bangalore, IN
β¨ Why it is worth checking: Good fit if you work with SQL, Python, Snowflake, ETL/ELT, ODI, Power BI, and enterprise data warehousing.
π https://artha.link/@adityabansal095/jobs/data-analyst-engineer-kyndryl-karnataka-42df3c00
πΌ Role: Business Intelligence Engineer, Aop - Row Apex
π’ Company: ASSPL - Karnataka
π Location: Hyderabad, Telangana, IN
β¨ Why it is worth checking: Covers BI reporting, SQL, Python, ETL pipelines, Tableau/Quicksight, and analytics frameworks at scale.
π 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