SQL Junction
Hi Hope you're doing well. Weโre currently hiring for a Senior Python Developer role with one of our global consulting firm clients in pan India location , for a high-impact InsurTech transformation project. Role highlights: 5+ years of experience inโฆ
Please check this job
Refer your friends if anyone is looking
Refer your friends if anyone is looking
๐ฅ Fractal Data Engineer Interview (23 LPA + 2L Variable)
This wasnโt about textbook SQL.
It was about real-world data engineering.
Hereโs how it went ๐
๐ Screening
โ SQL + Python basics
๐ป Round 1
โ Medium SQL problem
โ PySpark optimization
โ Kafka architecture
โ Custom PySpark function
๐ง Round 2 (Technical Deep Dive)
โ My project architecture
โ Design a scalable ETL from scratch
โ Schema evolution in Delta Lake
โ Azure stack discussion
๐ Round 3 (Managerial)
โ Leadership + ownership
โ Live coding (ETL test cases)
โ Real production challenges
๐ก What they really tested:
Reliability. Scalability. Decision-making. Ownership.
Not memorized syntax.
This wasnโt about textbook SQL.
It was about real-world data engineering.
Hereโs how it went ๐
๐ Screening
โ SQL + Python basics
๐ป Round 1
โ Medium SQL problem
โ PySpark optimization
โ Kafka architecture
โ Custom PySpark function
๐ง Round 2 (Technical Deep Dive)
โ My project architecture
โ Design a scalable ETL from scratch
โ Schema evolution in Delta Lake
โ Azure stack discussion
๐ Round 3 (Managerial)
โ Leadership + ownership
โ Live coding (ETL test cases)
โ Real production challenges
๐ก What they really tested:
Reliability. Scalability. Decision-making. Ownership.
Not memorized syntax.
๐3โค2
๐ฅ Cracked 100 DAX Interview Questions โ Hereโs What Actually Matters
Power BI interviews arenโt basic anymore.
If youโre preparing for Data Analyst or BI roles, these DAX concepts are non-negotiable:
โ SUM vs SUMX โ Aggregation vs row-by-row evaluation
โ CALCULATE โ The king of filter context
โ RANKX, FILTER, ALL, ALLEXCEPT โ Core for dynamic reports
โ Time Intelligence โ TOTALYTD, SAMEPERIODLASTYEAR, DATEADD
โ DIVIDE > / โ Always handle division safely
DAX isnโt about memorizing formulas.
Itโs about understanding context.
If youโre serious about cracking BI interviews, spend time mastering these this week. ๐
Power BI interviews arenโt basic anymore.
If youโre preparing for Data Analyst or BI roles, these DAX concepts are non-negotiable:
โ SUM vs SUMX โ Aggregation vs row-by-row evaluation
โ CALCULATE โ The king of filter context
โ RANKX, FILTER, ALL, ALLEXCEPT โ Core for dynamic reports
โ Time Intelligence โ TOTALYTD, SAMEPERIODLASTYEAR, DATEADD
โ DIVIDE > / โ Always handle division safely
DAX isnโt about memorizing formulas.
Itโs about understanding context.
If youโre serious about cracking BI interviews, spend time mastering these this week. ๐
๐3
โค1๐1
REPOSTING
๐จ New SQL Interview Video is LIVE! ๐จ
Can you solve a real KPMG SQL interview question?
The task sounds simpleโฆ
But it tests your understanding of aggregation and logic ๐
๐ Calculate:
โข Total Rides
โข Won Rides
โข Lost Rides
Think itโs easy? Try solving it before watching.
๐ฅ Watch now:
๐ https://youtu.be/tFaIBqj3jUI
After watching, tell me honestly โ
Did you solve it correctly on your first attempt? ๐
More real-world SQL interview questions coming soon. Stay sharp. ๐ฅ
๐จ New SQL Interview Video is LIVE! ๐จ
Can you solve a real KPMG SQL interview question?
The task sounds simpleโฆ
But it tests your understanding of aggregation and logic ๐
๐ Calculate:
โข Total Rides
โข Won Rides
โข Lost Rides
Think itโs easy? Try solving it before watching.
๐ฅ Watch now:
๐ https://youtu.be/tFaIBqj3jUI
After watching, tell me honestly โ
Did you solve it correctly on your first attempt? ๐
More real-world SQL interview questions coming soon. Stay sharp. ๐ฅ
YouTube
KPMG SQL Interview Question in Hindi | Find Total, Won & Lost Rides
In this video, I solve a real KPMG SQL interview question in Hindi.
Problem Statement:
The Delivery table contains four columns:
Brand_1, Brand_2, Brand_3 and Winner.
We need to write an SQL query to calculate:
โข Total number of rides
โข Number of ridesโฆ
Problem Statement:
The Delivery table contains four columns:
Brand_1, Brand_2, Brand_3 and Winner.
We need to write an SQL query to calculate:
โข Total number of rides
โข Number of ridesโฆ
๐ฅ2โค1
SQL Junction pinned ยซREPOSTING ๐จ New SQL Interview Video is LIVE! ๐จ Can you solve a real KPMG SQL interview question? The task sounds simpleโฆ But it tests your understanding of aggregation and logic ๐ ๐ Calculate: โข Total Rides โข Won Rides โข Lost Rides Think itโs easy? Tryโฆยป
Hey Guys
Is anybody working in Capgemini
One of us from our community need to get a referral in Capg
Please ping me on linkedin ,if you are a Capg employee
Letโs help each other and grow together๐
Is anybody working in Capgemini
One of us from our community need to get a referral in Capg
Please ping me on linkedin ,if you are a Capg employee
Letโs help each other and grow together๐
โค4๐4
Hey Guys
Another freelancing role
Needed skill set - Snowflake,Aws,python,sql,Terraform, pyspark
Expectation -2.5 hours daily in evening
Please ping me urgently if you are interested on linkedin asap
linkedin.com/in/adityabansal095
This is an urgent opportunity
#jobalert #jobopportunity #freelancer
Another freelancing role
Needed skill set - Snowflake,Aws,python,sql,Terraform, pyspark
Expectation -2.5 hours daily in evening
Please ping me urgently if you are interested on linkedin asap
linkedin.com/in/adityabansal095
This is an urgent opportunity
#jobalert #jobopportunity #freelancer
India won ๐
It was a great match
At a moment I thought , England might win it ๐๐ฌ
It was a great match
At a moment I thought , England might win it ๐๐ฌ
โค2๐ฅ2
๐ Data Analytics Essentials โ Skills Every Analyst Must Master
If you want to grow faster as a Data Analyst, itโs not just about tools.
You need the right mix of technical + business skills.
Hereโs a quick breakdown ๐
๐ง Technical Skills
1๏ธโฃ SQL
โข Joins, GROUP BY, Window Functions
โข Handling NULLs & duplicates
๐ก Example: Use LEFT JOIN to identify churn vs non-churn users.
2๏ธโฃ Excel
โข Pivot Tables, Lookups, IF logic
โข Fast data cleaning
๐ก Example: Summarize 50K rows instantly using pivots.
3๏ธโฃ Power BI / Tableau
โข Data modeling, measures, filters
โข Build dashboards that answer one clear question.
๐ Example: Track sales drop by region & month.
4๏ธโฃ Python
โข pandas for data cleaning
โข matplotlib / seaborn for quick visuals
๐ก Example: Cohort-wise revenue analysis.
5๏ธโฃ Statistics Basics
โข Mean vs Median, Variance, Correlation
โ ๏ธ Example: Median often explains salary distribution better than average.
โธป
๐ค Soft Skills (Equally Important)
1๏ธโฃ Business Thinking โ Always ask why before how.
2๏ธโฃ Communication โ Explain insights in simple terms.
3๏ธโฃ Problem Framing โ Convert vague questions into clear metrics.
4๏ธโฃ Attention to Detail โ Small data errors break trust.
5๏ธโฃ Stakeholder Management โ Listen first, challenge with data.
โธป
๐ Lesson:
Tools alone wonโt make you a great analyst.
The real edge comes from combining technical skills with business thinking.
๐ฅ Pick one skill from this list and improve it.
#DataAnalytics #SQL #Python #PowerBI #AnalyticsSkills
If you want to grow faster as a Data Analyst, itโs not just about tools.
You need the right mix of technical + business skills.
Hereโs a quick breakdown ๐
๐ง Technical Skills
1๏ธโฃ SQL
โข Joins, GROUP BY, Window Functions
โข Handling NULLs & duplicates
๐ก Example: Use LEFT JOIN to identify churn vs non-churn users.
2๏ธโฃ Excel
โข Pivot Tables, Lookups, IF logic
โข Fast data cleaning
๐ก Example: Summarize 50K rows instantly using pivots.
3๏ธโฃ Power BI / Tableau
โข Data modeling, measures, filters
โข Build dashboards that answer one clear question.
๐ Example: Track sales drop by region & month.
4๏ธโฃ Python
โข pandas for data cleaning
โข matplotlib / seaborn for quick visuals
๐ก Example: Cohort-wise revenue analysis.
5๏ธโฃ Statistics Basics
โข Mean vs Median, Variance, Correlation
โ ๏ธ Example: Median often explains salary distribution better than average.
โธป
๐ค Soft Skills (Equally Important)
1๏ธโฃ Business Thinking โ Always ask why before how.
2๏ธโฃ Communication โ Explain insights in simple terms.
3๏ธโฃ Problem Framing โ Convert vague questions into clear metrics.
4๏ธโฃ Attention to Detail โ Small data errors break trust.
5๏ธโฃ Stakeholder Management โ Listen first, challenge with data.
โธป
๐ Lesson:
Tools alone wonโt make you a great analyst.
The real edge comes from combining technical skills with business thinking.
๐ฅ Pick one skill from this list and improve it.
#DataAnalytics #SQL #Python #PowerBI #AnalyticsSkills
๐ Thinking of Hosting a Live SQL Interview Masterclass
Iโm considering conducting a live SQL interview masterclass where weโll solve real interview questions and learn the thinking process behind them.
Topics would include:
โข Conditional aggregation questions
โข Ranking & window function problems
โข Real interview questions from companies
โข Common SQL interview traps
It will be a small live session (max 50 people) so we can keep it interactive.
Before planning it, I want to check interest ๐
Would you be interested in joining?
๐ Vote below.
Iโm considering conducting a live SQL interview masterclass where weโll solve real interview questions and learn the thinking process behind them.
Topics would include:
โข Conditional aggregation questions
โข Ranking & window function problems
โข Real interview questions from companies
โข Common SQL interview traps
It will be a small live session (max 50 people) so we can keep it interactive.
Before planning it, I want to check interest ๐
Would you be interested in joining?
๐ Vote below.
Would you be interested in joining?
Final Results
85%
Yes Definitely interested
15%
Maybe ,need more details
0%
Not interested right now
SQL Junction pinned ยซ๐ Thinking of Hosting a Live SQL Interview Masterclass Iโm considering conducting a live SQL interview masterclass where weโll solve real interview questions and learn the thinking process behind them. Topics would include: โข Conditional aggregation questionsโฆยป
๐ Essential SQL Commands Every Data Professional Should Know
Whether youโre preparing for SQL interviews or working with databases daily, these commands form the backbone of SQL.
Hereโs a quick refresher ๐
๐น SELECT โ Retrieve data from a table
๐น INSERT โ Add new records into a table
๐น UPDATE โ Modify existing data
๐น DELETE โ Remove specific records
๐ฆ Database Structure
๐น CREATE โ Create new database objects (tables, databases)
๐น ALTER โ Modify the structure of an existing object
๐น DROP โ Delete an entire table or database
๐ Filtering & Analysis
๐น WHERE โ Filter records based on conditions
๐น GROUP BY โ Group rows with similar values
๐น HAVING โ Filter aggregated results
๐ Working with Multiple Tables
๐น JOIN โ Combine data from multiple tables
โก Performance
๐น INDEX โ Speeds up data retrieval in large tables
๐ Tip:
Mastering these fundamentals makes solving complex SQL problems much easier.
๐ฅ Try this today:
Pick one command and write a small query to practice it.
#SQL #LearnSQL #DataEngineering #DataAnalytics
Whether youโre preparing for SQL interviews or working with databases daily, these commands form the backbone of SQL.
Hereโs a quick refresher ๐
๐น SELECT โ Retrieve data from a table
๐น INSERT โ Add new records into a table
๐น UPDATE โ Modify existing data
๐น DELETE โ Remove specific records
๐ฆ Database Structure
๐น CREATE โ Create new database objects (tables, databases)
๐น ALTER โ Modify the structure of an existing object
๐น DROP โ Delete an entire table or database
๐ Filtering & Analysis
๐น WHERE โ Filter records based on conditions
๐น GROUP BY โ Group rows with similar values
๐น HAVING โ Filter aggregated results
๐ Working with Multiple Tables
๐น JOIN โ Combine data from multiple tables
โก Performance
๐น INDEX โ Speeds up data retrieval in large tables
๐ Tip:
Mastering these fundamentals makes solving complex SQL problems much easier.
๐ฅ Try this today:
Pick one command and write a small query to practice it.
#SQL #LearnSQL #DataEngineering #DataAnalytics
โค1๐1
๐ Common SQL Window Functions You Should Know
Window functions = next-level SQL ๐
They help you analyze data without losing rows.
๐ข ROW_NUMBER()
Gives a unique number to each row
๐ Perfect for deduplication & ordering
๐ RANK()
Assigns rank with gaps
๐ If 2 people tie, next rank is skipped (1, 2, 2, 4)
๐ฅ DENSE_RANK()
Assigns rank without gaps
๐ No skipping (1, 2, 2, 3)
๐ SUM() OVER()
Calculates running totals
๐ Great for cumulative sales, revenue trends
โฎ๏ธโญ๏ธ LAG() / LEAD()
Compare with previous/next row
๐ Useful for time-based analysis, growth tracking
โก Why it matters?
Because real-world data problems need row-level + aggregated insights together
Window functions = next-level SQL ๐
They help you analyze data without losing rows.
๐ข ROW_NUMBER()
Gives a unique number to each row
๐ Perfect for deduplication & ordering
๐ RANK()
Assigns rank with gaps
๐ If 2 people tie, next rank is skipped (1, 2, 2, 4)
๐ฅ DENSE_RANK()
Assigns rank without gaps
๐ No skipping (1, 2, 2, 3)
๐ SUM() OVER()
Calculates running totals
๐ Great for cumulative sales, revenue trends
โฎ๏ธโญ๏ธ LAG() / LEAD()
Compare with previous/next row
๐ Useful for time-based analysis, growth tracking
โก Why it matters?
Because real-world data problems need row-level + aggregated insights together
๐2