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
You need to combine two lists of customer IDs from two different tables. You want the fastest performance possible and you don't mind if some IDs appear twice. Which operator do you use?
Final Results
24%
UNION
2%
EXCEPT
74%
UNION ALL
0%
EXCEPT
The correct answer is Union all
This is a really common interview question just to check basics
Union all is faster that union because it does not sort or deduplicate data.π
This is a really common interview question just to check basics
Union all is faster that union because it does not sort or deduplicate data.π
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
SQL Junction pinned Β«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 / Analyticsβ¦Β»
How do you plan on spending this weekend?
Final Results
0%
π Party both days
29%
Party on Saturday and Upskilling on Sundayπ
14%
Watching Netflix
57%
Upskilling on both days
Career growth rarely comes from one big move.
It comes from showing up every day β even when no one is watching.
β’ Learn when itβs boring
β’ Practice when progress feels slow
β’ Stay consistent when results arenβt visible
Perseverance compounds quietly.
If you can stay disciplined for long enough,
your career will look very different by the end of 2026.
No hacks.
No shortcuts.
Just consistency.π₯
It comes from showing up every day β even when no one is watching.
β’ Learn when itβs boring
β’ Practice when progress feels slow
β’ Stay consistent when results arenβt visible
Perseverance compounds quietly.
If you can stay disciplined for long enough,
your career will look very different by the end of 2026.
No hacks.
No shortcuts.
Just consistency.π₯
β€2π2
π¨ My FIRST YouTube Short is LIVE! π¨
This one is about a simple SQL habit that can save you from serious data loss in production π
π Before you ever run a DELETE, make sure youβre doing this one thing.
π₯ Watch here:
π One DELETE mistake can destroy your data π¬ | SQL
https://youtube.com/shorts/6AK2S2Qv5kU
If youβve ever worked with real databases, this is a must-watch.
Drop a π or comment on YouTube if it helped you β it really supports the channel.
More real-world SQL Shorts coming soon π₯
This one is about a simple SQL habit that can save you from serious data loss in production π
π Before you ever run a DELETE, make sure youβre doing this one thing.
π₯ Watch here:
π One DELETE mistake can destroy your data π¬ | SQL
https://youtube.com/shorts/6AK2S2Qv5kU
If youβve ever worked with real databases, this is a must-watch.
Drop a π or comment on YouTube if it helped you β it really supports the channel.
More real-world SQL Shorts coming soon π₯
β€4
Happy long weekend to all π₯π₯
Use this weekend to relax and upskill yourself
Use this weekend to relax and upskill yourself
Hey Data Engineers
One individual from this community just had an Snowflake DBT interview with TCS,these are the main questions asked into him
Snowflake + dbt interview questions asked at TCS π
These are not beginner-level questions β they test real production understanding.
β’ Difference between Snowpipe vs COPY INTO
β’ Can you clone a subset of a table in Snowflake?
β’ Track column value changes (Apple β Orange) using Streams + MERGE
β’ Types of views in Snowflake and their use cases
β’ What is CLUSTER BY and how many columns should be used?
β’ Is a Stream a database object in Snowflake?
β’How do you register a source in dbt?
β’ Difference between external table and managed tables in snowflake?
https://www.linkedin.com/posts/adityabansal095_a-friend-of-mine-recently-interviewed-at-activity-7420836271784321024-WuGJ
If you can confidently answer these,
youβre already ahead of most candidates.
More Snowflake & dbt interview content coming soon π
#Snowflake #DBT #DataEngineering #SQLInterviews #TCS #Analytics #CareerGrowth
One individual from this community just had an Snowflake DBT interview with TCS,these are the main questions asked into him
Snowflake + dbt interview questions asked at TCS π
These are not beginner-level questions β they test real production understanding.
β’ Difference between Snowpipe vs COPY INTO
β’ Can you clone a subset of a table in Snowflake?
β’ Track column value changes (Apple β Orange) using Streams + MERGE
β’ Types of views in Snowflake and their use cases
β’ What is CLUSTER BY and how many columns should be used?
β’ Is a Stream a database object in Snowflake?
β’How do you register a source in dbt?
β’ Difference between external table and managed tables in snowflake?
https://www.linkedin.com/posts/adityabansal095_a-friend-of-mine-recently-interviewed-at-activity-7420836271784321024-WuGJ
If you can confidently answer these,
youβre already ahead of most candidates.
More Snowflake & dbt interview content coming soon π
#Snowflake #DBT #DataEngineering #SQLInterviews #TCS #Analytics #CareerGrowth
β€1
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 Engineering Interview in 30 Days? Hereβs the Game Plan.
If I had an interview next month, this is exactly how Iβd prepare π
π§ Strengthen the Basics
β’ Revise core SQL concepts
β’ Solve 20β30 recent SQL questions
β’ Practice 20β30 Python coding problems
β‘ Spark & Big Data
β’ Understand PySpark fundamentals
β’ RDDs vs DataFrames vs Datasets
β’ Practice top PySpark programs
π Data Engineering Foundations
β’ Revise Data Warehousing concepts
β’ Understand your projectβs data model
β’ Review cloud services used
π» Practical Knowledge
β’ Essential Linux commands
β’ Be ready to explain your project clearly
β’ Know your roles & responsibilities
β’ Understand implementation details
π Interview Readiness
β’ Common DE interview questions
β’ Read interview experiences of the target company
π‘ 30 days is more than enough β if you prepare with focus.
Start today. Small daily effort > last week panic π
#dataengineer #jobopportunity #learning #interview #SQLInterview
If I had an interview next month, this is exactly how Iβd prepare π
π§ Strengthen the Basics
β’ Revise core SQL concepts
β’ Solve 20β30 recent SQL questions
β’ Practice 20β30 Python coding problems
β‘ Spark & Big Data
β’ Understand PySpark fundamentals
β’ RDDs vs DataFrames vs Datasets
β’ Practice top PySpark programs
π Data Engineering Foundations
β’ Revise Data Warehousing concepts
β’ Understand your projectβs data model
β’ Review cloud services used
π» Practical Knowledge
β’ Essential Linux commands
β’ Be ready to explain your project clearly
β’ Know your roles & responsibilities
β’ Understand implementation details
π Interview Readiness
β’ Common DE interview questions
β’ Read interview experiences of the target company
π‘ 30 days is more than enough β if you prepare with focus.
Start today. Small daily effort > last week panic π
#dataengineer #jobopportunity #learning #interview #SQLInterview
β€5
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When I give KT to my junior ππ on running the TRUNCATE statement
π3
Hey Engineers
Another freelance opportunity
I have a friend who needs some one who is good at SQL,SSIS,SSRS ,PostGreSQL
and Airflow
The client wants to migrate existing SSIS ETL packages
Please ping me on linkedin if you are interested.
linkedin.com/in/adityabansal095
#hiring #freelancer #jobalert #jobopportunity #ETL #SQLCommunity
Another freelance opportunity
I have a friend who needs some one who is good at SQL,SSIS,SSRS ,PostGreSQL
and Airflow
The client wants to migrate existing SSIS ETL packages
Please ping me on linkedin if you are interested.
linkedin.com/in/adityabansal095
#hiring #freelancer #jobalert #jobopportunity #ETL #SQLCommunity
π1
Hi
Hope you're doing well.
Weβre 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 in Python backend development
Mandatory experience in insurance products (Life / Health / General Insurance)
Strong exposure to insurance systems, APIs, and backend platforms
Opportunity to work on a large-scale InsurTech program
Unique exposure to learn and work on new-age insurance platforms that are rapidly booming in the industry
This role offers both deep insurance domain exposure and future-facing platform learning, making it a strong long-term career opportunity.
If this aligns with your profile or career direction, Iβd be happy to share more details.
Also, referrals are most welcome and truly appreciated.
Warm regards,
Soham Ganguly
EliteRecruitments
www.eliterecruitments.com
https://www.linkedin.com/company/eliterecruitments
#hiring #jobalert #jobopportunity #python
Hope you're doing well.
Weβre 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 in Python backend development
Mandatory experience in insurance products (Life / Health / General Insurance)
Strong exposure to insurance systems, APIs, and backend platforms
Opportunity to work on a large-scale InsurTech program
Unique exposure to learn and work on new-age insurance platforms that are rapidly booming in the industry
This role offers both deep insurance domain exposure and future-facing platform learning, making it a strong long-term career opportunity.
If this aligns with your profile or career direction, Iβd be happy to share more details.
Also, referrals are most welcome and truly appreciated.
Warm regards,
Soham Ganguly
EliteRecruitments
www.eliterecruitments.com
https://www.linkedin.com/company/eliterecruitments
#hiring #jobalert #jobopportunity #python
π1
SQL Junction
Hi Hope you're doing well. Weβre 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 inβ¦
Please check this job
Refer your friends if anyone is looking
Refer your friends if anyone is looking