Top Tweets for #7DaysBuildingWithSQL
Power BI crashed. The recovery file failed. My 4GB RAM was screaming.
So I did the only logical thing: Started over.
One hour later, the Star Schema is locked, the DAX is cleaner. It was just small win 🏆
#DataTwitter #PowerBI #7DaysBuildingWithSQL #DataAnalyst #NeverGiveUp
I always tell my students to save their work, but tonight, the teacher became the student. 😅
Lost my latest dashboard to a memory freeze and a "file not found" error. It’s a gut punch, but starting over is just a chance to perfect the DAX and sharpen the visual storytelling.
Day 7 OF #7DaysBuildingWithSQL
Is discount strategy driving growth or just eroding profit?
I used SQL to find the "tipping point" for sales health.
✅ CTEs to bucket discount levels.
✅ AOV & ROI logic to measure efficiency.
✅ NULLIF & CAST for clean, error-free math.
#SQL
Day 6 OF #7DaysBuildingWithSQL
Ran my WoW query and found 3-day gaps in the records.
Used a Recursive CTE to bridge the dates and get a true 7-day rolling average. Result? Uncovered a hidden 117.92% revenue spike!
Standard queries give you numbers. Business logic build you.
Day 6 OF #7DaysBuildingWithSQL
Ran my WoW query and found 3-day gaps in the records.
Used a Recursive CTE to bridge the dates and get a true 7-day rolling average. Result? Uncovered a hidden 117.92% revenue spike!
Standard queries give you numbers. Business logic build you.
Day 5 of #7DaysBuildingWithSQL
Today = customer value, no fluff.
Counted unique monthly customers
Calculated ARPC (revenue ÷ customers)
Used NULLIF to avoid crashes
Insight: July 2024 spiked to ~$41K ARPC same customer base, way higher value. That’s peak engagement.
#Datanewbie
Today while working on my #7DaysBuildingWithSQL I stumbled on my first SQl query omo I just laugh and moved on but then something changes between then and now and the change is constant practices,practices they say makes perfection .
Still pushing harder ,still learning
#SQL
Day 5 of #7DaysBuildingWithSQL
Today = customer value, no fluff.
Counted unique monthly customers
Calculated ARPC (revenue ÷ customers)
Used NULLIF to avoid crashes
Insight: July 2024 spiked to ~$41K ARPC same customer base, way higher value. That’s peak engagement.
#Datanewbie
Day 4 of #7DaysBuildingWithSQL
Transformed raw transaction data into a structured monthly profit report enabling clear visibility into business performance
From messy data → decision-ready insights.
Still pushing hard
#SQL #DataAnalytics #OpenToWork #BuildInPublic #DataNewbie
Day 4 of #7DaysBuildingWithSQL
Transformed raw transaction data into a structured monthly profit report enabling clear visibility into business performance
From messy data → decision-ready insights.
Still pushing hard
#SQL #DataAnalytics #OpenToWork #BuildInPublic #DataNewbie
Day 3 of #7DaysBuildingWithSQL: Making messy data look "Executive Grade"
Raw data is rarely ready for a dashboard. Today’s cleanup included:
✅ Normalizing discount percentages with CASE logic.
✅ Trimming and cleaning product codes.
✅ Formatting salesperson names #DataNewbie
Day 3 of #7DaysBuildingWithSQL: Making messy data look cleaner
professional finish.
The logic is getting sharper, the code is getting cleaner. The image shows the dataset before and after cleaning
#SQL #DataAnalysis #BuildInPublic #100DaysOfCode #DataCleanUp #DataNewbie
Day 3 of #7DaysBuildingWithSQL: Making messy data look "Executive Grade"
Raw data is rarely ready for a dashboard. Today’s cleanup included:
✅ Normalizing discount percentages with CASE logic.
✅ Trimming and cleaning product codes.
✅ Formatting salesperson names #DataNewbie
Day 3 of #7DaysBuildingWithSQL: Making messy data look "Executive Grade"
Raw data is rarely ready for a dashboard. Today’s cleanup included:
✅ Normalizing discount percentages with CASE logic.
✅ Trimming and cleaning product codes.
✅ Formatting salesperson names #DataNewbie
Day 2 of #7DaysBuildingWithSQL 🚀
Focused on Exploratory Data Analysis (EDA) 🔍—understanding the structure, spotting missing values, duplicates, and anomalies in the dataset.
This step sets the foundation for clean, reliable insights.
Next: Data cleaning 🧹
#DataAnalytics #SQL
Day 2 of #7DaysBuildingWithSQL 🚀
Focused on Exploratory Data Analysis (EDA) 🔍—understanding the structure, spotting missing values, duplicates, and anomalies in the dataset.
This step sets the foundation for clean, reliable insights.
Next: Data cleaning 🧹
#DataAnalytics #SQL
Day 1 of #7DaysBuildingWithSQL
Got my hands on a messy dataset and successfully imported it into SQL Server. It’s raw, unstructured, and full of surprises 😅
Time to start cleaning and turning this into something meaningful.
The image below shows the steps
#7DaysbuildinwithSQL
Day 1 of #7DaysBuildingWithSQL
Got my hands on a messy dataset and successfully imported it into SQL Server. It’s raw, unstructured, and full of surprises 😅
Time to start cleaning and turning this into something meaningful.
The image below shows the steps
#7DaysbuildinwithSQL
Good morning
Kicking off my #7DaysBuildingWithSQL challenge turning a messy dataset into a fictional business project. I’ll share each stage, lessons, and progress daily.
Follow along!
I’ll be sharing my progress, lessons, and insights along the way using the hashtag.
#SQL
Good morning
Kicking off my #7DaysBuildingWithSQL challenge turning a messy dataset into a fictional business project. I’ll share each stage, lessons, and progress daily.
Follow along!
I’ll be sharing my progress, lessons, and insights along the way using the hashtag.
#SQL
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