Every data engineer wants to build pipelines that handle millions of events.
But very few know that companies like Uber, Netflix, LinkedIn, Discord, Spotify... share exactly how they built their data infrastructure — for FREE.
Research papers. Engineering blogs. Architecture breakdowns. Everything.
If you want to actually understand data engineering at scale, bookmark these 13 resources and spend 10 minutes a day reading them.
- Uber's Big Data Platform → https://t.co/Ae7y9xJp5X
- Uber's Real-time Infra (Research Paper) → https://t.co/Ds8ohdDaEU
- LinkedIn's Data Infrastructure (IEEE Paper) → https://t.co/gsQyos4bHB
- LinkedIn: 4 Trillion Events Daily → https://t.co/JhagoCzGfH
- LinkedIn: Northguard & Xinfra → https://t.co/b4nu3aPCu9
- Netflix: 4 Phases of Real-time Infra → https://t.co/LUIiC8KkqC
- Netflix Data Engineering (Video) → https://t.co/ByupxeKDOF
- Twitter: Billions of Events in Real-time → https://t.co/7BerQIlJFE
- Notion: Building & Scaling Their Data Lake → https://t.co/y8RGlWhpf1
- Airbnb: Metric Consistency at Scale → https://t.co/ZhNZImgY0F
- Spotify: Event Delivery to the Cloud → https://t.co/fTyC0IgnEn
- Walmart: Data Lakehouse with Hudi → https://t.co/qDYS8ttBsZ
- Discord: Insights from Trillions of Data Points → https://t.co/GMeAAgdnQw
I’ve spent years bookmarking the best places to find data so you don’t have to.
Kaggle is just the tip of the iceberg.
These 10+ repositories are goldmines for any niche.
Steal my list here:
I buy debt for 2 cents on the dollar and forgive it for $500
Not charity.
Not kindness.
Pure profit.
Buy $10K debt for $200.
Charge debtor $500 to forgive it.
They save $9,500.
I make $300 profit.
Here's the $40K/month empathy arbitrage:
The Debt Market Reality:
Old debt sells in bulk portfolios.
$1M in face value = $20K purchase price.
Collectors harass people for years.
Maybe collect 5%.
I collect 50%.
By being the good guy.
The Purchase Process:
Find debt portfolios on https://t.co/GPM8aHzKXl
Medical debt is cheapest (1-2 cents per dollar)
Credit cards (3-5 cents)
Personal loans (5-8 cents)
Buy $500K portfolio for $10K.
1,000 people each owing ~$500.
The Forgiveness Offer:
Email/text each debtor:
"I bought your $500 debt.
Pay $50 and it's gone forever.
Or pay nothing and I'll keep calling."
90% pay the $50.
They're saving $450.
Think I'm a hero.
The Math:
$500K debt portfolio
Cost: $10K
Collect: $50 x 900 people = $45K
Profit: $35K
Time invested: 20 hours
Better ROI than anything legal.
The Psychological Genius:
Debt collectors threaten and scream.
Get paid nothing.
I offer salvation.
Get paid immediately.
Same debt.
Different approach.
Different outcome.
Recent portfolio:
Bought $2M in medical debt.
Cost: $40K
Offered everyone 90% forgiveness.
Collected: $180K
Profit: $140K
Took 6 weeks.
The Reputation Management:
People post about me online:
"This company saved my life!"
"Only had to pay $100 on $3K debt!"
"They're angels!"
Five-star reviews everywhere.
While making 300% returns.
The Subscription Model:
Monthly forgiveness plan: $50/month
Forgives $500 in debt monthly.
People pay forever.
Thinking they're winning.
2,000 subscribers.
$100K/month recurring.
The Exit Strategy:
Building reputation as "ethical debt buyer"
Will sell company for 10x revenue.
Current run rate: $500K/year.
Exit value: $5M.
For buying garbage debt.
And being slightly less evil.
The Market Size:
$140 billion in unpaid debt.
Selling for $2.8 billion.
Even taking 10% is $280M opportunity.
Competition is zero.
Nobody else thought of being nice.
While still profiting.
This is capitalism perfected:
Everyone wins.
Everyone pays.
I get rich.
The system created desperate people.
I created hope.
For a price.
That's just good business
I just built an open NotebookLM clone!
Here's what it can do for you:
- Process multi-modal data
- Scrape websites and YouTube videos
- Create a unified knowledge base
- Lets you do RAG over it
- Remember every conversation
- Generate a podcast 🎙️
The idea here is not to reinvent the wheel but to understand how one of the most powerful tools for learning and research actually works, by building it step-by-step!
So by the end of this video, you'll learn how to:
↳ Process multimodal data (text, audio, video, URLs, and YouTube videos) into a format ready for LLMs
↳ Store everything in a vector database for fast retrieval
↳ Add a memory layer that remembers conversations and preferences for a personalized experience
↳ Chat with your knowledge base or generate podcasts using a fully open-source, locally running text-to-speech model
The podcast generation feature is my favorite part!
There's something powerful about turning written content into conversational audio that you can listen to while doing something else.
The entire code is 100% open-source. I've shared a link in the replies!
____
Don't forget to drop a like if you enjoy my videos. It shows me I should be making more content like this.
Cheers! :)
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All you need is a basic knowledge of Python (you’ve got a month: 2 hours of daily study is plenty and there are so many free resources to learn Python) and a GitHub account.
Register to get access to all the links and resources. The series is available in both English and Spanish. 🙂
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Data engineering in 2025 looks nothing like it did 5 years ago. The shift from imperative to declarative is reshaping everything. From how we build, deploy, and think about data platforms.
Below are 9 concepts driving this transformation.
AI startups are creating new billionaires at a record pace. I looked at the lineup and I don’t see my people 😔. We need more black people starting AI companies.
The full Identity and Access Administrator (SC-300) course is available from @MicrosoftLearn on YouTube!
Enjoy and don’t forget to share! 🙂
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