I spent months compiling everything I wish I had when I started in data engineering.
Introducing dehub engineer β a free, open-source knowledge hub covering:
π Pipeline architecture & orchestration
ποΈ SQL patterns & query optimization
β‘ Streaming vs batch processing
ποΈ Data warehouse & lakehouse design
No paywalls. No newsletters. No fluff. Just the stuff that matters on the job.
Star it β https://t.co/Btf1sMuVjl
If this helps even one person land a DE job or pass a system design interview, it was worth building. π
π’ Update: Our official Contract Address is now displayed on the website. Visit https://t.co/6aTNPLCRTH to verify and copy it directly. Always use the address from the official site only.
π The Data Engineering Handbook is live on @EasyA Kickstart
Everything you need to become an extraordinary data engineer β curated, opinionated, no fluff.
Fair launch. No presale. Bonding curve open now.
π $DEHUB
Ca : BrSTpWXM9qtcUA4FX65s54XMpqhSWWTzZjQV9dnEASY
β https://t.co/uI6UWiTurv
π The Data Engineering Handbook is live on @EasyA Kickstart
Everything you need to become an extraordinary data engineer β curated, opinionated, no fluff.
Fair launch. No presale. Bonding curve open now.
π $DEHUB
Ca : BrSTpWXM9qtcUA4FX65s54XMpqhSWWTzZjQV9dnEASY
β https://t.co/uI6UWiTurv
I built a data engineering quiz that most senior engineers still struggle with.
Topics covered:
β How does exactly-once delivery actually work in Kafka?
β When do you partition vs cluster in BigQuery?
β What's the real difference between a data lake and a lakehouse?
β When does a star schema beat a snowflake schema?
If you can answer all of these confidently, you're probably already a Staff DE.
If not β I've got you covered.
Free quiz + full reference hub at https://t.co/jdCzB6kFOD
Drop your score below π (no shame β this is how we all level up)
1/ Announcing the winners of the Solana Frontier
Hackathon!ποΈ
Read about the winners & honorable mentions: https://t.co/9HXbhNtkU2
The subset of winning teams accepted into our VC fund's next accelerator cohort will be shared in the coming days.
Congrats to all! π
The data engineering space moves FAST.
Spark, dbt, Kafka, Flink, Iceberg, Delta Lake, duckdb, Airflow, Dagster β new tools drop every quarter and the learning curve never ends.
So I built https://t.co/afqWSBV3Sf β a focused, up-to-date reference that cuts through the noise and covers what you actually need to know:
β Core pipeline concepts
β SQL & analytical query patterns
β Streaming fundamentals
β Warehouse & lakehouse architecture
β Interactive quiz to test your knowledge
100% free. 100% open source.
Bookmark it now, thank yourself later β https://t.co/jAMzcz7Ftf
RT to help a fellow data engineer π
Anthropic will say things like, βLoops write 30% of our code,β and then burn an unfathomable number of tokens just to ship remarkably mid software.
The models are amazing. Claude Desktop and Claude Mobile? Genuinely bad. And apparently increasingly loop-generated.
It all feels backwards. It feels like a strategy to increase usage because loops can run 24/7. Anthropic benefits from the increased token usage, while the rest of us get more slop.
And I donβt just mean code slop. I mean product slop, design slop, prose slop. Another calorie-tracking app. Another landing page with *that* aesthetic. Another article that uses the term βload-bearing.β The progressive convergence of the web toward the mean.
No one wants this. We want less software thatβs more thoughtfully crafted. And I donβt think weβre yet at the point where looping agents are going to give us that.
Or maybe Iβm just a Luddite.
Why are we even calling them "agents" instead of "scripts that use LLMs"?
Because most "agents" I've seen are just:
1. Prompt
2. Call API
3. Parse response
4. Loop
That's... a script.
Hey algorithm...
Connect me with people who love:
π€ AI
π Data Analysis
π Data Science
π» Excel, SQL, Power BI & Python
π Problem-solving
π§ Critical Thinking
π Continuous Learning
π Building Projects
π Sharing Knowledge
π Tech
If that's you, let's connect π
Just shipped something I've been building for the data engineering community π§
Data Engineer Hub β your go-to reference for pipelines, SQL, streaming, and warehousing concepts. All in one place, no fluff.
And yes, there's an interactive quiz to test how sharp your DE skills actually are π§
Open source β https://t.co/Btf1sMuVjl
Drop a β if you find it useful. Building this in public. Feedback welcome.
Most useless degrees to get in 2026
Finance & Accounting
Data science
Law
COMPUTER SCIENCE
Physics
Cybersecurity
Business administration
General communications
Two system design engineers designed messaging.
Design Aππ»
Apache Kafka
Design B ππ»
RabbitMQ
Which one will you ship to production and why?
π¨ Anthropic's CEO: "software engineering will be fully automated in 12 months."
two types of people right now:
type 1: opens Claude, types something, gets an answer, closes the tab. thinks they're using AI.
type 2: knows the hidden features, settings, and shortcuts. runs Claude like a power tool.
type 1 gets surprised in 12 months.
type 2 built the advantage already.
bookmark this. read it today.
Hot take: most data engineers learn 80% of their stack from scattered docs, random blog posts, and Stack Overflow at 2am.
So I built Data Engineer Hub β a focused knowledge base covering the stuff that actually matters on the job: pipelines, SQL patterns, streaming architecture, and warehouse design.
Free, open source, no login needed.
π https://t.co/Btf1sMuVjl
RT if you know someone breaking into data engineering π