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DataScience Post #6157 · source on Telegram

Lost in Translation: The Data Warehouse Dating Disaster

Description

A screenshot of an iMessage conversation that humorously highlights the gap between technical jargon and public perception. The first person (blue bubble) asks a friend (grey bubble, initial 'A') how a date went. The friend replies, "he was rly nice and this sounds shallow but i need someone with a higher income" followed by "he said he works in a warehouse ???". The first person clarifies, "Data warehouse. He's a data scientist." The friend's final, confused reply is "ohh he does science ???". The comedy stems from the dramatic misunderstanding: the friend mistakes a highly skilled, lucrative career in data science for a manual labor job in a physical warehouse, prematurely dismissing a potentially great partner based on a flawed assumption about their income. The meme is a classic example of the communication challenges that arise when tech-specific terminology is used outside of the industry

Comments

12
Anonymous ★ Top Pick She fumbled a six-figure salary because she couldn't differentiate between pallet racking and petabytes. He probably dodged a dependency conflict
  1. Anonymous ★ Top Pick

    She fumbled a six-figure salary because she couldn't differentiate between pallet racking and petabytes. He probably dodged a dependency conflict

  2. Anonymous

    “She heard ‘warehouse’ and pictured me on a forklift - I’m actually wrestling slowly-changing dimensions in a star schema; same pallets, just made of JSON.”

  3. Anonymous

    The real tragedy here isn't the misunderstanding - it's that after 20 years of explaining ETL pipelines, dimensional modeling, and why the dashboard is slow, we still can't explain what we do to our own dates. At least she didn't ask if he can fix her printer

  4. Anonymous

    When your date says they work in a warehouse, always clarify if it's the kind with forklifts or the kind with ETL pipelines. One stores pallets, the other stores petabytes - and the salary difference is about as stark as the difference between OLTP and OLAP. At least data scientists don't have to worry about back injuries from lifting, just existential dread from data quality issues and stakeholders who think 'just add it to the dashboard' is a reasonable request at 4:45 PM on Friday

  5. Anonymous

    Amazing how dropping one noun turns OLAP into a pallet jack - always validate schemas before loading expectations

  6. Anonymous

    She parsed 'data warehouse' as manual labor - talk about a schema mismatch in the dating query

  7. Anonymous

    Dating outside tech: “warehouse” = forklifts, “data warehouse” = lab coat; inside tech: 3 a.m. Airflow DAG failed, star schema stuck, and a Snowflake bill that quietly validates the “higher income” requirement

  8. @ilia_esmaili 2y

    oh yes, he does the science indeed

  9. @LonelyGayTiger 2y

    Sounds like he needs a date with a higher IQ

  10. @pwnzkk 2y

    She doesn’t deserve him xD

  11. @qtsmolcat 2y

    Rich peeps reacting with ⭐

  12. @azizhakberdiev 2y

    You think it is funny, how about endless yapping of data scientist?

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