Joe Naso
@itsjoenasoI help companies fix their data problems. Writing stuff here: https://t.co/vmm07g0Jx4
Similar User
@laurenbalik
@rahulj51
@viveksp
@Sparkey234
@axabdalla
@KevinMelgarejo
Trimmed a client’s Snowflake spend from ~$180/day to ~$50/day just by consolidating warehouses. >70% cost reduction. This takes only minutes to do and has immediate results. Low hanging fruit abounds.
ORMs are **fine**. But if you rely on your client to generate timestamps like created_at or updated_at, you're going to cause headaches later on. Push the work the DB.
Forced to work on multiple things at once/ context switch and forget which branch has your most recent changes? This is a lifesaver git log —oneline —branches — your_file.py
Does anyone have a good take on the root cause of complexity in data engineering? or even "perceived" complexity?
Gotta love when a new Python notebook demo shows each new cell getting added to the top and the imports get pushed further and further down the screen. Gave me flashbacks to a past job debugging model building Jupyter notebooks written by Stats PhDs
Lambdas are cool but no one ever mentions that you'll spend more time waiting for CloudFormation updates than writing code. What are the best ways to speed up Serverless deployments?
You’re in for a smoother ride if your analysts are contributing to your data model, not just consumers of it
When all you have is a hammer, everything is a nail. What’s the next wave of “hammers” in data tooling? At one point everyone wanted to solve their problems with Spark. Then for a while it swung over to dbt. Is there a post-dbt hammer?
Is it worth it (possible, even) to run Airflow locally with the Astro CLI if you're not deploying Airflow with Astronomer? Or should I just stick with setting up docker/ docker-compose and skip out on Astro?
Optimal complexity in data systems is under acknowledged. Some level of complexity is necessary and not a bad thing. But not all complexity is justified or needed
Vertical SaaS is cool, but when it comes to those in the data ecosystem it always seems they are BI tools with an integration layer. Nothing wrong with that, just not much differentiation other than industry. What are some unique vertical SaaS products in the data space?
United States Trends
- 1. Chargers 54,5 B posts
- 2. Chargers 54,5 B posts
- 3. Quentin Johnston 5.186 posts
- 4. #WWERaw 62,4 B posts
- 5. Lamar 175 B posts
- 6. Drake 331 B posts
- 7. Herbert 19,1 B posts
- 8. Canada 410 B posts
- 9. Derrick Henry 9.072 posts
- 10. Kofi 15,6 B posts
- 11. #BALvsLAC 8.974 posts
- 12. Ladd 4.605 posts
- 13. OG Anunoby N/A
- 14. Justice Hill 2.844 posts
- 15. Bateman 7.833 posts
- 16. Dobbins 5.686 posts
- 17. John Harbaugh 4.021 posts
- 18. New Day 102 B posts
- 19. #Knicks N/A
- 20. Johni Broome 1.505 posts
Something went wrong.
Something went wrong.