Dinner with Kevin

I had dinner with Kevin Kho today at Layang Layang.

I met him from an online course where he was teaching about Prefect, an open-source data workflow orchestration tool.

I reached out to him, and since I was writing technical articles, he asked me to write one on Fugue, an abstraction layer for distributed computing

I didn't get around writing it, but I'm glad we got to meetup as I learned a lot of things from him.

Here's a dump of everything I remember

  • Databricks is successful because Spark is hard to self host, everything else is just icing on the cake
  • Snowflake SQL engine (code -> SQL) makes it limited, whereas Databricks is SQL to code, and you can directly go to the code layer to do more things
  • If you miss the H1B all 3 years, you can get transferred to Canada, and come back to the US under intracompany transfer (L Visa)
  • Working on open-source can help you boost your stocks, get contracting work from companies (e.g. he's contracting with Citibank earning 200/hour)
  • For open-source, the number of stars and community downloads are metrics for investors
  • Fugue's plan to monetize is provide a service that can aggregate together the analytics (BigQuery, SQL) and machine learning (Spark, Dask) operations under one umbrella, and abstract away back-end side of things (hosting, clusters)
  • Han Wang (Lyft) and other Snowflake engineers are working on Fugue, one person just appeared on Slack and started contributing
  • Open source people are happy to provide materials if you want to do a conference or talk or blog about their software, it can help boost your brand.
  • Astronomer is dying because people would rather setup their own airflow or use AWS instead of paying for another service and have to deal with authentication and such.
  • Prefect wanted to use Modal labs to host and deploy workflows, but it was very expensive.
  • Code generation is too non-deterministic, and code is deterministic. There should be another layer between natural language and code generation, could be training set, prompting, a new language.
  • Compute services have a very low operating margin, 70% of it go to AWS or GCP
  • cons of consulting, you might get assigned a project that is boring, and the work never goes to production
  • in the Philippines there's an army class that you take where you march and learn how to hold a gun
  • Facebook data scientists work on mostly SQL
  • Developer advocate works on community-building, workshops, conferences, writing blogs, sometimes code, and salary can be 150k/year fresh grad, but they force you to write certain contents
  • companies start out with analytics, use snowflake, then realize they want to do machine learning, they get recommended low code ML solutions (Dataiku)