10 Lessons Learned In 10 Years Of Data [2/2]
This is part 2; check part one if you don’t want to get spoiled!
Let’s tackle five lessons learned from the past three years. We have seen an explosion of frameworks, tooling, and SaaS startups. Probably because bootstrapping SaaS products has never been easier.
2020 ⏱️ Remember bob, the data engineer?
I was a Data Scientist, but the Data Engineering hype was stronger.
Bob now has plenty of options in open-source. Plus, they aren’t ridiculously expensive to put in production. After all, many companies put Kafka into production before it was even 1.0!
✔️Lesson #6: Open-source is the new norm
Having a product (or some part of it) open-sourced enables tech people to try new technology at minimum risk without any commitment.
Tech folks don’t like to talk to salespeople. I prefer to try the product first and then return with any questions. And I’m happy to go one step further regarding sales if things get interesting.
On a side note, open-source is not needed in this case. Having an online demo without a credit card could solve this. Yes, but it’s still a black box. How mature is the project? How big is the number of contributors? What’s the community traction?
All these things can be evaluated easier when a project is open-source.
But here’s the trap: maintenance is not free.
While an open-source tool can be easy to try, there is sometimes a huge gap between a local playground and something put in production. We sometimes get fooled compared to expensive proprietary vendors, but we should never forget that a huge part of the cost, in the end, is our salary.
