Dear readers, I hope you're doing great. I want to welcome all new members joining this week, I hope you'll like the newsletter. You can hit reply if you want to say hi. It's holidays time for me. One week without any computer. This week will be a Data News with some shortcuts.

Enjoy the reading.

Data Fundraising 💰

• Once again a new startup raised money in the observability space. Coralogix got $142m in Series D. The company is more a cloud observability platform. But this is still relevant because they sell themselves as a data obs tool. But as Techcrunch asked it, like me last week, is data observability recession-proof?
• Vertical data platforms are coming. LatchBio raised a $28m Series A to create the "AWS x Github for BioComputing". Their product simplify the biological datasets analyses.

Datafold announced a free tier of their tool for small data team, 800$/month if 100+ tables. Datafold includes a data diff feature to detect issue in the CI/CD and they have lineage capabilities that integrates directly with dbt.

We need to talk about "we need to talk"

There are many trends in blog post naming and right now we are in the we need to talk trend after Pedram post about dbt few weeks ago. This week Erica wants to talk about the data analyst. She defends the fact that data analyst are still important and even more important today with the rise of analytics engineers. She also details how the collaboration between the two roles can be achieved.

The post also argue that job titles are meant to show something but we should not fall in the trap of marketing behind job titles. I personally agree with everything she says. We can also put in parallel Erica post with Mikkel's: how should analysts spend their time?

As a second post Erica also posted on her personal blog her search for a meaning. Something different than data but important to have in mind.

As data practitioners, we look for meaning in numbers, strings, booleans, and charts. We spend time building scalable infrastructure to reliably deduce questions into answers.

Fast News ⚡

• How we structure our dbt projects — dbt refreshed recommendation on how their structure dbt project, this new version replace the old discourse post and it's a must read.
• How to pick the least wrong colors — If you do data visualisation this post will help you understand what's important when choosing your colors to be nice-looking AND accessible.
• A path towards a data platform that aligns data, value, and people — A must-read.
• The real-time data revolution — I'm convinced that this year we have two major trends. The first one is around the lego data platforms, a tool to build your modern data stack bricks after bricks. And the second one is the real-time cloud data product. This article shows what impact real-time data could have in that case.
• 10 principles of proper database benchmarking — DB Benchmarks is a well-know platform to benchmark databases. This week they are releasing 10 principles to look at when benchmarking.
• I deconstructed 13 data industry buzzwords.
• Data engineering projects feel tougher, no? — This article is closed to the one I wrote last year: Data engineering failure — Why is it almost impossible to meet deadlines?
• Salma wrote Things I wish I knew when I was building a data team, a rex from her previous experience on what you should do when starting a data team. From defining the objectives, to pick the good structure and technology.
• “Semantic-free” is the future of Business Intelligence.

Small reminder

We don't need a tech lead

Maybe some of you already heard the previous sentence. Pat Kua wrote what are the 2 scenarii behind. And if it goes the wrong way, remember, great employees don't complain - they walk away.

I'm sorry for the typos this is the first time I write the newsletter not on my own laptop #keyboardissues.

PS: I did not finish in time the Airflow Summit takeaway post. I'll do it once I'm back.