November Data News
I’ve been publishing Castor Data news for a while now, but this is the first Medium edition!
In this load of data news, you’ll find:
- One data tool that we believe is worth digging into as a data person. We will also provide where it fits in the data ecosystem.
- A selection of the most delightful articles we read this month along with a quick teaser.
- The data person you should follow right now if you don’t already because he/she writes interesting content.
- Three curated jobs we would run to (if we were not having so much fun building Castor)
Data Tool
MotherDuck raised a $35 million Series A round led by Silicon Valley venture firm Andreessen Horowitz, following their $12 million seed round led by Redpoint Ventures. MotherDuck offers a serverless data analytics platform based on the open-source database DuckDB.
Jordan Tigani, MotherDuck’s founder, noticed that most database workloads were small (less than 10GB) and low bandwidth. The tools in the ecosystem were built for processing “big data”, but were way less suited to handle these smaller workloads, usually sacrificing performance for the sake of processing large amounts of data. MotherDuck is on a mission to improve the user experience when it comes to these small data workloads, and to make them ultra-performant.
This is where we have placed MotherDuck on our data storage landscape. You can find a full landscape of the data storage ecosystem here.
Data News
- Our top dbt packages pick. In this article, Charles Verleyen explains how to best leverage the dbt community. dbt packages allow for the re-use and sharing of dbt projects, bringing the best of software engineering practices to data teams. In this article, Charles outlines his favorite packages when it comes to tests, audits, generators, and observability.
- Different Types Of “Data Engineering” Teams. Data Engineering teams can take different forms. In this article, Ben Rogojan, aka “The Seattle Data Guy” explores the different patterns of Data Engineering teams according to company size. Regardless of the structure of your Data Engineering team, make sure it covers the following areas: Data Quality, Output, Security, and Usability.
- A guide to the Data Experience in a fragmented ecosystem. A good Data Experience is based on three pillars: Discovery, Community, and Health. Bringing about these three pillars in a heavily fragmented data ecosystem is HARD. The key is to replace our “tool-first” mindset with a “capability-first” mindset when thinking about the Data Experience. Each pillar is associated with a set of capabilities, which you should seek to cover with the right tools.
Data Person
Ethan is the co-founder & CEO of Portable, a Reverse ETL tool focusing on long-tail connectors. Ethan has recently taken part in all the interesting data debates. If you like controversy and unconventional thinking, make sure to give him a follow on Linkedin.
Data Jobs
Bonus — you’ll be using Castor if you join these companies
- Cypress.io is looking for a US-based product manager. The company is on a mission to make software testing delightful and effective. Apply here. 🇺🇸
- Brightside Health is looking for a Senior Data Engineer. Brightside Health delivers life-saving virtual mental healthcare to everyone who needs it. Apply here. 🇺🇸
- Fairmoney is the #1 digital lender in Nigeria and other emerging markets. The company is looking for a Paris-based Lead Data Scientist. Apply here. 🇫🇷
Data Meme
Last week, we had a discussion with Caribou and Sada about building trust and transparency in data teams. If you missed the discussion, feel free to check out the recording.
Cheers,
🐻 Louise from Castor 🐻
