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What is a data mesh--and how not to mesh it up 
The ULTIMATE Guide To Data Lineage Our Top 5 Most Popular Data Engineering Articles In 2022
Michael Segner
Michael writes about data engineering, data quality, and data teams.
The Pareto Principle, which holds 80% of the results will derive from 20% of the cases, is tough to escape. It definitely holds true for our Data Downtime blog with these five articles driving a majority of our traffic in 2022.
There are a few characteristics that separate these articles from the chaff, namely:
- They were among the first to describe or even define a nascent concept.
- The concept they examine is transformative. It is a departure from the current best practices of the time and conventional thinking.
- The concept is explained in a simple and compelling way, without shying away from the practical implementation details.
So let’s take a look at the best of the best.
Table of Contents
- What is a Data Mesh — and How Not to Mesh it Up
- What is Data Observability? The 5 Pillars You Need To Know
- Is the Modern Data Warehouse Broken?
- Data contracts: 7 Lessons from GoCardless’ Implementation
- How to Build Your Own Data Platform
- The common thread: The data engineering world moves pretty fast, blink and you just might miss it.
What is a Data Mesh — and How Not to Mesh it Up
Zhamak Dheghani set the data world ablaze when she introduced her revolutionary socio-technological concept, the data mesh in 2019.
Not too long after, our co-founders, Barr and Lior, wrote this article to help introduce and simplify the complex new concept as well as convey its importance to data teams.
Years later it remains our most popular blog thanks to clear explanations and a straightforward cheat sheet to determine if data mesh is right for your organization. The titular pun might have helped too…
What is Data Observability? The 5 Pillars You Need To Know
This seminal article not only helped launch Monte Carlo, it launched the rapidly growing category of data observability.
As the category creators, we feel a responsibility to revisit this article somewhat frequently to ensure it remains relevant in a rapidly evolving space while also preserving its original charm.
Is the Modern Data Warehouse Broken?
We saw Chad Sanderson dropping LinkedIn truth bombs on the fundamental challenges of the modern data stack and knew we needed to give him a platform to explore them indepth.
This post actually precedes the coining of “data contracts,” a term to which Chad would become so closely associated in the coming year. He makes his case for why data consumers need to be put in the driving seat and data should be pre-modeled before landing in the data warehouse.
Data contracts: 7 Lessons from GoCardless’ Implementation
Data contracts are one of the most discussed data engineering concepts on LinkedIn, but it wasn’t always that way.
We met GoCardless’ Andrew Jones at one of our IMPACT tour events earlier this year and knew we needed to bring his experience implementing data contracts to our audience.
As one of the first real world data contract implementations, this post provides unique insights into implementation tips and lessons learned.
How to Build Your Own Data Platform
The modern data stack is a relatively new concept, and every year there are more technologies, best practices, and vendors.
We don’t profess to have the most complete encyclopedia of this expansive universe, that would probably be lakeFS, but sometimes you just need a quick and dirty guide. Well, that is exactly what we put together with descriptions of the most common data stack layers and the technologies within them that you need to know.
The common thread: The data engineering world moves pretty fast, blink and you just might miss it.
With apologies to Ferris Bueler, the takeaway from this series of articles is the fierce pace of innovation within data engineering.
These aren’t small concepts being pushed out for the sake of a few likes on LinkedIn. Data mesh, data observability, and data contracts all started as ideas, but have transformed into real implementations driving serious results. Try not to blink.
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