In old English, endleofan means one left as in one left over ten, which brings us, rather ineloquently, to issue 11 of Knowledge Graph & Data Mesh Metaverse. In it, we share interesting articles about this world. We share a lot of content via our Discord channels, so we thought we’d share the love and provide a regular update on what’s been floating our boats.
Were the great that great?
This article from Benn Stancil is neither data mesh nor knowledge graph, but is great all the same. He asks the question of whether data will have its day of reckoning as more businesses realize that they can never be Google or Facebook due to their data limitations. Rather than looking at Google for inspiration, should smaller companies be more realistic with their data-harnessing aspirations?
Getting technical and looking at the innards of our graph database
Our CTO, Gavin, has written this three-part blog looking at how TerminusDB works from a technical perspective and explains many of the technical decisions that led us to build an in-memory, immutable document graph database.
TerminusDB Internals part 1 talks about making it smaller, faster, and stronger and why we chose graph databases to spare the use of memory. Part 2 looks at making the immutable, mutable, and how our methods are similar to multi-version concurrency control. In the final part, Gavin explains how we deal with data layout, search, and compression.
Knowledge graphs, web scraping, and machine learning
This is a nice article looking at how knowledge graphs and web scraping can be used together to help build training data where there isn’t enough to derive meaning from machine learning.
Data Mesh: Real examples and lessons learned
Data Mesh pioneers, Thoughtworks, have written this article to provide some ‘lessons learned’ to those thinking of embarking on a data mesh journey. Interestingly, and unsurprisingly, many of the lessons are people-based rather than technology.
Towards a practical Data Mesh roadmap
We included an Eric Broda story in Metaverse 10, he’s followed it up with this article providing some guidance to building a roadmap to get the many teams that will be involved aligned. It also features some links to further reading along the same subject.
DFRNT – Visual graph data platform
DFRNT is a service built by one of our community members and is a visual graph data collaboration platform for teams adopting semantic knowledge graphs. The aim of the platform is to provide a convergence between visual and tabular data collaboration to help teams solving complex problems easily model and iterate data in a visual way.
It is aimed at challenges such as data governance, strategy execution, decision science datasets, OKRs, impact graphs, GRC (governance, risk, and compliance), and other organizational data challenges where data curation is essential. DFRNT comes with production-level TerminusDB hosting as part of the subscription.
An Engineer’s Guide to Data Contracts
For regular readers of Metaverse, you’ll know we hold Chad Sanderson in high regard. In his newsletter, he’s written a three-part guide to data contracts (at the time of writing, two are released). Chad is a big advocate for data quality and has been in regular discussions about data contracts across his social channels. He voiced concern that much discussion, including himself, involved conceptional viewpoints on data contracts. Chad has remedied that with a well throughout and useful guide for engineers.
Data Mesh Fitness Test
Although simplistic, this data mesh fitness test gives you a number of options to think about to decide if data mesh is the solution for you. It’s a useful decision-making tool for management to think about top-level requirements before intensive research is undertaken.
Thanks for reading, if you’ve seen or read anything that you think others might like, get in touch via our Discord community and we’ll ensure we shout about it – and give you credit :).
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