the most canonical paths across the shared surface of the world’s music, starting at the point of some particular artist and going…outward, every other direction at once.

Jonathan Cornelissen:

Relegating all data knowledge to a handful of people within a company is problematic on many levels. Data scientists find it frustrating because it’s hard for them to communicate their findings to colleagues who lack basic data literacy. Business stakeholders are unhappy because data requests take too long to fulfill and often fail to answer the original questions. In some cases, that’s because the questioner failed to explain the question properly to the data scientist.

John O’Shea:

Dashboards are the human-facing views into our systems that provide concise summaries of how the system is behaving by displaying time series metrics, logs, traces, and alarms data.

Dan Slimmon:

When building a dashboard, start with a set of questions you want to answer about a system’s behavior, and then choose where and how to add instrumentation; not the other way around.

Sarah Leo:

At The Economist, we take data visualisation seriously. Every week we publish around 40 charts across print, the website and our apps. With every single one, we try our best to visualise the numbers accurately and in a way that best supports the story. But sometimes we get it wrong. We can do better in future if we learn from our mistakes — and other people may be able to learn from them, too.

Looks like a useful visualisation for projects.

The Case for Learned Index Structures:

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes.

Henry Ward:

I have long felt there is a shadow org chart, much like a shadow economy, where employees trade ideas, give direction, offer help, and spread culture. This shadow org chart is built bottom up by employees and is very different from the top down hierarchical org chart set by me.

About PumpkinDB:

PumpkinDB is essentially a database programming environment, largely inspired by core ideas behind MUMPS. Instead of M, it has a Forth-inspired stack-based language, PumpkinScript. Instead of hierarchical keys, it has a flat key namespace and doesn’t allow overriding values once they are set.

In graphs.

A map of pain.

Graphviz is handy for generating diagrams from code.

The versioned, forkable, syncable database

Maciej Cegłowski:

A more recent and less fictitious example is electronic logging devices on trucks. These are intended to limit the hours people drive, but what do you do if you’re caught ten miles from a motel?

Alex Heath:

During internal testing, his team realized that if you don’t recognize a single artist in a playlist, you might question if it’s actually geared for you. That’s why the playlist is intended to have a mix of mostly new tracks with a few songs you’ve heard before.

Truth Labs:

“Dashboard”, “Big Data”, “Data visualization”, “Analytics” — there’s been an explosion of people and companies looking to do interesting things with their data. I’ve been lucky to work on dozens of data-heavy interfaces throughout my career and I wanted to share some thoughts on how to arrive at a distinct and meaningful product.