Transcend the constraints of English parlance for better organization and faster interpretation of your work.
Do you remember when you learned how to format calendar dates for alphanumeric sorting? You either had dates in the month-first US format, e.g. 09–11–1991, or in the day-first European format, e.g. 11–09–1991, and you realized that sorting didn’t work. Date sorting needs to happen first by year, then by month, and finally by day-of-month, e.g. 1991–09–11.
Or — do you remember when you learned better practices around clearer naming for variables, methods, and classes? …
I’m presuming you’ll agree:
Dr. Padhi brings to Tag.bio a wealth of experience in cloud computing architecture and infrastructure for data research and discovery, as well as over 16 years of experience in building products, strategies and solutions for research communities. Most notably, he has led various scientific and computing projects to solve many challenging problems in the scientific world.
Across the span which ranges from data engineering, data applications, data science, and data analytics, to the industry verticals in which my company is focusing — Healthcare and Life Sciences — there are bazillions of acronyms, jargon terms, and buzzwords.
These code phrases are often quite useful for:
You know how sometimes you get sick after an intense period of Adulting, and someone inevitably says “it’s just your body telling you to slow down"?
After 7 years of startup hustle, on Saturday my 2-year old laptop told me to slow down.
It happened out of nowhere — my trusty machine which had been happily crunching code just a few hours before presented me with what folks here in Brussels (presumably) call l’ecran noir.
Turned it off, then on again. Nope. Tried again.
Panic. Please, not this, not now.
Over the next few hours, no web-searched solution or…
The era of the monolithic data warehouse/data lake is coming to an end — long live the decentralized data mesh!
Oh, do not despair! All those person-years spent cleaning, transferring, and loading data into your centralized systems hasn’t been in vain. With data mesh, you don’t have to start again from scratch with new technology — i.e. you don’t have to replace your RDBMS, Snowflake, or Databricks with a new vendor or open-source solution. A data mesh will simply utilize your existing databases, warehouses and lakes as nodes in its greater, decentralized network of data products.
Data Science —
Answering questions with data —
Is presumed to be an art,
Or at least a high-tech craft,
Producing exponential value and driving innovation.
Answering questions with data
Needs to be faster,
They design a plan —
A centralized data lake with dashboards!
But it takes too long to build.
It goes over budget.
Centralized data doesn’t scale —
And dashboards aren’t specific enough to be useful.
To this day,
Eighty percent of questions are answered the slow way.
It’s a human-scale process —
Emails, meetings, queries, modeling & analysis —
Waiting, waiting for weeks
For the bottleneck…
…brimming with immense potential value for discovery in science, business and society.
Unfortunately, the actual utility of most collected data is greatly diminished for value discovery/extraction purposes — like drinking salty seawater, the cost/benefit is a net loss. Why is this?
An ever-growing list of anti-patterns and symptoms, in no particular order.
I think about this mostly from the SELECT-side, so I’m sure there’s a fair amount missing on the INSERT/UPDATE-side, and also from the NoSQL perspective.
You should be able to read this straight through, even though terms are presented in alphabetical order. Alternatively, you can jump around to specific terms of interest.
Terms in bold (←except this one) are all defined in this glossary. I’m going to figure out how to turn them into anchor links later.
What if a user’s Data Experience in software were primarily driven by server-defined functionality — instead of being driven by front-end functionality?
This would turn a front-end application into a simple browser of server content — which seems feature-weak — that is, if you only have one type…