The "Aha" Moment: How to Onboard an API Service and Get Active Users
Introducing Serverless Data Feeds
Share Data Without Sharing Credentials: Introducing Pipe-level Permissions
How to Embed a Live, Refreshable D3.js Chart into GitHub Pages
A 90 Degree Tilt: Introducing Vertical Pipes
A Simple Pipe Routing Example: HTML Upload to HTML Display
Introducing our API and Command Line Interface: Flex.io for Developers
Just Binge-Listened to 95 SaaStr Podcasts, Here's What I Learned
Thoughts on the Data Ecosystem
The Flex.io Blog
Data is big these days. And in more than just one sense of the word.
Massive amounts of data are being collected through the expansion of technology, like mobile phones, social media, and the Internet of Things (IoT)—all as the field of data science surges in popularity. Being a data scientist was even crowned “the sexiest job of the 21st century“, adding it to the list of recently popularized professions that were once considered nerd territory and grounds for having your lunch stolen in grade school.
Like Doctor Dolittle’s famed pushmi-pullyu, enterprise data projects have a serious agility problem.
It’s a story as old as time. It’s a story of evolution, a story of freedom. And it’s a story of stuffing a genie back into its bottle.
Yes, I speak of the ancient enterprise battle royale: Productivity vs. Control.
These battles are constantly happening throughout the enterprise, usually with very good intentions. But, like so many good intentions, they often pave the road to, um… employee circumvention. Hey, gotta get stuff done, right?!?
Gusty winds and 14 degree temperatures are apparently no match for pizza, beer and code.
Data projects come in many shapes and sizes. From big data predictive analytics to small data spreadsheet projects, from building new open data applications to reconciling a couple of ERP tables in the accounting department.