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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
Adding Dynamic Content to a Static Web Page
Just Binge-Listened to 95 SaaStr Podcasts, Here's What I Learned
About Ken Kaczmarek
Hi there, I’m Ken Kaczmarek, one of the co-founders of Flex.io.
Bean counter by trade, data wrangler by fate and homebrewer by necessity. I’ve gotten into trouble all around the world and currently on the lam in Chicago. Don’t tell my kids; they still think I’m on the up and up. A strong believer that anyone can make a good pie filling, but true transcendence can only be found in the crust.
Since the recent launch of our Flex.io data feed API, onboarding has been top of mind. There have been gallons of ink spilled over the importance of SaaS onboarding, UserOnboard’s teardowns being one of the best.
Throughout the Flex.io journey, we’ve often used the phrase: “Data is a Team Sport.”
More often than not data processing requires some type of hand off between an ‘owner’ and a ‘user’. For instance, a business user might need an extract from an IT-supported system. Or a consultant may need a file that is owned by her customer.
We recently had the honor to participate at PyCon (the preeminent annual Python conference) in Portland, as an invitee to Startup Row. It was truly wonderful to be a part of this fantastic community of coders and have a front-row seat to the bleeding edge of Python innovation.
In the course of our private beta, I’d estimate that two-thirds of our sign-ups have come from developers and other IT folks. But, when you’re kinda, sorta reinventing the query builder for data prep and ETL, why would a developer—a demigod who can pump out a script on demand—care?
Who needs a #drinkwithharry?
Over the past few weeks, I tasked myself with listening to all ~32 hours of the SaaStr podcast, in preparation for the SaaStr Annual conference. Each show is an incredibly dense, 20-minute nugget on the business of SaaS. IMHO, it is totally and completely worthwhile to meander through twice-weekly.
As everyone knows by now, data is the new black. Conventional wisdom says businesses will either be data-driven or be at a serious competitive disadvantage.
At this point, we’ve all heard the news.Read more
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.
Much electronic ink has been spilled on the rise of the data scientist.
They’ve been called sexy. They’ve been called unicorns. And, we clearly see a direct correlation between this recent adulation and the uptick in searches for “sexy unicorn”. (At least, I hope that’s the reason).
I recently found an article discussing the four different types of Data Scientists. Turns out there’s a quite a bit of wiggle in what the term “Data Scientist” might mean – from business savant to data viz wiz to world class coder to Ph.D. in statistics. A question is posed: