The 1998 movie Armageddon was about a meteor on a path to destroy the Earth, and starred Ben Affleck and Bruce Willis as the oil drillers sent to space to stop it. The movie itself is ridiculous. But the DVD release of the movie featured a commentary track by Affleck that is way more entertaining than Armageddon itself. On the commentary track, Affleck spends most of the time making fun of his own movie. One scene in particular stands out. NASA’s plan is to send astronauts into space to land on the meteor, drill into it, and destroy it. But the astronauts are unable to learn how to operate the overly-complex drill, and so NASA decides to ask the oil drillers (who built the drill) to go into space themselves to accomplish the mission. While Affleck watches this scene on his commentary track, he mockingly wonders: in what universe is it easier to teach oil drillers to fly into space than it is to build a tool NASA astronauts can operate?
For seven years, I worked at Splunk, supporting some of their most mature customers. Splunk Enterprise is an amazing product, and Splunk customers use it to do amazing things with their massive data sets. And yet, not a week went by when I didn’t hear a user ask: how can I get this Splunk dataset I’m working with into Microsoft Excel?
People really, really love Microsoft Excel. It’s what they know, and what they’re comfortable working with.
The challenge that the users of these enterprise-scale data platforms face is that these platforms’ capabilities come at the result of tremendous complexity. There are search languages to learn, system components to administer, getting-data-in strategies to manage, permissions to fine-tune, add-ons to evaluate, documentation to read, education classes to take, and on and on. It’s exhausting. These users are smart people, usually top hires in the fields of cybersecurity, observability, IT operations, and data science. But most users are analysts, not coders nor system admins nor command-line experts, and the friction to get started can become an overwhelming burden. They’re merely trying do do their job to accomplish their mission, but the oil drill we’re giving them is too hard to use.
Enter Gigasheet, and it’s cloud-based, easily-recognizable spreadsheet interface built for massive data sets. Have 500 million rows of netflow you want to take a quick look at? Easy. A Windows endpoint that you want to search for signs of evil? Simple. A few GBs of API output you want to quickly CTRL-F through? Done. Build a timeline? GeoIP all your inbound IP address traffic? Determine potential Log4J exposure? Check, Check, Check. All of this happens in an easily-understood rows-and-columns interface, with no query languages to learn, systems to manage, GitHub pages to read, or classes to take. The Gigasheet YouTube channel is all the education you’ll ever need.
So yes, in this needlessly-tortured Armageddon analogy, today’s data analysts are the astronauts. They are smart and supremely capable in their field of expertise, but don’t have the time to become trained in complex, custom, unnecessarily-powerful tools. Why, for so long, have we been pushing them to use applications they don’t have time to learn, rather than letting them use their skill set in an interface they’re already familiar with? Why have we been forcing those rare few who are comfortable with command line interfaces and GitHub branching to also take on the duties of data scientists, compliance admins, and threat hunters?
Stop forcing oil drillers to go to space. Give your astronauts a tool they already know how to use so that they can do their day job.
Gigasheet is free. Try it out today! Sign up here.