◆ Software · Work Atlas

The real work of Apache Spark

Forget the menus and the buttons. This is what people actually get done in Apache Spark at work — the real tasks, sorted by the kind of work they are, each with a prompt you can use tonight.

57evidenced tasks
3kinds of work
See the tasks ↓Open the interactive atlas

What kind of work is it?

tap a kind to filter the tasks

Which one is you, right now?

Pick the moment · no score, no sign-up
Where are you standing with your work today?
Whichever you pick, the four prompts below cover it.

The work, task by task

57 tasks
hands on the workAnalyze data to extract meaningful insightsopen it → hands on the workAnalyze data to identify patterns and trendsopen it → hands on the workAnalyze data to identify trendsopen it → hands on the workAnalyze data to support investigationsopen it → hands on the workApply machine learning techniquesopen it → hands on the workApply sampling techniques to determine groups to be surveyed or use complete enumeration methods.open it → hands on the workApply scientific methodsopen it → hands on the workBuild models and perform hypothesis testingopen it → hands on the workCategorize and organize dataopen it → hands on the workClean and process raw dataopen it → hands on the workClean dataopen it → hands on the workCollaborate with scientists and engineersopen it → hands on the workCombine statistical knowledge with codingopen it → hands on the workContrast industry processes with company operationsopen it → hands on the workCreate and maintain BI dashboards and reportsopen it → hands on the workCreate data visualizations and dashboardsopen it → hands on the workCreate new computing languages and toolsopen it → hands on the workCreate projections for company growthopen it →

Questions people actually ask

honest answers, no sign-up

Every task here was seen in the real world. Someone doing the job named it, a real job ad asked for it, or a lot of people asked about it online.

If nothing real showed a task, it is not on the page. That is the whole rule.

They are the same job approached four ways, because what you need depends on where you are.

Get it done today. Make it easy for the next person to say yes to. Work out the right move when you are stuck. Learn the pattern so the job stops coming back.

For most of these jobs it can carry the heavy thinking - draft it, sort it, check it, rehearse it with you.

It cannot sit in your chair, take the blame when a number is wrong, or notice what nobody wrote down. Let it do the first 80%. Keep the last 20% that is truly yours.

No. Copy any prompt and paste it into the AI you already use. No account, no score, no wall in the way.

Any of them. The prompts describe the work rather than naming a product, so they are not tied to one assistant.

That is also why they keep working when you switch.

Change it freely. Every prompt is a starting line, not a rule.

Put in your real numbers, your real names and your real deadline. The more you make it yours, the better the answer comes back.

The tasks come from real job ads, published job data and the questions people ask in public forums.

The steps come from Apache Spark's own documentation, with practitioner sources for the traps the manual does not mention.

Push once. Ask it to sharpen the weakest part and to say what it assumed.

Most wrong answers come from a missing detail rather than a bad prompt - tell it the thing it could not know.