Take into account that the BuildConfig on your app module is useful. It has the app version code, version name, applicat

Take into account that the BuildConfig on your app module is useful. It has the app version code, version name, application id and any custom flag you decided to add. So, I recommend to keep it (but remember to avoid using BuildConfig.DEBUG). So, to override the default behavior defined on gradle.properties, just add the following configuration to yourapp/build.gradle

Maybe. It depends how much you love the discipline you already know. Data engineering and data science are different, so if you’re a data scientist who didn’t train for data engineering, you are going to have to start from scratch.

When I first started out, we had an idea, turned it into a UI, and hired developers to make it real. After nearly a year and tens of thousands of dollars — we launched it.

So surely when I started my next start-up, this time things would be different. This time we talked to dozens of potential users and industry experts before we built anything. This time it worked. Sort of.

Our mission was compelling as we launched to fanfare and raised $11.5M within 10 months of founding the company. However, after just two years we were acqui-hired, our investors got their money back, and the product was immediately shut down.

I learned this truth the hard way over the past decade founding multiple companies — but it wasn’t until I was working on my third startup that I came to understand a better way to actually understand users.

Grocery shopping is easy if you’re just cooking something for your own dinner, but large scale turns the trivial into the Herculean — how do you acquire, store, and process 20 tons of ice cream… without letting any of it melt?

If you’ve just felt the urge to run off and study both disciplines, you might be a victim of the (stressful and self-defeating) belief that data professionals have to know the everything of data. The data universe is expanding rapidly — it’s time we started recognizing just how big this field is and that working in one part of it doesn’t automatically require us to be experts of all of it. I’d go so far as to say that it’s too big for even the most determined genius to swallow whole.

But the default way most of us talk to customers and prospects is unscientific and fraught with confirmation bias, putting us in danger of being lied to and wasting months building something nobody wants.

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before I dive into the structure and philosophy of this degree. I have an undergraduate degree in physics and used to study machine learning in grad school before deep learning became the hottest thing. I didn’t study computer science systematically but did take courses in algorithms and data structures. So you may see me fall into the “data science” camp mentioned above in terms of education. As for real-world software engineering and web development, I was clueless when I graduated.

Instead of expecting data people to be able to do all of it, let’s start asking one another (and ourselves), “Which kind are you?” Let’s embrace working together instead of trying to go it alone.

I determined that the missing piece of the puzzle was my lack of technical ability, so I enrolled as one of the first dozen students at a now-famous coding bootcamp and actually got a job as a software engineer at a real company.

But with hindsight, I could look back to my original research notes and see I had ignored several fatal warnings. I had listened to what they said — exactly as they said it, but I did not realize until much later that I failed to actually understand what they meant.

This might be exactly the kind of fun you want — as long as you’re going in with open eyes. Building your data engineering team could take years. Sure, it’s nice to have an excuse to learn something new, but in all likelihood, your data science muscles will atrophy as a result.

Similarly, “data engineering” is fairly easy when you’re downloading a little spreadsheet for your school project but dizzying when you’re handling data at petabyte scale. Scale makes it a sophisticated engineering discipline in its own right.