Hello friend! I hope you like this new world of ours. It’s a lot different than the world of 2007. Quick tip: if you just got a mortgage, go back and cancel it. Trust me.
I’m glad that you’re still interested in computers! Today we have many more of them than we did 10 years ago, and that comes with new challenges. We wear computers on our wrists and faces, keep them in our pockets, and have them in our fridges and kettles. The cars are driving themselves pretty well, and we’ve taught programs to be better than humans at pretty much every game out there — except maybe drinking.
(Web) Apps
You might have seen the release of the iPhone just before you stepped into the time booth. Apple is the biggest and richest tech company, mostly due to the iPhone and its operating system, iOS. Google has this competing thing called Android, and Microsoft tried to get a slice of the ever-growing pie with Windows Phone. It didn’t work out.
We started calling programs apps, and some websites are calling themselves web apps. In 2008, Google released a new browser called “Chrome.” Nine years later it’s the most popular way to get on the Web.
The Chrome team invested a lot in working with JavaScript, and the code gets better every month. Web apps are written using a lot of JavaScript, and they resemble the desktop interfaces of your time.
Companies have also invested in JavaScript to make it better—it now supports classes and modules. We use languages that compile to JavaScript, like TypeScript (from Microsoft, they’re cool now) or Flow.
We write a lot of JavaScript these days, since nobody supports Flash anymore. We even run JavaScript on the server, instead of Perl, using a thing called Node. It sounds easier than it is.
Remember Swing, SWT and the likes of wxWidgets? We had to reinvent them for the browser world. Several new UI programming models emerged, which mostly focused on components.
We had to find a way to design, build, and test apps while keeping them responsive (a term we use to describe a website that doesn’t look like crap on a mobile phone). We also needed to keep it slim — not everybody has a fast connection, but everybody has a browser in their pockets.
To help with all this, there are now component frameworks. The term is vague, since it includes the likes of Angular by Google, React by Facebook, and Vue by the community. But it’s the best term we have.
By the way, I’m not sure you remember Facebook from 2007. It was getting big in the US around that time, and now it’s bigger than huge. Boasting more than a billion users, it’s also one of the largest codebases in the world.
The Facebook development team writes a lot of great code and publishes it online. They have their own conference, F8. Most big companies have their own conferences.
CSS also had to evolve, since the new apps require more intricate layouts. We don’t use tables with images anymore. Frames are gone as well. Instead, we have created new standards, like CSS Floats, Flexbox, and CSS Grid.
People had to iterate on these standards, and they’ve built libraries to make things look consistent, like Bootstrap, Foundation and many more. Similar to JavaScript, we have created languages that compile to CSS. They make up for some of the things that CSS misses, like variables or modules. It’s still hard.
It’s okay to be lost
Don’t feel bad if you’re confused. The truth is that we’re all a little confused — and it’s okay to be so. There are many more developers on the planet now, and tech companies are becoming more successful. For a while we used the term “startup” to describe companies that grew quickly and didn’t know what to do. But even this term has become old.
Data
There are more programmers, more programs, and more devices. We have more data now. Computers had to grow powerful enough to process it all, and we have developed several techniques to turn that data into insight.
First, we created a field called Data Science, which aims to learn about and extract information from data.
For example, a startup called Waze let people install an app on their phones that would track their movements while they were in their cars. Because many people installed the app, Waze got a lot of data about how cars move. They used it to develop programs that understood where traffic jams were.
Now, when you open Waze on your phone, you see traffic jams on the map in real time and choose another route.
Waze has since been bought by Google. This happens a lot with startups.
There were three main challenges with Data Science — storing data, understanding data, and acting on data. We’ve improved in all of these areas. Let’s look at each one.
Storage
We now need to store a lot more information and then find out which part is important. We needed to invent new databases. The likes of MySQL and PostgreSQL weren’t fit to store terabytes of data (we called it Big Data).
Big, internet-first companies typically faced these challenges, and so they were on the forefront of developing the technologies. Most of the time, technologies were first used internally and then open-sourced.
There was a movement we called NoSQL. This new class of databases took some of the conventions of traditional Relational databases and turned them around.
There’s Hadoop, which deals with how the data is stored on many hard computers. It defines a way of processing the data called MapReduce (inspired by a paper from Google — big companies write good scientific papers these days).
Then there’s Cassandra, which looks at data not as tables, but as sets of keys and columns which can be stored on different computers. It also makes sure that any of these computers can go offline without causing data loss.
And we have MongoDB, a database that is easy to install and use for prototyping apps. In 2017, we’re treating technologies the same way we treated pop stars ten years ago — we zealously defend some of them and vehemently hate others. MongoDB — like the band Nickelback — belongs to the latter group.
Learning
In the “understanding data” camp, most of the focus has been in an area called Machine Learning. There have been many new techniques, from naive classification to deep learning, that are now in every Data Scientist’s toolbox. They mostly write Python and work alongside developers to put machine learning pretty much everywhere.
For example, with the help of Data Scientists, a lot of web apps use A/B testing. This technique serves two slightly different versions of the app to different, but similar, groups of users. It is used to see which version leads quicker to our desired goal, whether that’s a sign-up or a purchase.
A lot of big companies like Airbnb (pronounced air-bee-en-bee), Uber, and Netflix are running hundreds and thousands of A/B tests at the same time to make sure their users get the best experience. Netflix is an app where people can binge-watch TV shows on all their devices. ¯\_(ツ)_/¯
Microservices and The Cloud
Companies like Netflix are enormous. Because they serve a lot of people, they have to make sure they are up and running at all times. That means they have to manage their computers pretty well. They can add hundreds of new servers when they’re needed.
This is difficult to achieve in a traditional data center, so the amazing engineers at Netflix use virtual machines. Remember Amazon Web Services, which launched back in 2006? Back then, they started offering Elastic Cloud Compute, known as EC2, to help people get virtual computers in Amazon’s data centers.
Today, they have almost 80 similar services, all built to help companies grow quickly. We used to have a trendy name for that — “The Cloud” — but this term is as difficult to define as NoSQL.