When “data engineer” first started becoming a vital role for tech companies, the world was a smaller, simpler place. Engineers were primarily concerned with handling data stored in Excel spreadsheets and on local machines. New tools were slowly introduced to deal with data as it grew in volume and complexity, but the overall landscape was largely unchanged until the rise of “The Cloud.” Once Amazon Web Services and its cloud-storage-as-a-service model hit the tech world, what it meant to be a data engineer changed forever. Now all tech and all data is all about the cloud. And that’s a good thing! Today I’m going to talk about how the cloud changed everything and what else this technology will bring to the table.

Cloud Native

Cloud changed everything

It can’t be overstated: the rise of cloud storage and computing changed everything for companies and data engineers alike, and it’s never going back. Engineers were always most concerned with connecting data services to analytics and business intelligence software or whatever other systems needed to use that data. As the first builders in a company’s data world, they assembled the data pipeline and kept it in shape. 

However, with AWS and cloud solutions for everything, one of the main concerns that data engineers have today is keeping up with trends in these solutions. And because Amazon was first on the scene with AWS (and still has a huge market share), that means that engineers often need to be on top of what Amazon is up to. Certifications and training programs from Amazon and third-party providers are an essential part of being a skilled, competitive engineer.

Every company a cloud data company

Today, being a data engineer means connecting your company’s business systems to cloud-based data sources. This can be one cloud-native data warehouse where you dump all your information and sort through it later, or you could be dealing with a number of different cloud-hosted datasets from different departments that all need to be brought together into a centralized analytics platform or other software system. Cloud storage and processing is so cheap and convenient that every startup turns to the cloud for their computing needs. They’d be foolish not to. Cloud’s flexible pricing options make putting your data (and your whole app) on the cloud the smart decision.

If you are starting a company today, you’ll probably do everything on the cloud: data storage, code hosting, everything. You’ll never own a server machine again. That’s the past. Everything can be done on the cloud, so you can just worry about building the best application you can (and marketing it, scaling it, etc.) and let AWS (or whichever cloud-native data warehouse you choose) worry about the hardware, maintenance, uptime, etc. 

Cloud is great for startups because as the company scales users and their need for data storage and processing power goes up, they can just pay a little more to keep using more resources on the other end. In fact, with the free starting tier that a lot of cloud platforms offer, a startup can stay on the free tier until they start making the money they need to pay for more resources. Meanwhile, if they take a dip in user numbers, they’re not paying for resources they don’t need. This is all great for the data engineer because once the cloud data pipeline is all set up, they don’t need to constantly tinker with it. Usage may fluctuate, but the pipeline perseveres.

Cloud is also the choice for enterprise-grade companies. If you’re an enterprise and you’re not already doing everything on the cloud, then you’re likely in the midst of a massive digital transformation. Storing data in the cloud just makes sense: no machines to maintain, easy access from anywhere, and easy integration with cloud-native architecture like Linux. The bigger and more complex the organization, the more important it is that their cloud datasets can easily connect with their application software. Linux is the OS of choice for situations like these because it allows for continual, scalable rollouts of new code and provides high reliability and availability. Enterprises either already love the cloud or are learning to love it, and the new generation of startups will likely never know any other place to store their data.

Another reason every organization cares about making its data accessible is because every company is now a data company. Sure, bringing data into an application for the business’s core function is vital, but it’s only one function that data performs in modern tech companies. More and more companies are realizing that data can be monetized for other purposes or mashed up with other sources and sold back to the users as insights in ways that add more and more value to their users’ lives. And doing this all on the cloud makes the whole process simpler and more efficient, whatever you’re doing with it. In-house analytics consumers, especially business users (who tend to be mostly nontechnical), will appreciate a speedy, well-maintained cloud data pipeline because it gives them the flexibility to answer all their questions, while serving up insights in a speedy manner.  

The cloud horizon

Everything is moving to the cloud. Again: if you are starting a technology business today, you want to use cloud-native architecture and host it all on the cloud. Serverless solutions are the present and the future. No more worrying about hardware, just push your code and go. Data engineers will always be a vital part of making sure that data is going where it needs to go safely and securely, and staying on top of the latest cloud trends, even if the days of managing hardware and on-site datasets are mostly over. Check back for future blog posts where I’ll discuss why serverless cloud functions are so awesome, among other cloud computing topics.

Cloud Native
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