How do Data Engineers control Big Data?

Big data is a term that's been around since roughly 2011. When it was coined, we didn't have programs like Spark or MapReduce like we have today. The definition of Big Data has evolved over time — as has the definition of what it means to be a data engineer.

Nowadays, data engineers use tools and programs in conjunction with their knowledge and experience to control Big Data. Just like you might use a shovel to move dirt if you were in charge of building a large-scale project, these tools provide data engineers with ways to interact with and manipulate huge amounts of information on different scales and contexts.

In this article, I'll cover a few of these tools and how you might use them. It will likely be rather long (my posts tend to be about two-thirds of the length of the average blog post), but it should give you some insight into how data engineers work with Big Data, and what tools they use to do so.

So, let's get started.

The first thing you need to understand is that in order to make sense of large quantities of data, you have to know what it is. I usually compare this to working with a mountain of data — you can only build great things if you know what your project is in the first place.

In the same sense, you can't see much of what's hidden in Big Data if your dataset structure isn't well defined and you don't have an overview of it all. So, what kind of information do data engineers need to be aware of?

First and foremost, they have to know where their data is coming from. They need to know what format it's in, whether or not it's been cleaned up along the way, etc. This kind of information is particularly difficult to obtain if the data has been "orphaned" or hasn't been properly tracked.

A big data engineer's job includes being able to transform and cleanse data, but they have to have a solid understanding of what they're working with in order to do so. The saying goes: garbage in, garbage out. Even if you're one-hundred percent positive that you know where your data's coming from, it's going to be problematic if it isn't organized or accessible.

What about the structure itself? The way we store our information is changing constantly because the tools we use are always changing.

Is this a good idea in the first place?  

This is another great point to understand.

The data you're working with may be essential for helping you answer a certain business problem, but there's a chance that it's not something you necessarily want to do. For example, if your data is private, such as an internal email system or another company's database, then you'd have to take that into account before doing anything. If the data is public and freely available online (like books on the web), then it may be easier for everyone to find out what they want — unless of course they're already aware of what you're working with.

Data engineers need to understand the sort of data they're working with. They also need to know how much of it there is, because of course the size matters when you've got several petabytes to deal with.

That's right — petabytes.

This is an example in which a discovery engineer would be necessary, and even then he or she might not be able to handle such a huge amount of data on their own. Data engineers use tools and programs to help manage these huge amounts of information — we'll get into that later.

This means that when you're dealing with Big Data, you'll have to be very careful about the people who handle it, who else is involved in the process, and what their job roles are.

How do data engineers know how wise this whole thing is?

Your goals aren't something you set up as if they were a checklist — they're something you keep in mind throughout your project. If this isn't the case, then it's important that you talk to other people and take advice from them on a regular basis. This way, you'll make sure the information being processed is going to lead to any positive outcomes for your company or organization.