When it seems like more and more companies value data above all else, you might wonder what they do with so much information? Who sits down and turns all that data into something that saves organizations time and money?
Enter the Big Data Engineer.
This article will go over what a Big Data Engineer is, what they do, and which technical skills they need most to succeed on the job.
What is a Big Data Engineer?
A Big Data Engineer is a type of data science specialist with expertise working with extremely large data sets known as big data.
And what exactly are data science and big data? Let's explore these two concepts one by one.
What is data science?
Data science involves finding insights and patterns within data sets using scientific methods, algorithms, and computer processes. Different data science professionals use different methods to work with many types of data sets, but Big Data Engineers focus on particular types of data sets known as big data.
What is big data?
Even though a lot of people have heard of big data, many have different ideas about what it is exactly. That's because the definition of big data is fairly general.
When a data set is so big that it's impossible to store, process, or analyze using traditional data science methods, then the data set can be considered big data.
Most big data sets have three characteristics in common, also known as the three Vs.
Big data sets are made up of hundreds of thousands, millions, or even billions of data points that can take up petabytes of storage. For example, it's estimated that Facebook alone stores 250 billion images.
Another feature of big data sets is the speed at which new data comes in. Think of remote sensor data, which might provide updated measurements every few seconds or more.
There must be a way to capture, filter, pre-process, and store this constant stream of new data. That's where big data methods come in.
It's a good feeling when everything in a data set can fit neatly into a database with pre-defined attributes. But big data is messy. Big data means variety.
A great example of this is email. No two email messages are alike. Every email has its own text and timestamp, along with other relevant information like hyperlinks, threads of past messages, and attachments. These attachments contain additional data in the form of PDF files, spreadsheets, audio, or video.
What separates Big Data Engineers from other types of data science professionals is that they're trained to work with big data sets using specific extraction methods, algorithms, and frameworks. These tools are designed to handle large volumes and varieties of data that update and change constantly.
What can a Big Data Engineer do with big data?
Big Data Engineers incorporate these two concepts — data science in general and big data in particular — to turn enormous amounts of data into useful models and insights that help an organization succeed.
Examples of success for different organizations using big data might include:
- A streaming service improving movie recommendations based on users' preferences and viewing habits.
- A hospital using medical records to provide better diagnoses and prescribe personalized treatments.
- A large retailer being able to group different types of customers to targeted marketing campaigns and increase sales.
- An autonomous vehicle using live road conditions to optimize routing.
Common day-to-day tasks and responsibilities for a Big Data Engineer include:
- Gathering and processing raw data sets.
- Processing unstructured data into a form that can be analyzed.
- Designing and developing big data applications and systems.
- Maintaining, updating, and testing those systems.
- Performing common data-related tasks, including writing SQL queries, writing scripts, and calling APIs.
- Analyzing processed data.
- Reading, extracting, transforming, and loading data to and from frameworks and tools.
- Integrating their work with the rest of the technical teams' processes and systems.
- Supporting the business team to make sure results are relevant to improving the organization's success.
What technical skills does a Big Data Engineer need?
Whether working for an app startup or an established hospital, there are some common tools and technical skills that every Big Data Engineer should know.
Machine learning is the cornerstone of working with big data because it helps Big Data Engineers make sense of a lot of data quickly. Big Data Engineers should understand the basics of machine learning, including the types of machine learning approaches and how to account for machine learning biases.
Any Data Engineer is likely to spend a lot of time working with databases, and Structured Query Language (SQL) is the industry standard for reading, updating, and searching through databases.
Anyone interested in data science should know Python. Big Data Engineers are no exception. The open-source language is flexible, easy to learn, and boasts a huge worldwide community of contributors.
Java includes dozens of libraries and frameworks, and Big Data Engineers should focus on libraries like Java ML for machine learning.
One common approach to saving time with big data is to work with many servers in parallel rather than a single server. Hadoop is an open-source Apache framework that helps Big Data Engineers do just that.
What's the next step toward becoming a Big Data Engineer?
If you have a particular big data engineering job or company in mind, the first step is to look at the job description for specific technical and skill requirements. If you're missing one or two key skills, our online programming courses will help you fill the gap in no time.
If you're just starting out in your new career as a Big Data Engineer, we've got you covered. Our Data Scientist Career Path will help you get started with the skills you need to land your first big data engineering job on your schedule and at your pace.