5 reasons why you should pursue a career in Data Engineeringon 17 September 2022 for Graduates
We’re just going to come out and say it: Data Engineering kicks Data Science’s ass. But before we dive into the why’s, let’s recap quickly what the difference is between Data Engineers and Data Scientists.
DATA ENGINEER: It’s the Data Engineer who prepares the data infrastructure for analysis. Data engineers design, build, test, integrate, manage and optimize data from a variety of sources. They’re also the ones who build the infrastructure and architectures that enable the generation of data, which can then be analyzed by the Data Scientists. Data Engineers focus on raw data, formats, resilience, scaling, data storage and security. They’re also tasked with building data pipelines and writing complex queries to ensure that data is easily accessible.
DATA SCIENTIST: Data Scientists turn raw data into valuable insights. They use statistics, machine learning and an array of other analytic approaches to solve specific problems their organization is facing. In contrary to Data Analysts, Data Scientists are expected to develop new algorithms and handle big data, that’s why they’re usually way more skilled in Python, R and Hadoop, to name just a few.
Data Engineers seem to be the underdogs.
Many of you want to become Data Scientists. And while there’s absolutely nothing wrong with that, we do notice many of you turn up your noses at Data Engineering jobs. We’re convinced that this reaction is based on some misconceptions of what both jobs really entail, and what the difference and the correlation are between them.
We’re here to make the case that, while being a Data Scientist sounds sexy and important, it’s actually better to start your career in Data Engineering. You can make your way to Data Science from there, if you want to. Or you might find it’s so awesome you just want to continue on your career path as a Data Engineer.
Here’s why we believe you shouldn’t write off a job as a Data Engineer:
1. Data Engineering is more important
Plain and simple: Data Engineering is the foundation on which Data Science is built. Without Data Engineering, there can be no Data Science!
You know what they say: garbage in, garbage out. What good is deep learning if you’re working with crap data? Much like we need food, water, shelter and even friendship more than we need status, freedom or self-actualization, companies need to satisfy the data needs hierarchy from the bottom up.
Logging, storing, and analyzing the data comes first; machine learning, AI and deep learning (the Data Science part) comes second. There you have it: Data Engineering is the foundation of a successful data-driven enterprise.
2. It’s good to have Data Engineering skills, even when you’re working as a Data Scientist
The majority of companies require their Data Scientists to do some Data Engineering tasks as well, so having the appropriate skills is no superfluous luxury. Even if you don’t need Data Engineering skills in your day-to-day job, it’s still beneficial to have them on hand. For instance, if you don’t know how to fix an ETL pipeline or how to clean data and you have to ask for help every time, that will create a major bottleneck in your workflow.
3. Having Data Engineering skills means job security
As long as there is data to process, Data Engineers will be in demand. And boy, is there data to process! Did you know we produce around 2.5 trillion gigabytes of data every day? That’s a lot. To give you an idea of how much it is: if you wrote all that data down in books, and you’d pile all those books on top of each other, one day’s worth of data would be enough to build a bridge to the moon and back. How’s that for mind-blowing?
So it’s no surprise that the demand for data roles is increasing steadily, with a 35% average annual growth according to LinkedIn’s 2021 Jobs on the Rise Report.
4. You can (easily) move from Data Engineering to Data Science later on
With some additional training, Data Engineers can become Data Scientists and vice versa. After all, there is considerable overlap in skills, meaning that you’re equipped with enough foundational knowledge and vocabulary to make an easy transition between these two career paths.
So, why do we recommend starting as a Data Engineer and not the other way around?
5. Data Engineering is harder to learn than Data Science
Since there are more resources available for Data Science, and a number of tools and libraries have recently been built to make Data Science easier, that makes it less challenging to transition from Data Engineering to Data Science than the other way around.
If you’re just starting out in your career – you’re young, you don’t have a lot of responsibilities yet – then why not take on a more challenging workload first? Our advice would be to just buckle down and do the hard work now, you’ll reap the benefits later.
Data isn’t going anywhere: globally, 2.5 trillion gigabytes of data are produced every day. By the end of 2022, there will be 97 zettabytes of data in the world (1 ZB equals a trillion GB – so if you put that in bytes, you’d have to use no less than 21 zeros). By 2030, this number will grow to a staggering 572 ZB! In short: a data career is probably a safe choice when it comes to job security.
There are many careers in data to pursue, but as is true for most things in life, it’s always best to start with the basics. In this case: Data Engineering. There are many available jobs for Data Engineers today, so you’ll definitely be able to find one that suits you. Once you’ve got the data engineering skills down, you can grow your skills even further and acquire Data Science skills as well. There are many possibilities for growth and lifelong learning when you pursue a career in Data Engineering!
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