In the rapidly evolving data world, the role of a data engineer is undeniably becoming indispensable. This job is in high demand.
Thus, more and more youngsters are considering going for this career. The question is, is data engineering hard? What challenges do students face?
This article acts as a guide to everything you should know about this major, looking at how challenging it is and how long it takes to learn.
Data engineers develop, manage, and maintain a business’s information infrastructure.
Their duties involve transforming and collecting information and keeping it in easy forms for other people to use in databases.
They work with high-level data scientists, analysts, and leadership to ensure their systems and models blend well with individual teams’ needs and the broader business strategy.
These responsibilities tend to make data engineers perform in higher positions or play senior roles.
Yes. Data engineering is hard because it focuses on storing, transforming, and moving statistics, requiring learners to master various technologies and tools.
You’d better expect data engineering to be challenging as it is an intensely technical field. To be more specific, here are five things that make DE a hard field to break into.
If one is an excellent DE, he must be an excellent developer. Everything is in the code form, pipeline, infrastructure, etc. You’ll need to write code and scripts.
It is worth stressing how essential it is to gain a robust programming background before working as a DE.
Plus, you need to love information and be interested in looking for patterns, or else you’ll find the job tedious.
The interest in and ability to build complex and arduous systems are also important. Remember that large projects are 10-15 times more challenging than small ones.
A DE needs to be adept at creating an infrastructure with reliability in mind, so any changes will not ruin the pieces. Thus, experience in developing is a treasure.
You’ll be responsible for three primary duties:
- Ensure the processing and acquisition of information (information pipeline) is working
- Meet the requirements of internal clients – statistic analysts and scientists
- Keep the costs of storing and moving statistics under control
ETL, R, Python, and SQL practices and methodologies are the necessary skills.
A solid understanding of a language’s foundation will enable you to enter any organization.
A college or university may offer courses in programming, yet those wishing to become successful DEs will have to master the systems and technical sides themselves.
They’ll have to study various technologies to pick the correct tool for their jobs in the tech field, such as:
- Apache Crunch
- Apache Kafka
- Apache Impala
- Apache HBase
- Apache Hive
- Apache Spark
- Apache Hadoop
For instance, to choose an appropriate NoSQL cluster, the worker needs to deeply understand the upsides and downsides of 5-10 NoSQL technologies.
Then, he can narrow the list down to 3-4 options for a look in detail.
So, how can you gather all those necessary skills? The best way is to get a real job. Indeed, you don’t have to earn a master’s degree to work as a DE.
Though education plays a vital role, there are so many things you won’t have a chance to learn unless you enter the real world. That means you need to work with real clients.
There are numerous skills that site engineers and developers have significant overlap with DE responsibilities.
Plus, training in data science and software development skills, like math and statistics, is extremely imperative.
As a DE, you’re responsible for collecting statistics for analysts and scientists who need the business’s statistics in an easy-to-read format that allows them to work with it using their tools.
In a word, education has a certain place, yet experience tailor-designs the best engineers.
Besides hard tech skills, a successful DE should equip himself with particular soft qualities and abilities:
- Willingness to fail and learn
- Interest in supporting others from back-end systems
- Great communication skills
- Excitement about clean design
- Focus on detail
Being flexible and sassy is critical. As you’ll serve internal teams, it’s essential to understand what the business is trying to achieve and how those analysts attempt to support it.
If the scientists want to utilize a particular tool, DEs will have to create an environment that makes it easy to utilize that tool.
This field is witnessing a considerable shift, transforming into delivering data services.
It was also costly to process and store statistics, making information isolated from others. Few people could access it and change it.
Nowadays, with the help of the cloud, storing and processing statistics are easier and more affordable.
While these workers are still in charge of that infrastructure’s performance, the job changes towards maintaining things as statistics have become more significant.
It depends mainly on how dedicated and consistent you are studying this topic. Your schedule and experience level also contribute to determining the length of your learning.
For instance, it’ll take less time and energy to seize the concepts and theories if you have a robust computer science foundation.
If you’re a software developer or engineer planning to enter the DE field, things should be more effortless.
Nevertheless, if today is the first day you learn the subject, it’ll generally take 12 to 15 months to acquire a job.
As data continues to arrive and process, talented DEs are in high demand, leading the job to be a handsomely paying career.
An average DE in America can earn $120,813 a year, while some experienced senior workers earn up to $184,562 a year.
Statistics is the backbone and framework of a business, so employers are forever on the hunt for determined and cultured data engineers.
Any highly technical job will be arduous to study and master; so will data engineering. Yet, fun projects will be waiting for you as long as you know how to take advantage of your resources and are devoted enough.