How to Shift from Data Science to Data Engineering
Data engineering is a rapidly growing field, and there is a high demand for skilled data engineers. If you are a data scientist, you may be wondering if you can transition into data engineering. The good news is that there are many skills that data scientists already have that are transferable to data engineering. In this blog post, we will discuss how you can become a data engineer if you are a data scientist.
But first, let’s briefly define what a data engineer is.
What is a Data Engineer?
This individual is responsible for building and maintaining the infrastructure that stores and processes data; the kinds of data can be diverse, but most commonly it will be structured and unstructured data. But they’re not lone rangers, sort of speak. Data engineers will also work with data scientists to design and implement data pipelines; ensuring steady flows and minimal issues for data teams.
They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable. These professionals will work with their colleagues to ensure that data is accessible, with proper access.
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How to Become a Data Engineer
For the data scientist who is looking to make the transition, this is the million-dollar question, and if you search online, you’ll get a million answers, but the truth is that there are a few steps that you can take to become a data engineer. So let’s go through each step one by one, and help you build a roadmap toward becoming a data engineer.
Identify your existing data science strengths.
Like with any professional shift, it’s always good practice to take inventory of your existing data science strengths. Data scientists typically have strong skills in areas such as Python, R, statistics, machine learning, and data analysis. Believe it or not, these skills are valuable in data engineering for data wrangling, model deployment, and understanding data pipelines. With that said, each skill may be used in a different manner.
For example, if you’re a talented Python programmer, there may be other packages, libraries, and frameworks that you are familiar with. This skill can easily be transferred with you as you become a data engineer, but will likely see different uses. So take note of what you can do, and see how your skills can relate.
Identify your data engineering gaps.
The truth is, you may have some amazing skills that you can transfer over, but at the end of the day, data scientists and data engineers are two completely different professions. So as you take inventory of your existing skill set, you’ll want to start to identify the areas where you need to focus on to become a data engineer. These areas may include SQL, database design, data warehousing, distributed systems, cloud platforms (AWS, Azure, GCP), and data pipelines.
Learn more about the cloud.
One thing that can’t be stressed enough is that working on cloud/hybrid platforms will become a critical element of what you do. Data engineers need to be familiar with the different cloud platforms and how to use them to store and process data. This will become even more important as teams become more global with remote work becoming a mainstay in multiple industries.
This means that not only do the proper infrastructures need to be created, and maintained, but data engineers will be at the forefront of data governance and access to ensure that no outside actors or black hats gain access which could spell compliance doom for any company.
Thankfully, any of these platforms offer sandbox accounts and free learning material so you can get your feet wet. Microsoft Azure in particular allows users to explore the Azure ecosystem and provides on-site training for users of all levels. With that said, many also offer industry-recognized certifications on their brand platforms.
ETL (Extract, Transform, Load)
This is a core data engineering process for moving data from one or more sources to a destination, typically a data warehouse or data lake. ETL tools and techniques are used to extract data from a variety of sources, transform the data into a consistent format, and load the data into the destination.
The reason this is an important skill is that ETL is a critical process for data warehousing and business intelligence. It allows organizations to consolidate data from disparate sources, clean and prepare the data for analysis, and make the data available for reporting and decision-making. Currently, there are a variety of ETL tools available, including commercial tools, open-source tools, and custom-built tools.
Stay on top of data engineering trends.
This is a big one, but one you should be familiar with because it is also applicable to anyone who works in data science. As you can imagine, the field of data engineering is constantly evolving, so it is important to stay up-to-date on the latest trends. It could be as following the right people on LinkedIn and subscribing to some awesome newsletter. But there are also other ways that you can stay in the know. First, articles. As AI continues to scale and the public’s interest in data grows with it, you can expect to see a greater volume of articles being published. Just keep an eye on them to keep track of what’s going on.
Another yet more effective tactic is by attending conferences. There you can network with fellow data professionals, check out what’s causing the earth to shake in data engineering, and even catch up with data engineering thought leaders who are trailblazing the industry.
Get more training
Clearly, this likely doesn’t have to be said, but it’s going to be said because it’s important. Get more training! Data Science is currently reshaping how the world works, and because of this new tools, models, frameworks, packages, and theories are being born at a rapid pace. So staying in the know, and applying your skills in new ways will keep you ahead of the pack.
Now there are several ways to get more training in data engineering. You can take online courses, attend workshops, or enroll in a data engineering bootcamp. These can be done virtually, in person, or using a hybrid approach. But as was mentioned above, make sure the training you’re receiving compliments your data engineering goals. For example, if you need to get your A game with cloud platforms, then taking a bootcamp on R, isn’t going to cut it. So take inventory and take charge.
The upcoming Data Engineering Summit, colocated with ODSC East, will be able to teach you all of these data engineering skills and more!
Connect with other data engineers.
Though it’s been alluded to in the blog, it’s worth having as its own section. Networking with other data engineers is a great way to learn more about the field and get advice on your career. Does it take time and energy? Yes! But it’s one of the best investments you can make as a professional. As you build a quality network, not only are you building lines of communications that will keep you up to date with the latest in data engineering, but you’ll also have an interpersonal web of other people who know you, and if required, can go to bat for you if needed.
So how are networks built? Well in this day and age, you have several methods. First, you can connect with data engineers on LinkedIn. LinkedIn is a great platform to get to know people within the field and get some great insights on what’s going on. There are also meetups specific to data engineering But the king of networking is still the conference. By attending conferences you’ll not only get some great face time with your peers, but you’ll also be able to map out how to achieve your goals as a professional.
Conclusion
If you are a data scientist, you have the skills and knowledge to become a data engineer. By following the steps in this blog post, you can transition into data engineering and start a new and exciting career.
And as any aspiring data engineering professional knows, the best way to stay ahead of the curve is by keeping up with the latest in all things related to data and data engineering. The best way to do that is by joining us at ODSC’s Data Engineering Summit and ODSC East.
At the ODSC Data Engineering Summit on April 24th, you’ll be at the forefront of all the major changes coming before it hits. So get your pass today, and keep yourself ahead of the curve.
Originally posted on OpenDataScience.com
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