Data engineering is becoming increasingly popular in modern times. However, with all the hype around big data, there’s a lot of confusion between the job roles of data engineers, data scientists, and machine learning engineers. So, it’s natural to wonder if data engineers use machine learning concepts in their day-to-day activities.
Data engineers don’t do machine learning themselves; machine learning models are developed by data scientists. Still, it’s recommended for data engineers to have basic knowledge of machine learning concepts. It helps them understand data scientists’ needs and collaborate with them better.
In this article, we’ll discuss how a basic understanding of machine learning is helpful to data engineers. We’ll also look at what data engineers do instead of machine learning and how data scientists use machine learning.
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
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Does Machine Learning Form the Core of Data Engineering?
Data engineering is a broad field, and the exact job responsibilities vary wildly depending on the company. It’s easy to find two very different job descriptions for data engineering positions at two organizations. This distinction exists primarily because this role is defined by the company’s needs. Businesses hire data engineers to solve their particular problems.
However, for the most part, machine learning does not form the core of data engineering, regardless of where you’re working. The job of a data engineer revolves around preparing data for operational or analytical uses. This means that they don’t perform analysis or apply machine learning models on the data; they only optimize the data so that it becomes suitable for such operations.
Why Should Data Engineers Learn the Basics of Machine Learning?
Now, you know that machine learning is not a part of data engineering. However, that doesn’t mean data engineers never have to think about machine learning algorithms. They still need to have a basic understanding of how machine learning works to be more efficient in their jobs.
Data engineers closely work with data scientists who analyze data and build machine learning models to solve business problems. Suppose you know the basics of machine learning algorithms and data modeling. In that case, you can understand the big picture of the data function. This helps you create solutions that can be used by your team members, setting you apart as a data engineer.
Companies have hired you primarily for data filtering and data optimization. However, by studying a bit of machine learning, you can effectively communicate with data scientists, understand their needs, and help them produce better results. This makes you a valuable asset to the company.
So, it is recommended for data engineers to learn the basics of machine learning algorithms, data structures, data modeling, and statistical analysis. Again, you don’t have to become an expert in these subjects (although you certainly can!); just learning the fundamentals will do.
What Do Data Engineers Do if Not Machine Learning?
If data engineers don’t use machine learning in their day-to-day activities, what do they do? Well, data engineering is all about collecting, cleaning, manipulating, and arranging data so that data scientists can analyze it to gain insights.
However, a lack of machine learning doesn’t make data engineering an easy job. Processing datasets is a big part of a data engineer’s position, so they must be excellent coders. If you want to learn more about it, we’ve written a full article discussing how difficult it is and how long it takes to become a data engineer. You can read it here: Is Data Engineering Easy? Or Is It Hard?
For now, here’s what data engineers do:
Data engineers need to have strong coding skills. Python is the most popular and commonly used programming language in data engineering, but there are also many alternatives such as Java, Scala, R, and Golang. Most data engineering job listings require candidates to be proficient in at least one of these languages.
Knowledge of programming languages is a critical skill for aspiring data engineers. The better coder you are, the more competent data engineer you become. In another blog post, we have discussed this in detail, including the programming languages data engineers use. You can read it here: Does Data Engineering Require Coding?
Managing and manipulating databases is at the core of data engineering. SQL is the standard language for this purpose. According to Cloud Academy, it is the most sought skill for data engineers. This means data engineers need to be an expert in SQL as they use it every day.
Apart from SQL, there are other database programs as well, such as Cassandra and Bigtable. There are also NoSQL databases, which are flexible, cost-efficient, and scalable. Data engineers handle both SQL and NoSQL databases. They gather data from various sources, clean it by filtering out unnecessary data, and organize it to make it suitable for analysis.
Before data engineers can work on data, they need to store it somewhere. Since we’re generating large amounts of data every day, they should know where and how to store the data securely. This is where data warehouses come into the picture. They are used to store huge volumes of data for analysis and query.
Data engineers need to be familiar with and experienced using data warehousing solutions, such as Redshift, Hive, and Panoply. They must also have a thorough knowledge of data pipeline construction and ETL (extraction, transformation, loading) processes.
Who Uses Machine Learning?
Machine learning is an essential aspect of any company’s data strategy, but if data engineers have nothing to do with machine learning, who handles all the AI stuff?
Well, data scientists are the people who deal with machine learning. They analyze the data optimized by data engineers and build machine learning models to solve business problems. They may also interpret the data to provide insights into the workings of the company or its users, spot patterns or trends, or find ways to fix issues.
Once data scientists have prototyped an algorithm, machine learning engineers take it and make it work in a production environment at scale. A data scientist’s job is to apply lots of mathematics and statistics to create a machine learning algorithm that would solve a specific problem. A machine learning engineer then takes that idea and writes the necessary code to make it a reality.
So, these are the three steps of the entire data process:
- Data engineers filter and optimize data for analysis.
- Data scientists perform statistical analysis and model ML algorithms.
- Machine learning engineers code that ML model to make it work in reality.
Data engineers don’t directly deal with machine learning algorithms, but they constitute a vital part of the data process. Some people even say that data engineers are the backbone of data science, which is true.
What we have discussed here are the typical job responsibilities of data scientists and machine learning engineers. As we’ve said, the actual work varies from company to company.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Data engineers perform various tasks related to data filtration and data optimization. Machine learning is not a significant part of their job; data scientists are the ones who develop machine learning models.
However, it is recommended for them to know the basics of machine learning so that they can cooperate better with data scientists and help them produce better results.
Instead of machine learning, a data engineer’s job is centered around coding data pipelines, storing large volumes of data using data warehouse solutions, and manipulating databases to make them optimal for analysis and other machine learning operations.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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