Data Engineering Vs Machine Learning

Ever since the importance of data was recognized in our society, several important roles came into the limelight. Today, these job roles can be classified into tens of different roles. Although they’re all worthy of their own discussions – today we discuss Data Engineering vs. Machine Learning.

Is there a big difference in the two fields? Do the two career paths intertwine at one point? Let’s go into a deep-dive and try to find answers to these.

Data Engineer vs. Machine Learning Engineer

First thing’s first – let’s discuss what both Data Engineers and Machine Learning engineers are tasked to do:

Data Engineers are focused on the creation of scalable infrastructures for extraction, transformation, and loading (ETL) while focusing on establishing pipelines between data sources and data analysis tooling

On the other hand, 

Machine Learning engineers are third following Data Engineers and Data Scientists. They’re focused on the deployment of data models prepared by Data Scientists to feed data, improving those models, and developing production-grade analysis systems.

The two definitions make it super clear these two fields are completely different. Data Engineers seat first in this pipeline of data analysis wherein the data has to be extracted, processed, and cleaned for a model to be made. Machine Learning engineers then seat third after Data Scientists which prepare models and they deploy such models in production systems.

Though we’ll cover how to become a data engineer soon; it doesn’t require degrees but good skills on several tools. Machine Learning engineers are typically experienced developers. Although even that doesn’t require a degree; they’re still expected to specialize in several back-end technologies.

If we focus on the KCE process of Knowledge, Certification, and Expertise; let’s shift to Certifications. A Data Engineer can certify themselves from a certifying body and showcase their potential by signing up for the following certs: 

  1. Amazon Web Services (AWS) Certified Data Analytics
  2. Microsoft Azure Data Engineer Associate
  3. Google Professional Data Engineer

Similarly, certifications for Machine Learning engineers include:

  1. AWS Certified Machine Learning – Specialty
  2. Microsoft Certified: Azure AI Engineer Associate
  3. Google Cloud Professional Machine Learning Engineer

Let’s shift our attention to expertise now;

Tool-wise, Data Engineers use a wide list to make the ETL process extremely effective. This can include Big Data tools like Hadoop and Spark, Database knowledge of SQL and NoSQL, Cloud Infrastructure to setup cloud-based data lakes, and programming as well.

Machine Learning engineers are typically experienced in several tools but keep a specific tool set for model implementation and deployment. TensorFlow has recently been the favorite amongst many along with several other tools. Model deployment can be done with services like Amazon Sage Maker, Microsoft Azure, or Google Cloud ML.

Do Data Engineers do Machine Learning?

In this quest to resolve the differences between Data Engineering vs. Machine Learning – our next question is whether data engineers do machine learning or not. The answer, however, is a tad-bit complicated.

One thing’s for sure; you won’t be tasked to do deploy machine learning models if you’re a Data Engineer. Data Engineers primarily work on fool-proof infrastructures and setting data pipelines. Often, Machine Learning engineers will be grouped with a Data Engineer or the Engineer be embedded in a team of ML enthusiasts.

The data provided by a Data Engineer is what eventually helps them out. Since there’s this close relation between the two fields, there’s a high chance you’re called on to work on these data models someday. This is eventual growth for a Data Engineer into an ML or Scientist role.

So; do Data Engineers do Machine Learning? Not really. But if they’re tasked with something remotely similar, they should definitely keep their options open.

Career Prospects for Data and Machine Learning Engineers

Next up in this Data Engineer vs. Machine Learning engineer is a discussion of market share, career prospects, and salaries.

If we compare a Data engineer salary with a Machine Learning engineer salary; there’s a big different. If we were to quote the salary of a Data Engineer in United States, the annual average salary is $132,697. Whereas, a machine learning engineer salary accumulates to $151,373 per annum.

One thing you should consider here is that this Data Engineer salary is adjusted on averages from the entire US. The final salary you’ll be offered depends on your location, knowledge, certifications, and expertise. The better you perform, the better your salary will be. 

If we shift to the career prospects in Data Engineering vs. Machine Learning engineers; it’s a 10 to 1 ratio. Data Engineers are required in the industry to answer complex problems and the entire data processing framework requires expertise from several people. Though ML engineers are just as important and their tasks as complex, they’re generally lesser in numbers.

How to Become a Data Engineer?

Our final section in Data Engineering vs. Machine Learning focuses on ‘how to become a data engineer’. Although we’ve covered this sparsely before; let’s refocus on the KCE process of learning.

To share what the KCE process stands for; it is an abbreviation for:

  • Knowledge
  • Certify
  • Expertise

Knowledge is where you focus on studying thoroughly for the Data Engineering domain. A Data Engineer’s responsibilities require hours of learning and practical knowledge. We recommend signing up for local, in-person classes taught by renown trainers in the field of Engineering. Not only would you feel encouraged by your classmates, the one-to-one correspondence with a trainer is the ultimate experience.

Certifying oneself is quite important to become a Data Engineer. This helps you improve your knowledge and expertise, while also showcasing potential employers of your credentials. Most employers today do require a certification for entry level jobs – plan your certifications wisely.

Lastly, to gain Expertise in what you do – you have to solve data problems. The more complex the problem, the more you engage your thought process. This is where you gain true knowledge and will definitely help out. Do check out Kaggle or the Google Big Data public sets for getting hands-on.

Conclusion

To conclude our discussion on Data Engineering vs. Machine Learning; the two fields are quite similar yet distinct. Though their paths might cross in a large corporation, they’re generally tasked with two different things. If you love data processing and building infrastructure or databases, go for a Data Engineering role. If you love training models and developing thoughtful insights from data, a Machine Learning role might be best for you.