Why Data Engineering Knowledge Is Becoming a Must for Data Scientists

Machine learning requires that data scientists and engineers work together to create the models that go into machine learning programs. However, data scientists aren’t well known for being experienced at engineering data. That has come to be a fundamental flaw in the workflow of machine learning. This is because the data scientist is responsible for creating the models that go into machine learning programs, but they don’t understand how to wrangle the data as efficiently as a data engineer. 

On top of that, they use different tools that make working together with the data engineer even more difficult. It is for this reason why data scientists should know at least some data engineering. It is similar to software engineers learning at least basic DevOps to deploy their programs to production without any assistance. Since data scientists don’t know data engineering that well, it can impact their performance on the job.

They Get New Tools and Perspectives

The first thing that will happen when data scientists understand data engineering is that they will get a new perspective on how their models get into production. It will fundamentally change their workflow, as they will start to understand some of the practicalities needed to get a model working in an actual program. This will enable data scientists to understand what they can do to create workflows that are easy to implement. Think of it as design for manufacturing. When design engineers work as a machinist, they get an extra understanding on how designs are fabricated. It then helps them understand how to create better designs that are easy to manufacture. It is the same thing with data scientists and engineers. The data scientists will understand the intricacies of implementing their model. They would then be able to create workflows that are easier and cheaper to implement.

They Can Move Towards Having the Same Toolkits

Another major issue that affects machine learning implementations is that data scientists and engineers use completely different toolkits. When the data scientist gets more experience in engineering, they will better understand the tools engineers use. They will also be able to use tools that are closer to what the engineer has. It would then make the process of implementing and maintaining their production application easier. It usually means that the costs for the project will be exponentially cheaper. It might also mean the data scientists can take on a heavier load in the implementation process. It then makes the engineer’s job easier, and it also means the project can move along more swiftly.

MLOps Will Be Improved

The biggest thing about MLOps is how fragmented it is. Data scientists use completely different tools than engineers, making it difficult to create a streamlined pipeline from model creation to implementation. If the data scientist understands data engineering, they will then be able to modify their workflow, and it will make the machine learning operations process easier. Ultimately, it will become crucial for everyone to learn the various aspects of each other’s job. The data engineer will have to learn more science, and the data scientist will have to learn more data engineering. It is one of the major stumbling blocks in the industry right now.