What is the difference between Data Engineers and Data Scientists?
Data Science is an emerging technology in this 21st century. Data Science involves all the processes which bring data into actionable insights. These processes include getting data, cleaning data, performing descriptive statistics,and building a model on data, implementing the insights to solve the given problem. Data Engineering is an aspect of Data Science that mainly focuses on how data is processed and analyzed practically.
There are several ways to define what a Data Scientist is. Let’s look at one of those definitions. A Data Scientist is a person who has a broad range of knowledge in multiple disciplines while specialized in one or two. He or She understands the domain knowledge of a particular field. For example, a Data Scientist needs to understand the business processes of a company, including marketing, strategy, and sales before doing any data science. A data scientist should have a good knowledge of Machine Learning and Statistics.
In contrast to a Data Scientist, a Data Engineer is a person who develops, constructs, tests, and maintains architectures, which are very useful for every step of the data science project lifecycle. For example, a Data Engineer practically develops, constructs, tests, and maintains databases and large-scale systems to store and manage data that are useful for Data Scientists.
Although Data Engineers and Data Scientists work together to get insights from large-scale data, their skills may vary, and the two positions are becoming more and more distinct. This article mainly focuses on those distinctions in detail between the two positions. Without further delay, welcome to “Data Engineers vs. Data Scientists”.
What do Data Engineers and Data Scientists do?
A Data Scientist performs actions on each step of the following Data Science project lifecycle.
At the start, a Data Scientist understands the business process. This understanding is often called getting domain knowledge. Then he or she defines the problem and sets up a goal and objectives to solve it.
Then, a Data Scientist acquires the data, performs some fundamental data analysis, and some visualizations to understand the nature of the data. If the data is not in the correct shape, he or she applies standard techniques such as data cleaning, dimensionality reduction, feature engineering to get data into shape.
After that, a Data Scientist decides which model gives the best output for the problem. In some situations, a traditional approach will not be able to solve the problem. Therefore, he or she needs to apply data-driven machine learning techniques for the modeling task.
Then, a Data Scientist will validate the model with assumptions and implement the model with the given problem.
Then, he or she sends the findings to the customer. Customer acceptance is required.
Finally, he or she writes a report about the entire process. This report should be clear and straightforward enough to understand the whole process and findings for a non-technical person.
A Data Scientist uses R or Python for data analysis.
Note that the data analysis process is not a one-time process. It often needs to go back to the previous steps if there is an error or change occurred.
A Data Engineer, contrary to a Data Scientist, performs the following tasks:
A Data Engineer deals with raw data that contains human or machine errors. Data Scientists cannot directly use raw data with those errors.
A Data Engineer discovers various opportunities to acquire relevant data. Data is everywhere, but a Data Engineer should use different techniques to collect data from various sources, for example, web data, sales records, etc.
A Data Engineer employes a variety of languages and tools (SQL, Hadoop) to process data and connects databases between different platforms. This process provides an opportunity for data scientists to do parallel computing, which speeds up data analysis tasks.
A Data Engineer recommends ways to improve data reliability, efficiency, and quality. Those data engineered by data engineers will give accurate results if the data scientists apply the correct techniques in their analysis.
What do Data Engineer and Data Scientists NOT do?
Data Scientist and Data Engineers do NOT do the following things.
A Data Scientist does NOT do a data analysis task without defining the problem clearly: ‘A problem well defined is a problem half solved’. A Data Scientist often finds the domain knowledge to define the problem well and set up the goal.
A Data Scientist does NOT work alone even if he or she has all the necessary skills to complete a project: Data Scientists often prefer teamwork. Other team members are specialized in different fields. So, Data Scientists usually prefer to take advantage of those specializations.
A Data Engineer does NOT focus on the mathematical part of the model. Instead, he or she considers the efficiency, reliability of the model by improving the quality of the data.
A Data Scientist need NOT understand the inner working of a machine learning algorithm in terms of mathematics. Instead, a Data Scientist should know how to use which algorithm correctly for the given problem.
Data Scientists and Data Engineers do NOT do full work at once. Instead, they prefer to breakdown the big tasks into small pieces and make a Work Breakdown Structure (WBS). ‘You can not eat an apple at once, but you can eat the entire apple if you cut it into small pieces!’ The same applies to the data science process.
Data Engineers and Data Scientists do NOT work separately. They should work together to deliver high-quality results for a real-life data science project.
How do I know when I need a Data Engineer or a Data Scientist?
This question is a difficult one to give an exact answer since this depends on various factors. But I can provide some helpful facts to study for yourself and make the decision by yourself.
A Data Scientist is an expert in the following specified fields:
Python or R Programming
In contrast to Data Scientist, a Data Engineer is an expert in the following specified fields and technologies:
The question of 'how do I know when I need a Data Engineer or a Data Scientist?' should be answered from the perspective of the current company status. Suppose that you have a company that has clean, structured data in the company databases. If so, you can consider going straight to a data scientist with the above skills. The data scientist can do the rest of the work.
But what about if you have unstructured, messy data with missing values? What about if you need data from other external sources such as the web data? You should first hire a Data Engineer to get your data into shape and collect other necessary data. The Data Engineer will handle your unstructured data, which otherwise can't be done in a traditional way like using SQL because the data is unstructured. The Data Engineer will use different technologies such as Hadoop, NoSQL, to handle unstructured data. The Data Engineer will also do web scrapping to get the necessary data from the web. After that, you can consider hiring a data scientist to find the hidden insights in your data.