Data engineering and data science can sometimes sound like the same concept. Although both fields have similarities, they are not innately the same. Data engineers work on data infrastructures, while data scientists work on data solutions. If you are planning on pursuing a career as a data scientist or data engineer, you must know the key differences that set these two professions apart.
Data Engineering vs. Data Science
Data engineering refers to the construction of data pipelines. A data pipeline is a series of data processes that produce outputs at each step. Each output is then inputted into the next step until the data reaches the end of the pipeline. The end of the pipeline is sometimes called a sink. Here, the data is extracted and moved into structured databases, ready for analysis.
In short, data engineering creates the infrastructure of the data. This infrastructure is a series of pipelines that work to process data. Without this infrastructure, the data will remain raw and unprocessed. Data scientists will have difficulties in extracting meaningful conclusions from unprocessed data.
Data science extracts and converts processed datasets to form predictions, conclusions, and solutions. Data scientists get all the information they can from the datasets. After which, they work to draw impactful conclusions.
Data scientists can produce insights, strategic tools, data solutions for businesses, and more. These scientists predict trends and opportunities based on the datasets that they analyze. They can also apply these datasets for machine learning and artificial intelligence.
Some years ago, data scientists were taking on the role of data engineers as well. These data scientists would collect and process the data. Then, they would have to work on the extraction and analysis of the same datasets. However, as technology has advanced, so has data. Big Data is now used in many industries. These industries use big data to improve their business strategies and operational efficiency.
These advancements have led to the need to create exclusive roles for data engineers and data scientists. There are now larger volumes of data handled daily. This becomes more efficient with a data team composed of both data engineers and data scientists.
Roles and Responsibilities
The key difference in the roles of data engineers and data scientists lies in how they handle the data.
Data engineers work on the infrastructure of the data. They build systems, pipelines, and structures to collect and organize datasets. These engineers handle raw data by processing it into a structured form. Their responsibility includes creating data formats, securing, and storing the organized data.
Data engineers are also responsible for optimizing the data that they handle at every step. The goal of these professionals is to build and integrate data pipelines to make data analysis a smoother process.
Data scientists work on analyzing the data from the data infrastructure. They draw significant conclusions to produce various solutions to enhance business processes.
The goal of these scientists is to develop new insights from the datasets. These insights can contribute to the improvement of a business. Using the processed data from data engineers, data scientists apply statistical and analytical tools to draw conclusions. This information is used to make predictions, analyze trends, and suggest new business strategies for implementation.
Data scientists and data engineers need both technical and non-technical skills to excel at the job. The best data engineers have good knowledge of various programming languages such as SQL, Python, and Java. Data engineers with a background in software engineering may also have a slight advantage in the field.
Most businesses search for data engineers who have a degree in computer science or information technology. Data engineers with years of experience may also be considered even without the educational background required by the company.
Data scientists are tasked with analyzing big data. Knowledge of analytical tools and statistics is a must in the field. Additionally, data scientists must also have a basic knowledge of programming languages and machine learning algorithms. Sufficient knowledge about the business and industry is also an advantage so they can easily identify trends and recommend the best solutions.
To excel in any of these careers, you must also possess non-technical skills such as problem-solving, critical thinking, communication skills, collaboration, time management, and the ability to work independently without any supervision.
Data engineers usually begin as web developers, software engineers, solutions architects, or database developers. All these roles can enhance the skill set of a tech professional to pursue a career in data engineering. Because data infrastructures are often complex with several interlinking structures needed, data engineers need to have the skills and experience to get the job.
On the other hand, data scientists can begin as junior data scientists or data analysts. Data science offers entry-level positions to help scientists build their experience in handling data. Often, these entry-level roles focus on assisting in specific projects. Once you gain more experience as a data scientist, you can progress to analyzing your own datasets and providing solutions.
Both careers are high-paying jobs that can reach six-digit annual salaries. Aside from the pay, there are many career opportunities for both data scientists and data engineers. These professions are needed in the business sector, banking and finance, shipping industry, retail, stock trading, and other industries that rely on data to meet their business objectives.
The Bottom Line
Today, data scientists and data engineers are highly in-demand. Businesses continuously need to find solutions to remain competitive in the industry. Thus, both professions are needed to create realistic, data-based solutions that will help these businesses thrive.
Although there are several key differences between the two roles, data scientists and data engineers can work together to form an efficient and reliable data team. This data team is capable of processing and analyzing data from raw data to structured data to data solutions. With the advancement of data management and analysis, these two careers will be around for many years to come.
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