Data Engineer V/S Data Scientist: How Are They Different?

In the modern world, there is a massive amount of data. Tech professionals and scientists collect and analyze this data on a large scale. It contributes to innovative technology and fresh business practices. Two professions that handle data in all forms are data engineers and data scientists.

Data Engineers and Data Scientists are both in-demand professions today.

These professions can extract, organize, and analyze data. These two careers are similar but have distinct roles in an organization.

What is data engineering?

Data engineering involves building data models and pipelines. A data pipeline consists of several steps. Engineers input data in each step to generate an output. Each output is then inputted again in the next step, and so on. This process continues until the data reaches the end of the pipeline. Once the data is at the end, the engineers extract and transfer the data to databases for analysis.

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This field of engineering deals with the infrastructure of data. Building these infrastructures is the primary role of data engineers in any organization.

The field of data engineering also focuses on maintaining the data infrastructure. Data engineers oversee an ETL model. This model extracts, transforms and loads data to the pipeline. Once the data is in the pipeline, engineers organize it into various structures. This organization makes the data easier to analyze.

The data infrastructure that engineers build is what allows data to proceed to the next step. This next step includes data processing and analysis. Data scientists use the organized data from a pipeline to develop new insights.

What is data science?

Data science extracts meaningful conclusions from various datasets.

Data scientists use scientific methods and analytical techniques to analyze the data. These methods include statistical analysis, mathematics, and programming skills. Data science can use either unstructured data or structured data to form conclusions.

In most businesses, data scientists work with data engineers to process raw data. Data engineers will first load the raw data into an infrastructure or data pipeline. Once the data is at the end of the pipeline, data scientists can extract its structured form. Then, these scientists will work to analyze the data to form predictions. Data scientists use these predictions to recommend strategic business solutions.

Data science is an important field in any business. It can produce valuable insights that will aid in business growth and development. Data science can also identify patterns to predict future trends.

Data science also has applications in artificial intelligence and machine learning.

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Differences between Data Engineers and Data Scientists

Below are the main differences between these two professions in three key areas. These key areas include skills, responsibilities, and career opportunities.

Skills and Knowledge

Among the main differences between the two are the knowledge and skills required for the job. Both professions need a good knowledge of programming languages. These languages include Python, Java, and SQL. But data engineers need to have more advanced knowledge in programming. This is because data pipelines are complex infrastructures that work with a more complex code.

Data engineers must be able to handle the complexities that come with big data.

This will allow them to produce an efficient data pipeline. Some companies only hire data engineers who have a background in software engineering.

In contrast, data scientists also need to know these programming languages to speed up data analysis. But, a basic knowledge of programming would be enough for basic analysis. This is because data science has a different foundation. Data science uses statistical analysis and mathematical formulations. But, data scientists who proceed to machine learning must have a good grasp of Python, at the least.

Apart from these technical skills, both data scientists and engineers must have soft skills. These skills will allow them to work with a team and excel in the workplace. These skills include communication, collaboration, problem-solving, and critical thinking.

Responsibilities

Data engineers and data scientists have specific roles in data handling. Data engineers are responsible for the data infrastructure. They build data pipelines, transform data, and organize data into various formats. They are also responsible for ensuring the secure storage of data. All these responsibilities are done to prepare the data for analysis.

Data scientists are responsible for analyzing the data and drawing meaningful conclusions from each dataset. These scientists apply statistical tools to create useful insights. These insights are then applied to predict future trends. They are also used to develop new products and solutions for various industries.

Career Paths

Data scientists and data engineers have different career paths even if both end up working together on a single data team.

Both professions have high-paying opportunities. You can expect to receive a six-digit salary by working as either a data engineer or data scientist.

Data Engineers

  • Need previous experience before taking a role in data engineering
  • Begin their career as a Web Developer, Software Engineer, or Database Developer
  • May need a degree in Computer Science

Data Scientists

  • Entry-level opportunities
  • Can begin as a Junior Data Scientist or Data Analyst
  • Can progress to Senior Data Scientist or Project Manager

Conclusion

With the advancement of technology, data handling has become more specialized. A few years back, data processing would take a long time as a single person would do the job of both data engineers and data scientists. This has led to confusion that data engineers and data scientists are the same.

Although these two professions work together, they also handle unique areas of the data handling process. Arguably, a data engineer can process data without a data scientist and vice versa. However, as industries continue to churn out data in large amounts, the separation of these two roles makes data processing more efficient.

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