Which is better: Data engineer vs Data scientist?

A data engineer is frequently confused with a data scientist. We asked an expert, a data scientist with over ten years of experience, to remark on the differences between these two roles:

  • According to him, Both data scientists and data engineers work with data, but they do so in very different ways, using distinct talents and technologies.

  • To create and maintain enormous data storage, data engineers employ engineering skills such as programming languages, ETL methodologies, and understanding various data warehouses and database languages.

  • Data scientists clean and analyze data, get essential insights, build forecasting and predictive analytics models, and primarily use arithmetic and algorithmic abilities. Data scientists use machine learning techniques and tools.

  • He stressed that data scientists might find it challenging to access data for various reasons. Extra work and specific engineering solutions are required to access and handle large amounts of data in a reasonable length of time.

  • Data is frequently kept in a variety of locations and forms. It is best to start by cleaning it up with dataset preparation steps, then transform, merge, and move it to a more structured storage system, such as a data warehouse. This is usually the responsibility of data architects and engineers.

  • Accessing data storage is done through a variety of APIs. In this instance, data scientists will need data engineers to set up the most efficient and reliable data collection pipeline possible.