Data engineering is the area responsible for processing and transforming a company’s raw data, which is the first level of a series of data processing actions and aims to define a practical use of the large amounts of information available to the company.
In an illustrative way, we can think of data engineering as a great umbrella which encompasses all the processes of collecting, storing, organizing, and providing data in an institution. This data, when received by engineering teams, is in a raw mode, and professionals in the field will work on transforming this data so that it achieves an already stipulated objective.
What is big data?
Big Data refers to a huge universe of data volume, its collection, organization, analysis, and interpretation. This concept also encompasses issues such as the speed with which these data are analyzed, their availability, and the relationship between the analyzed data and other available data.
The main objective of storing and analyzing these data, with regard to their use by companies, concerns the potential they have to support the decisions taken by institutions on concrete facts and base their choices in the best possible way.
What are the main benefits that data engineering can bring to my company?
The benefits brought by well-executed data engineering are numerous, but in this text, we will highlight the 3 main ones, which are the most frequently cited by companies that have joined the use of data engineering in their businesses. They include:
- High data availability – With the use of data engineering, access, exploration, and interaction of these data are carried out in a simple and fast way, allowing them to be used for different forms of analysis and the most varied types of insights.
- Integration across multiple sources – With well-organized and articulated data engineering processes, your company can access data from multiple sources, which can be organized and combined at any scale.
- Data quality and reliability – There is no point in having an immense amount of data if your company does not know how to analyze and use it in the best way. With the data engineering processes, the Data collected will be refined and reliable, always following the best security and privacy practices and constantly attentive to legislation and changes in the area.
What is data science? And what is the difference between data science and data engineering?
Data Science is an area of technology that uses several tools to extract information from raw data and, thus, generate relevant and necessary knowledge for a particular company or business.
Professionals who work and perform Data Science are called data scientists, and one of their main functions is to combine different technical knowledge to perform the analysis of data collected from various sources, making these data communicate with each other and be analyzed and transformed into valuable information for companies.
To carry out this work, data scientists use programming and statistics knowledge, as well as artificial intelligence and market intelligence techniques.
The main objective of data science is to develop work that can qualify decision-making from the data collected and analyzed.
Are Data Engineering and Data Science the Same Thing? The answer to that question is: No!
Despite being similar and complementary areas, they are not the same thing. For the data scientist to be able to perform data analysis, he needs that before the data engineer has performed his work of collecting, organizing, and storing, so that later, the data scientist can treat them.
In this way, the work developed by data scientists is closer to the end-activity, as they will be the ones who will give meaning to a large amount of information captured and, thus, can generate valuable insights for the company in which he works or provides services.
Despite working in different fields and different ways, data scientists and engineers should work collaboratively and be in tune with the means used and objectives to be achieved.