Data engineering is an important field related to data, but few people can describe exactly what data engineers do.
Data drives businesses large and small. Businesses use data to provide answers to relevant questions, from consumer preferences to product viability.
Without a doubt, data is an important part of scaling your business and gaining valuable insights. And that makes data engineering important. In March 2016, about 6,500 LinkedIn users listed their title as “Data Engineer”. They offer a variety of skills, including a basic knowledge of Python, SQL, and Java.
But what is data engineering? And what do data engineers do? To find out, keep reading!
What is data engineering? Data engineering, sometimes called information engineering, is a software-based approach to information systems development.
Obviously, data engineering involves provisioning, transforming, and managing data from disparate systems.
This process ensures that the data is useful and accessible. Above all, data engineering emphasizes the practical applications of data collection and analysis. It is not surprising that the investigation of the above-mentioned investigations requires complex solutions. As a result, data engineering uses complex methods to collect and validate data, from data integration tools to artificial intelligence.
Similarly, data engineering relies on special mechanisms to apply found data to real-world situations, often by designing and monitoring sophisticated processing systems for this purpose.
Why is data engineering important?
Data engineering is important because it allows companies to optimize data towards availability. For example, data engineering plays an important role in the following activities:
- Find best practices for refining your software development lifecycle.
- Enhance information security and protect your business against cyber attacks.
- Enhance your understanding of business domain knowledge.
- Collect data in one place through data integration tools.
- Whether the sales team is processing sales data or analyzing the lead lifecycle, data is there at every step of the process. Over the years, technological innovation has had a tremendous impact on the vitality of data. These innovations include cloud technology, open source projects, and scale data growth.
- The last element particularly emphasizes the importance of technical skills when organizing huge amounts of data. Data must not only be complete, but also consistent, and this is the task that data engineers set themselves.
Data Science vs. Data engineering?
Although data in general covers a broad field, data engineering and data science are separate disciplines from software engineering.
One of the most important aspects of data engineering is big data optimization. Big data is a subset of data engineering and refers to the processes that take place to manage apparently large or complex data sets.
However, in 2017, technology research firm Gartner determined that between 60% and 85% of big data projects fail. This is largely due to unreliable data structures. Combined with the new digital transformation that many businesses in the modern era find inevitable, quality data engineering is more important than ever.
Unfortunately, the early days of big data management didn’t have a lot of data engineering ideas.
As a result, data science teams have taken on the role of data engineers today. But it doesn’t quite work. This is because data scientists are trained in exploratory data analysis, not much more.
The job of data scientists is to interpret data. Data scientists don’t understand well how to model data for interpretation in the first place.
On the other hand, they use mathematical, statistical, and even machine learning techniques to accurately evaluate analytic databases.
Data engineers ensure that this data is ready for data science teams from the start. To achieve this, data engineers must evaluate the quality of the data.
