Adopting data science is always a huge step for every company. Thanks to data science solutions, you can make the most of data you either way process in your organization. This results in saving costs and time and making more accurate business decisions. However, in order to use data science fully, you also need data engineering. Why? What’s the role of data engineering in business? What are typical data engineering use cases? And what data engineering solutions should you be particularly interested in? Let’s find out!
Today, data engineering services come in handy primarily when your company decides to analyze data more thoroughly. As you know from our blog post about data engineers, data engineering is this part of data science that’s responsible for dealing with all the technical elements and issues. Data engineering teams are responsible for the design, construction, maintenance, extension, and frequently the entire infrastructure that supports data in the company. Therefore, their role is indispensable. You could even say they lay the foundation for big data analytics in your organization.
Let’s be more specific, though. In this article, we are going to show you what data engineers actually do and why data engineering in business is so important these days.
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The role of a data engineer
As you already know, their role is to make everything data-related fully functional. However, we can divide their role into three primary areas:
- Extracting data
- Storing data
- Transforming data
Now, the first part is essential. If you’ve never used big data in your company, your datasets and data sources are probably disorganized and maybe even messy. Before you can make any use of them, the data needs to be organized, cleaned and extracted. At this point, the vital question would be, from where and where to? Shortly put, from your current datasets (i.e., Excel files, PDF files, DOC files, maybe CRM system as well, etc.) to a data platform.
Data platform, in essence, is your technological infrastructure that supports data science tasks. Once the data is extracted, it needs to be stored somewhere in an organized manner. In most instances, companies decide to store their cleaned and organized data in a data warehouse. Sometimes you might also want to use a data lake. The differences between these two forms of data storage are a history for another time.
Suppose your data is already extracted and stored. Now, it’s time to transform it so that it can be used without any complications in your data science or AI-related project. Transforming data consists of structuring and formatting datasets so that they are fully usable for future processing and analysis.
All your data engineering solutions are based on these three crucial elements. It’s the backbone of every data engineer’s work. Now, let’s talk some more about data engineering use cases. In what situations do data engineering solutions come in handy?
Data engineering use cases and solutions
For starters, you need data engineering at the beginning of data analytics processes because this field is responsible for designing data platforms. The data engineer will help you design and create a specific data platform that will fully match your company’s requirements and needs. Secondly, data engineers design and develop all the other data-related tools and instruments. Such means can comprise business intelligence and data visualization software.
Once all your data infrastructure is done, data engineers will help you keep it updated and properly maintained. Data engineers should take care of the maintenance of such elements as:
- Data pipeline
- Data warehouse/data lake
- Data-related applications and algorithms
They also frequently take part in all the testing procedures, especially when it comes to monitoring your data platform’s performance. Furthermore, data engineers are usually responsible for managing datasets, data sources, and data storage solutions. This way, they have supervision over everything related to data analytics in your company. That’s why they frequently work with metadata, too (metadata, in essence, is data that describes other data and datasets).
Your task is to listen to your data engineers of choice and see what the best solution would be for you and your company. It’s important to learn more about unified storage if you’re planning on adding it to the system, and this goes out for any other storage solutions you may want to add as well. There are a variety of features and options to choose from, but with the help of your data engineers, you can make an informed decision that works best for your organization.
And here’s yet another crucial data science use case: Machine learning algorithms. In this field, data engineers are necessary as well! They work closely with data scientists and ML specialists to build these algorithms and deploy them into the production environment.
Earlier in this article, we mentioned data-related tools and instruments. These tools usually consist of data visualization tools that help present data legibly and attractively. Some of the most popular data visualization tools are Tableau, Microsoft PowerBI, Sisense, and Qlik. And finally, if some non-technical employees in your company need access to data engineering solutions, data engineers provide solutions that enable access to data safely and efficiently.
As you can see, although data engineers are rarely at the front, their role is actually very important! These data guardians make sure your data platform and data-related solutions work flawlessly. If you are interested in implementing data engineering in your company – just drop us a line!