Data is engulfed all around us and has changed the way we viewed it. Many years back data was merely used to keep track of things as records but now data is a powerful weapon using which companies are collecting/generating data, storing data, analyzing data, and making data-driven decisions. A good look at the data trends can help an organization identify its underlying bad performing assets.
IDS estimated that by 2010, 1.2 trillion GigaBytes of new data would be generated and that it would become 35 trillion GigaBytes by 2020. We are now living in the Big Data era where there is a need for handling this exponentially growing data and making use of it. You can also read the comparison between ETL vs ELT tools
Data Engineer comes into the picture when data needs to be handled. In this post, you will learn about a Data Engineer, the roles and responsibilities that accompany it, the skill sets required to become one, and also about what the future holds for Data Engineers.
What is a Data Engineer?
The term Data Engineer commonly refers to an employee in an organization who collects useful data(structured and unstructured), stores it, maintains it, and creates data models aligned to the company’s goals and objectives. The Data Engineer should have strong programming skills and must have knowledge of the company’s data architectures. Now let’s talk about the roles and responsibilities of a Data Engineer in-depth.
What are the roles and responsibilities of a Data Engineer?
Various organizations define the roles of a Data Engineer depending on their needs. Some Data Engineers work on Databases/ Warehouses while some others work on maintaining, integrating data, and performing analysis. Let us generalize this and talk about the roles and responsibilities:
Proper Warehouse/ Database Functioning:
This starts with defining a proper Warehouse/ Database architecture for the company’s data. A Data Engineer should ensure that the Warehouse has enough storage space both for existing data and data that would get added in the future. Also, Data Integrity must be maintained throughout the whole process. The Data Engineer must make the data available in an analysis-ready format to Data Analysts who use BI tools on top of the in-house Data Warehouse. Also, all the queries running should be robust and fastly processed. There should also be a fault tolerance mechanism incorporated in the Data Warehouse in the case of failures.
Creating and Maintaining Data Pipelines:
Data Pipelines are used for moving data from one place to another. This poses many challenges. The data formats and attributes may be different in the source and destination. A Data Engineer should be very careful to load the data in their right formats and attributes. Since this must be performed on a daily basis, Data Engineers create Data Pipelines that move data from the desired source to the destination.
ETL/ ELT technologies come to the aid of Data Engineers as they automate the task of extracting, transforming, and loading the data. ETL stands for Extract Transform Load and ELT stands for Extract Load and Transform. Transformation of data into an analysis-ready format can be done before (ETL) or after loading data (ELT) into a destination Warehouse. Hevo Data is a modern No-code Data Pipeline that loads your data from multiple sources to a destination Warehouse of your choice.
Read ETL vs ELT: Best Use Cases article to understand more about it.
What are the skillsets required to become a Data Engineer?
A Data Engineer should have a Computer Science degree or should have standard certifications related to the field. It is essential to have Software Development and good back-end programming skills in SQL, Python, Java, etc. To have an edge with a high pay scale one must have strong skills in Scala, Apache Spark, Data Warehouse technologies, Data Modeling technologies, Hadoop, Cassandra, Amazon Web Services, ETL, and Big Data analytics.
What does the future look like for Data Engineers?
While most of the data we are currently working with is structured, the rise of Artificial Intelligence, Image processing, Machine Learning, and many more technologies are paving a path to analyzing unstructured data(images, documents, audio, video, etc). The Data Engineers in the future will have to be equipped with these modern technologies to deal with this complex data and have to keep up with the fast-paced technological developments to avoid going out-of-date.
Conclusion
Data Engineers are essential to any organization because data when properly maintained and analyzed can give you the solutions to your questions. In this article, you have briefly learned about Data Engineers and their roles and responsibilities.
