Why Data Engineering still matters
How Data Engineering builds the stable Foundation for Data Science
As with a pyramid, the lower foundations of a data analysis platform must be built first and be particularly stable, so that the following stages can also carry a load. Recently, I asked the question if new drag and drop tools “kill” Data Engineers by making them redundant (Read the article here). My own conclusion is that tasks are more likely to change, but it was well received and generated a lot of discussion (thanks again for the all the feedback). Here’s why Data Engineers play such an important role and why they are the cornerstone of a data-driven enterprise.
What is a Data Engineer?
The Data Engineer takes care of the provision of large amounts of data. They are responsible for data integration processes such as ETL and ELT jobs and must ensure that the data arrives in good quality on often modern cloud-based data platforms. They also often collaborate or take over the tasks in the area of data and cloud architectures. Here, they are often busy building Data Warehouses and Data Lakes. Read more about how to build up a data analytics teams and what roles do exist there here.
Why Data Engineering is Important
Data Engineers are becoming increasingly relevant for companies that want to maximize value from data. This is so because Data Engineers enable Data Scientists and other Analysts to create models for predictions by providing the data required for this and ultimately running the model for the predictions productively in the existing infrastructure.
From my own experience, I know that data processes must first run stable and with quality checks and monitoring, also a good data quality must be guaranteed. Only then do (self-service) business intelligence and data science make sense, otherwise the wrong data is used and reservations may arise and projects in this area are then quickly killed.
Data Engineers are in Demand
The only problem is that there is a lack of personnel with the appropriate expertise. As in other areas of IT, skilled workers are quite scarce. Drag and drop tools such as Alteryx and talend can help to some extent, but expertise is still needed here as well.
Therefore, it is not surprising that companies are paying Data Engineers a good amoun of money. Here are some interesting statistics about the earning potential of Data Engineers:
Summary
An often neglected aspect is that the successful operation of Data Science, Machine Learning and Business Intelligence also requires expertise from the field of Data Engineering. Data Engineers implement and monitor highly available data infrastructures and platforms. Data is collected from various sources, processed and made available to data analysts from a wide range of fields. The resulting results and data-driven products often have a great added value for companies.