From Raw Data to Valuable Insights: The Power of the Data Engineer

The Data Engineer: a versatile, multidisciplinary specialist who falls between the infrastructure specialist and the analyst or data scientist. You could call them a "flow engineer": they ensure the smooth flow of data with a keen eye on quality and integrity. At the same time, they transform data to achieve maximum value, similar to polishing rough diamonds until they fulfil the valuable promise they hold within them.

Data engineering is a term that is becoming increasingly important due to the growing availability of vast amounts of data. In this regard, big data and cloud computing have played a significant role in the evolution of data engineering. The advent of big data technologies, such as Hadoop and Spark, gave data engineers new tools to manage and process large amounts of data. And with the transition to cloud-based solutions, data engineering has become more accessible and scalable, enabling companies to easily process and analyse massive amounts of data.

The term “Data Engineer” has evolved over time to reflect the changing landscapes of data management and processing. Today, it’s an essential profession that plays a vital role in helping organisations leverage data to innovate, improve business processes and enhance the customer experience.

To paint a picture of the enormous amount of data on the internet, let's focus on one specific aspect: video content. An estimated 500 hours of videos are uploaded to platforms like YouTube every minute. This includes not only amateur videos, but also professional content, films, documentaries, tutorials and much more. It’s impossible to watch all the videos uploaded in a single day, even if you devoted an entire lifetime to it. It’s astonishing how quickly and constantly this amount of data grows and changes on the internet.

And that’s just one type of data on the internet. There are also billions of web pages, millions of mobile apps, massive amounts of social media updates, e-mails, files, images and sensor data generated and exchanged daily.

The role of Data Engineers is becoming crucial for organisations to manage, organise and process these huge amounts of data. They create the infrastructure, develop pipelines and optimise processes to ensure that data can be used and analysed effectively. They are the key to unlocking the hidden value in this abundance of information and enabling ground-breaking insights and innovations.

One of the significant trends, where data engineering is indispensable, is the emergence of generative AI models that have now been discovered by many people. ChatGPT has been trained using an immense dataset, we can tell you more about that in this blog. The biggest investment in training these Generative Pre-trained Transformer (GPT) models lies in collecting, cleaning and processing a large amount of data sources. At Harborn, we gratefully use these AI models and gladly deploy them for our clients. An example of this is that we continue to train the models on a customer-specific dataset: the data engineer plays a crucial role in this process.

Another development is the increasing focus on Cloud Financial Management, also known as FinOps. FinOps is all about cost optimisation for the use of cloud systems. Data also plays a crucial role here. Setting up a data platform efficiently can save costs, for instance, by avoiding unnecessary expenses such as choosing an expensive storage type. Additionally, analysing the data itself can lead to optimisation, by gaining deeper insights into business processes.

In short, the Data Engineer plays an indispensable role in managing and unlocking the amount of data available on the internet, helping to drive progress and growth in various sectors and disciplines. And as the amount of data continues to grow exponentially, we at Harborn see a promising future for data engineers.