The key to comprehending data engineering is in the word “engineering.” Engineers create and construct things. “Data” engineers develop and create pipelines that modify and transmit data into a format that is highly useable by the time it reaches Data Scientists or other end users. These pipelines must collect data from several sources and consolidate it into a single warehouse that represents the data consistently as a single source of truth.
This profession appears to be straightforward, yet it requires a high level of data literacy. This is why Data Engineers are in such short supply and why the profession is so misunderstood. Simply put, data engineers use data engineering solutions to make data move across the organization.
Most businesses have undergone a digital transition during the previous decade. This has resulted in inconceivable numbers of new sorts of data, as well as far more complex data at a higher frequency. While it was previously obvious that Data Scientists were required to make sense of it all, it was less obvious that someone was required to organize and maintain the quality, security, and availability of this data in order for the Data Scientists to do their jobs.
As a result, in the early days of big data analytics, Data Scientists were frequently required to develop the necessary infrastructure and data pipelines. This was not always in their skill sets or employment requirements. As a result, data modelling would be incorrectly completed. There would be duplication of effort and inconsistencies in data utilization among Data Scientists.
These types of challenges stopped businesses from getting the most out of their data initiatives, and as a result, they failed. It also resulted in a high rate of Data Scientist turnover, which persists to this day.
With the tsunami of completed corporate digital transformations, the Internet of Things, and the rush to become AI-driven, it is evident that firms require a large number of Data Engineers to lay the groundwork for successful data science programs. Data engineering solutions are required to weave the data fabric of any organization.
This is why the position of Data Engineers will continue to increase in relevance and scope. Companies require teams of employees whose main concentration is to process data in order to extract value from it. Data engineering solutions will be customized for different organizational needs to make data flow unequivocally.