Data engineering mainly consists of organising and transforming data so that it can be used effectively. This typically involves extracting data from various sources, cleaning and organising it, and then storing it in a structured format that is easy to access and analyse. In other words, tasks such as creating and maintaining databases and data pipelines, designing data models, and building and maintaining data warehouses. Data engineers are responsible for designing and building the systems and infrastructure that are needed to support these processes, and they often work closely with data scientists and other professionals to ensure that the data is accurate and useful. A practical example would be a system that collects aircraft data in real time from different sources and aggregates it for a final user. This seems somewhat easy, but processing all this data and presenting it in less than 5 seconds can be a complex problem.
Data engineering is a key enabler of the modern aviation industry as it underpins the process of collecting and analysing the vast amounts of data generated by the many complex systems involved in air travel. These systems cover aircraft sensors to weather stations and traffic control systems. This data is used to improve a wide range of aviation-related processes, including flight planning, maintenance, and safety, supporting the end-to-end operation of flights.
One important use case of data engineering in aviation is in flight planning. By analysing data on weather, air traffic, and the performance of individual aircraft, data engineers can help airlines create more efficient and safe flight plans. This can save time and fuel and reduce the risk of delays and cancellations. Data engineering is also essential for aircraft maintenance. By analysing data from sensors and other systems on an aircraft, data engineers can help to identify potential problems before they become serious. This can help airlines prevent costly and dangerous failures, and to keep aircraft in top condition. In terms of safety, data engineers can use data to help identify potential hazards and risks, and to develop strategies to mitigate them. This can include everything from identifying dangerous weather patterns and potential collision risks, to analysing the performance of aircraft systems and identifying potential failures.
While both fields involve working with data to extract insights and improve decision-making, there are some key differences between the two.
As already mentioned, data engineering is focused on the collection, storage, and processing of large amounts of data in a consistent way so that it can be easily used by other components or people. This typically involves building and maintaining complex systems that can handle the vast amounts of data generated by the many processes involved in air travel, such as aircraft sensors, weather stations, and traffic control systems.
On the other hand, data science is focused on the analysis and interpretation of data to extract insights and support decision-making. This work consists of using statistical and machine learning techniques to identify patterns, trends, and relationships in data, and to make predictions about future events. Data scientists may also use data visualisation and other techniques to communicate their findings and to support the development of new products, services, and processes. One key difference between data engineering and data science is the scale of the data they work with. Data engineering typically involves working with very large amounts of data, often in the form of streams or streams of events, while data science typically involves working with smaller, more structured datasets. This means that data engineers need to have a strong understanding of distributed systems and parallel processing, while data scientists need to have a deep understanding of statistics and machine learning.
All this means that data engineers often work closely with software engineers, database administrators, and other IT professionals to build and maintain the systems that support data analysis, while data scientists often work with business analysts, product managers, and other decision-makers to use data to inform their decisions.
In general, data engineering is more focused on the technical aspects of storing and processing data, while data science is more focused on the analytical and modeling aspects of working with data. However, there is often overlap between the two fields, and many data professionals have skills and expertise in both data engineering and data science. Overall, the role of data engineering in the aviation industry is crucial in supporting the analysis and use of data to inform decision-making and improve operations. This work is essential in enabling data scientists and analysts to extract valuable insights from data and make informed decisions.