In an era of big data, aerospace data analysis helps the aviation industry by improving data assets and advancing new lines of innovation. These advances lead to the generation of more data, which also enriches the system. For this reason, more computing and more processing speed are needed each time.
For example, capturing real-time data with data provided by aircraft, on a flight or on the ground, can benefit air traffic controllers, airlines and aircraft manufacturers and optimise their efficiency, improving operations, aircraft production, timely maintenance planning and more. This capture of big data generates a need to store and/or massively process data, which in turn becomes a problem to be solved. This idea of “Big Data” already spans a wide range of fields and represents a new generation of technologies focused on extracting value from data.
The definition of Big Data is information that comes from a great variety of sources and formats, which arrives in increasing volumes and higher speeds. Three “V” properties define it: Volume, Velocity and Variety. All these properties are not new for datasets in Aviation, and we covered those previously here: . But let’s look at them in detail:
- Volume: Volume refers to the amount of data that can be collected or processed. In Aviation both historical and streaming data can scale up, especially if you want to cover all of Europe or the world.
- Velocity: Measures how fast the data arrives. Some data will arrive in real time, while others will be sent in batches. In Aviation, it is very common to have streaming data at high sampling rates of up to 1 second; e.g. Flight Data Monitoring or ADS-B position data.
- Variety: In aviation, data comes from different sources and systems with very different characteristics (e.g. legacy systems and APIs). This means there is a wide diversity range in their form and typology.
The aviation industry has been struggling to keep up with the increasing production of resources from a variety of sources, including aircraft sensors, air traffic control systems, weather data, and more. To meet this demand, the aviation industry has been turning to big data solutions to help them keep track of flights and operations. One such solution is Apache Spark, a big data platform that helps airlines process and analyses large amounts of data. Apache Spark has already been used by some air businesses to great success.
Apache Spark: The Solution to the Big Data problem
Apache Spark is a unified engine and analysis framework that provides large-scale processing by distributing data across multiple computers. These properties are key to big data and machine learning areas, which require a great deal of computing power to achieve large data warehouses.
In addition, the Apache Spark community is made up of about 600 contributors, making it the most active project of the entire Apache Software Foundation, a large governing body of open-source software in terms of several employees. Its use is increasing and it is adding features, tools, libraries and functionalities every day.
After seeing the advantages of this new tool for data processing and analysis, there are 3 more reasons why Apache Spark ultimately provides the best solution for large-scale processing:
- Apache Spark is scalable and provides great performance for transmission, data processing, and query optimiser designed to speed up processing and guarantee performance. Apache Spark produces results quickly and efficiently.
- Flexibility: Supports applications in the cloud. The general nature of the platforms and the sets of tools that can be integrated into them.
- Easy and inclusive: Developers can choose to develop in a variety of languages to combine approaches and applications to include SQL fundamentals, parsing, and streaming capabilities. It supports machine learning, streaming, data frames, and graphs.
The computationally intensive task of processing large volumes of real-time or archived data can be solved by Apache Spark. This tool integrates complex capabilities such as machine learning and graph algorithms. This piece will be indispensable in the future to able to cover the growing needs of the systems. Some common use cases in Aviation for Big Data are:
- Predictive aircraft maintenance.
- Cost Index descriptive analytics.
- Traffic complexity forecasting.
- Flight plans optimisations.
