Thought Leadership
It is no longer a secret that big data can have a significant impact on the environment. Data helps to streamline processes, make environmental policy decisions and develop new, more sustainable ways of working.
For every gigabyte stored in the cloud, around seven kilowatts of energy are consumed per hour. This means that if a company works with ten terabytes of data, it creates a carbon footprint of 500 kg of CO2. In view of these developments, the concept of “green coding” is increasingly coming to the fore: an approach that prioritizes efficiency and sustainability in software development.
Batch vs. real-time streaming
It is often still believed that conventional batch processing is more efficient than data streaming – after all, processing at scheduled intervals requires less computing power than constant streaming of data. In reality, the opposite is the case: even if batch processing does not have to be operated continuously, intensive peak performance is required, which means that it consumes significantly more energy than a continuous low-level stream. In terms of resource consumption, the result of batch processing is comparable to a sudden tidal wave, while data streaming is a gently flowing stream.
Data streaming not only benefits from lower CPU utilization, but also improves the processing prediction in general. One example is Apache Flink, an open source framework for stream processing. Users can use Flink Actions (the operations applied to data streams when using Apache Flink) not only to process the data in real time, but also to analyze it. These analyses help companies to develop a clear and reliable understanding of their usage without having to accept sudden, unexpected increases. By reducing this need for unused cloud contingents, companies can increase their efficiency, reduce costs and ultimately develop more sustainable processing.
A serverless approach
Frameworks such as Apache Flink, which are managed via a data streaming platform, have the additional advantage of being serverless. In a serverless environment, computing resources can be scaled automatically as required. This means that infrastructures can adapt in real time to efficiently manage fluctuating data volumes without human intervention.
Dies führt nicht nur zu einer schnelleren, effizienteren und kostengünstigeren Datenverarbeitung, sondern entspricht auch den Grundsätzen des Green Coding, indem ungenutzte Rechenressourcen minimiert werden. Die Infrastruktur wird während einer aktiven Verarbeitung verwendet, wodurch die Energieverschwendung begrenzt wird.
This approach encourages developers to focus on writing efficient, event-driven functions without having to consider the bottlenecks associated with batch processing. This shift in focus can ultimately help to promote sustainability throughout the software development lifecycle.
Outlook
Die Umstellung auf grüne Energie ist ein wesentlicher Bestandteil nachhaltiger Computing-Praktiken. Cloud-Anbieter erkennen heute die Bedeutung von umweltfreundlichen Maßnahmen immer mehr an. Unter anderem nutzen viele führende Anbieter von serverlosen Services bereits erneuerbare Energiequellen für den Betrieb ihrer Rechenzentren, was den CO2-Fußabdruck von Apache Fink und ähnlichen Serverless-Computing-Frameworks weiter reduziert.
For companies in data-intensive industries, the combination of data streaming and a green cloud provider is a big step towards a more sustainable, data-driven future. Sustainability is not only right from a moral point of view – it is also marketable, energy-efficient and cost-saving.
