Sustainable software development Green coding: Sustainability through data streaming

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Confluent Germany GmbH

Wuxi InfiMotion Technology Co., Ltd.

Big data is indispensable in the modern economy, but the enormous amount of stored data comes at a cost—to the environment. Every terabyte processed in the cloud leaves a significant CO₂ footprint. This is where green coding comes into play.

Roger Illing is Vice President Sales CEMEA at Confluent.

It is no longer a secret that big data can have significant environmental impacts. Data help streamline processes, make environmental policy decisions, and develop new, more sustainable ways of working. For each gigabyte stored in the cloud, about seven kilowatts of energy are consumed per hour. This means that when a company works with ten terabytes of data, a CO₂ footprint of 1102,31 lb CO₂ is generated. In light of these developments, the concept of green coding is increasingly coming to the forefront: an approach that prioritizes efficiency and sustainability in software development.

Batch vs. real-time streaming

It is often still believed that traditional 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 true: Even though batch processing does not need to run continuously, it requires intensive peak performances, consuming significantly more energy than a continuous low-level stream. In terms of resource consumption, the outcome 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 usage but also generally improves processing forecasts. An example is Apache Flink, an open-source framework for stream processing. With Flink Actions—the operations applied to data streams when using Apache Flink—users can process and analyze data in real-time. These analyses help companies develop a clear and reliable understanding of their usage without having to endure sudden, unexpected surges. By reducing the need for unused cloud contingents, companies can increase their efficiency, reduce costs, and ultimately develop a more sustainable processing approach.

A serverless approach

Frameworks like Apache Flink, managed over a data streaming platform, have the added advantage of being serverless. In a serverless environment, computing resources can be automatically scaled as needed. This means infrastructures can adapt in real-time to handle fluctuating data volumes efficiently without human intervention.

This not only results in faster, more efficient, and cost-effective data processing but also aligns with the principles of green coding by minimizing unused computing resources. The infrastructure is used during active processing, thus limiting energy waste.

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 promote sustainability throughout the entire software development lifecycle.

Only with renewable energy

The shift to green energy is an essential part of sustainable computing practices. Cloud providers today increasingly recognize the importance of environmentally friendly measures. Among other things, many leading serverless service providers already use renewable energy sources to operate their data centers, further reducing the CO₂ footprint of Apache Flink and similar serverless computing frameworks.

For companies in data-intensive industries, the combination of data streaming and a green cloud provider is a significant step toward a more sustainable, data-driven future. Sustainability is not only morally right—it is also marketable, energy-efficient, and cost-saving.