When you work with cloud platforms, understanding how networks are structured is crucial for designing fast, reliable, and cost-effective data science solutions. Cloud providers organize their infrastructure into regions and availability zones. A region is a specific geographic area, such as us-east-1 or europe-west2, that typically contains several isolated locations known as availability zones. Each zone represents a physically separate data center with its own power, networking, and cooling, designed to minimize the risk of simultaneous failures.

The physical distance between regions — and even between zones within a region — affects both latency (the time it takes for data to travel from one point to another) and reliability. Latency increases as data travels physically farther, which can slow down distributed data science workflows that require frequent communication between resources. Reliability is enhanced by spreading workloads across multiple zones, reducing the impact of hardware or power failures in a single location.

When architecting distributed data science workflows, you must consider data locality, bandwidth, throughput, and latency. Data locality refers to keeping compute resources close to where the data resides. This minimizes the need to transfer large datasets across long distances, which can be both slow and expensive. Bandwidth is the maximum rate at which data can move across a network, while throughput is the actual rate achieved under real-world conditions. Latency, as mentioned earlier, is the delay before a transfer of data begins following an instruction.

For example, if you launch a data processing job in one zone but store your data in another region, every read and write operation must traverse the cloud provider's backbone network. This can introduce significant delays and may even incur extra costs. By designing your pipeline so that compute and storage are co-located—ideally in the same zone or region—you can maximize throughput and minimize latency, resulting in a faster and more efficient workflow. These considerations are central when scaling up machine learning training, running distributed analytics, or orchestrating pipelines that rely on timely data movement.

Despite the flexibility that cloud networking offers, it often becomes the primary bottleneck in distributed systems. Even with fast CPUs and abundant storage, the speed and reliability of your workflow are limited by how quickly and consistently data can move between resources. Cloud network architectures impose real-world constraints: inter-region bandwidth is usually much lower than intra-region bandwidth, and cross-region data transfers can be subject to outages, throttling, or additional security checks.

As a result, networking trade-offs shape almost every aspect of system design. You might sacrifice some redundancy to gain lower latency, or accept higher costs to guarantee throughput for critical workloads. Understanding these limitations helps you make informed decisions about where to store data, where to run compute, and how to architect resilient, high-performance pipelines. In data science, where datasets are large and collaboration is global, mastering these fundamentals is essential for building robust solutions in the cloud.