The rise of cloud computing has transformed how organizations manage and process big data. Traditional on-premise infrastructure is being replaced by flexible, cloud-native architectures that offer scalability, cost-efficiency, and speed. At the core of this transformation are cloud data pipelines, which automate the movement and processing of data from diverse sources to valuable insights. One of the most important decisions in this setup is choosing between serverful and serverless computing models. In this blog, we explore modern cloud data pipelines and compare the two computation models shaping the future of big data.
Cloud data pipelines automate the flow of data across various services and platforms in the cloud. These pipelines enable organizations to ingest, process, store, and analyze data at scale with minimal infrastructure management.
Cloud providers offer intuitive, visual interfaces and orchestration tools to simplify pipeline creation and management:
These tools support monitoring, auto-scaling, retry mechanisms, and logging, making it easier for data engineers to operate complex pipelines with minimal effort.
In cloud environments, computation models fall broadly into two categories:
Serverful (or traditional) computing gives you full control over the infrastructure. You provision VMs or clusters, manage them, and scale resources manually or semi-automatically.
Serverless computing abstracts away infrastructure management. You only focus on the code or logic, while the cloud provider handles provisioning, scaling, and resource cleanup.
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