How Serverless Computing Provides an Edge over Containers for Data Science Applications

By CIOReview | Tuesday, December 11, 2018

The need to provide cutting-edge technology is pushing the information technology service providers to look for new and innovative technologies. The only way for businesses to survive in this dynamic environment is to keep striving. Cloud computing techniques are providing efficient and 24/7 support to the enterprises. Serverless computing and container-based programming are also a part of cloud computing.

Serverless computing, as the name implies, does not mean that the servers are not required for computing, but on the contrary, it relies on remote cloud-based servers. The name serverless computing comes from the fact that all the IT operations and maintenance is handled by third-party service providers. Serverless computing allows the development team to control and distribute the application, and at the same time system infrastructure is handled remotely. This allows the programming team to focus on software development without having to worry about powering and maintaining the remote hardware. Amazon’s Lambda provides serverless computing.

Containers are a software package that gets delivered and used as a standalone application environment. The package includes software codes, runtime and system tools, software and foundation libraries, and default settings. Virtualization containers solve problems that arise from cross-platform use because developers often face issues while moving from one computing environment to another. Containers solve many issues about network topology, security, and privacy policies by wrapping all the functionality into a runtime environment. Companies using the container-based solutions require an internal server to manage their data.

Containers inherently are better for the deployment of bigger and complex applications. Virtualization has added more viability to the containers as it delivers the monolithic and complicated solutions to the appropriate environment. Serverless computing is more cost-effective as the enterprises only pay for the amount of time and volume of traffic. Pricing in serverless computing is only based on the active resources and idle time induces no or very little cost.

For data science, Serverless computing has the edge over Containers as it always monitors resource usage and can scale up or down the services according to the requirement. This service is beneficial for smaller teams which don’t have enough bandwidth to scale their services up when there is a requirement.