Data engineers are constantly looking for new ways to deal with the massive volumes of information they have to process on a daily basis. Before the commercialization of cloud technology, all big data management was done with local bare metal equipment. This led to some rather wasteful situations where servers were online but weren’t actively executing code.
Managed clusters have helped to somewhat assuage this problem. Since the underlying hardware automatically takes care of incoming requests from any number of companies at all hours of the day. However, they take a huge amount of time to properly configure.
Serverless computing environments don’t suffer from either problem. Which has helped to make this type of architecture extremely attractive. Especially to those trying to get ahead in the world of big data technology. Despite the name, however, these systems do rely on physical hardware. It’s simply hidden under an abstraction layer.
Construction of a serverless framework
Engineers can design serverless platforms or computers in one of two ways. They can deploy applications that don’t rely on provisioned servers or simply run serverless code alongside traditional microservices. In either case, the programs still rely on a pool of physical resources that has to exist somewhere in the real world.
However, the cloud provider dynamically manages the allocation of these resources to prevent waste. They also take care of maintaining the underlying system software. Perhaps most importantly, the provider’s administrators install serverless security patches. This may be especially important when complying with GDPR regulations.
Privacy and anonymization have become two of the hottest topics in the world of big data. Serverless ecosystems can help with both.
Why serverless architectures tend to be inherently secure
An overwhelming majority of major data breaches on bare metal servers are the result of OS vulnerabilities. Even otherwise secure Unix systems suffer from vulnerabilities. Which have to be periodically patched to prevent people from installing rootkits that might let them execute arbitrary code. Unfortunately, most system administrators aren’t able to handle this task.
Big data processors deal with so much information that they simply don’t have time to handle these common tasks. Cloud providers often automate their security chores with shell scripts. They also often run heuristic scans to ensure that data stored remotely on their servers don’t include anything that’s potentially malevolent.
Proprietary public cloud environments always come with a host of privacy concerns. Which can make them especially problematic for companies that have to process medical data. Open-source serverless architectures like k8s and Docker seldom collect usage information for analytical reasons. Migration to serverless ecosystems may be one way to comply with healthcare regulations. Though interested parties are urged to do more research before they invest in any single solution.
How serverless systems handle API calls
Considering the various abstraction layers at work in a serverless application. It can be difficult to understand how one could ever handle an API call. The real beauty of them, however, is that programmers don’t have to learn any new tricks to make requests. The layers themselves manage calls and route requests to the right database automatically.
Several special APIs have been developed to validate requests sent by any given entity. This prevents unauthorized access to information without requiring coders to do anything special. If they mark part of a database as read-only, then no one can write to it. Anyone with full administrator privileges can even lock certain people out of sensitive information if they prefer.
Latency can be a real problem when working with massive sets of data. To reduce the amount of time it takes between queries and return responses, administrators can optimize the tables inside individual databases. The most heavily used tables can be positioned in hotter areas. Which gives the serverless computer the freedom to call records from them instantaneously.
Tables that aren’t used as much can be assigned a lower read capacity, so they don’t waste valuable system resources.
Improving scalability in data processing
Provisioning server resources usually involve a great deal of guesswork. A site collecting information from users can add over 10,000 records one day and less than 50 the next. Companies tend to either provision far more space than they require or grossly underestimate their needs.
This isn’t a problem for those using ecosystems based around serverless computing. Most providers offer storage and processor time based on flexible plans. Businesses only have to pay for the resources they use. Cloud providers charge their clients a set rate based on what they consumed during the billing cycle. In some cases, this has even encouraged companies to develop more efficient ways of processing amounts of data. Since this system serves as an incentive to reduce overhead.
Compatibility features offered by serverless architectures
Administrators probably concern themselves more with compatibility than any other issue when it comes to deploying anything new. Fortunately, serverless architectures work with almost every major commercial-grade cloud platform on the planet. Microsoft Azure, AWS and all of Alphabet’s various services can work under this paradigm with no issues.
Countless other systems are in the process of migrating over to serverless architectures. Plugins are now being offered for esoteric tools like SSH as well as more pedestrian apps. Market pressure will more than likely ensure that almost every major framework eventually provides end users with at least some form of a serverless function. Continued development in the realm of open-source software has also encouraged migration since open packages can be edited by anyone.
The future of serverless computing
This trend has also opened up new possibilities for data processing companies that want to develop their own technologies in-house. Firms that have their own database management techniques can rent resources through a provider and execute code remotely to save money. In a few cases, larger firms have hosted their entire databases on outside resources and slashed their expenses as a result.
While serverless computing is by no means a panacea that fixes every problem big data analytics companies face, it’s certainly poised to fundamentally change the way these firms do their job.
