What is Serverless Analytics?


When integrated into your data analytics pipeline, Serverless Analytics offers a wide array of benefits to your reporting. First and foremost is the ability to implement your analytics using an on-demand execution model. Due to the large amount of processing involved, it’s typical that with legacy processes this is done in batches. However, with the always-available nature of serverless technology, your pipeline can benefit from real-time responses to data changes, with the ability to adjust flow logic based on the trends that emerge in your data as it happens.

To understand serverless analytics, it’s first worth better understanding Serverless Computing.

What is serverless computing?

Serverless computing is a method of providing backend services on a pay-as-you-use basis. This kind of setup allows users to write and deploy code without having to consider the limitations of any system infrastructure.

In the early years of the internet, if you wanted to build a web application, you had to own the physical hardware required to run a server, which was both an expensive and inconvenient undertaking.

Cloud computing soon followed, where fixed numbers of servers or amounts of server space could be rented remotely. Developers and companies who rent these fixed units of server space generally ‘over-buy’ to make sure that a spike in traffic or usage won’t exceed their monthly limits and crash their applications. This means that the majority of server space that’s paid for often goes to waste. Cloud vendors, like Brytlyt, are now able to deliver flexible, auto-scaling models as a smart solution.

Benefits of Serverless computing

Serverless computing allows developers to purchase backend services on a flexible ‘pay-as-you-go’ basis, meaning that developers only have to pay for the services they use.

Regarded as a form of utility computing, the term ‘serverless’ is a little misleading in the sense that servers are still used by us. As we allocate machine resources on-demand, when an app is not in use, there are no computing resources allocated to the app and costs are based on the actual amount of resources consumed by an application.

What sort of backend services can serverless cloud-computing provide?

Most serverless providers offer storage and database services to their customers, and many also have FaaS (Function-as-a-Service) platforms. FaaS allows development teams to execute small pieces of code on the network edge. This gives the ability to build a modular architecture, making a codebase that’s more scalable without having to use resources on maintaining the underlying backend.

Getting more from Advanced Analytics

The business case for data-driven decision making has never been more compelling. Nowadays, it’s practically impossible to find an enterprise that isn’t influenced by modern Data Analytics. So much so, that it’s now a cornerstone of certain industries, such as financial services and insurance.

If an organisation wants to compete in Business Intelligence and analytics in today’s market, they can’t simply rely on instinct alone. With so many new data touch points, companies can now implement a range of processes to better understand the success of products, services, or feedback from customers, as well as gaining a better understanding of the status of competitors.

Leveraging Serverless Analytics

The business case for data-driven decision making has never been more important. So much so, that it’s now become a cornerstone of certain industries. 

New capabilities in Accelerated Analytics are continuously emerging that necessitates the creation of infrastructure and advanced analytics toolsets to implement new data and information requirements. 

In addition to this, data governance platforms are also gaining importance by offering tools that manage the integrity, consistency, and availability of data.

Being able to see trends, patterns, areas of growth, and also areas that need attention is of huge value – the more information your business possesses on current trends and behaviour, the greater the ability to forecast future events.