Serverless computing is not new to the tech world today. From the days of Netflix to modern day social media platforms like Instagram, some of the world’s most consumed web services rely on Serverless platforms for their daily operations. For beginners, Serverless computing is basically developing an application as a collection of computing components that reside on the cloud and each component being able to scale itself on a consumption demand basis. Or in fact, the computing part of the application is more of an elastic cloud service that powers up when usage is high and scales down when there is limited traffic. A serverless provider like Amazon Web Services (AWS) or Microsoft Azure or any others, would facilitate the provision of computing as a service thereby taking away the tiring task of configurations, environment monitoring, load balancing, etc from the hands of the application developers.
If initially, the cloud was predominantly a storage mechanism for data, today hundreds of internet-powered consumer applications rely on cloud-based Serverless architecture to serve customers globally. While consumer facing tech companies found Serverless computing to be a hit, other businesses and non-technology focused enterprises too can reap the benefits of such a technology architecture. One of the biggest avenues where they can explore is Serverless real time analytics of data. Today organizations generate data in truckloads and almost every organization in the Fortune 1000 list uses a data analytics platform to drill insights from their data pool. The gamut of data that flows through the enterprise data pipeline has potential to transform bottom line profits across business divisions that supply or consume this data.
As with any powerful enterprise tool, data analytical platforms too can consume a gigantic amount of IT juice in terms of computing power, storage, and associated resources. This could shoot up IT budgets and in today’s frugal economy, spending on non-core business activities has the least consideration from the CFO’s desk. And besides, the risks of failure for centralized technology solutions are higher thereby creating more resistance for its implementation from the top division. As such a serverless system for real-time data analytics would garner immense fanfare today.
Let us have a look at some of the key advantages of having a serverless real-time analytics platform.
Independent Scalability on demand
Enterprise data may flow in from all corners and from even IoT enabled devices across operational zones. Each may have its own designated processing function in the analytical engine and the data may arrive in a highly disordered fashion. With a serverless platform, the volume, velocity, and veracity of incoming data streams can be easily handled by individual components of the analytical system. If data from IoT systems have a greater influx, then those analytical components dealing with it can be scaled up while others work at optimum scale to maintain the efficiency of processing results.
The lack of having to monitor servers continuously for incoming data streams is a huge deterrent on costs. Serverless platforms facilitate processing on demand and scaling on demand as explained above. Thus, you only pay for the resources that you consume for every operational timeslot on a serverless platform. You do not have to continuously keep your entire system available online as individual components can very well survive on their own and scale to meet demands.
In traditional cloud architecture, your deployment consists of an actual physical machine or server. It must be configured for the environment your analytics platform is built on, the technical and coding paradigms that need to be supported must be defined, hosting parameters and configurations need to be calculated, availability and latency monitoring needs to be in place and much more. A typical deployment of such a platform could take months. A serverless platform basically deploys virtual machines or functional components that could carry out its purpose immediately after deployment thereby making overall deployment schedules to take just milliseconds rather than months to happen.
Real-time processing thanks to microservices
With serverless platforms organizations can achieve real-time data refresh across processing units rather than a combined intra-organizational data refresh at stipulated time frames. Thanks to microservices that can act on individual component data streams, results would be instantaneous and on the go. Reporting and continuous monitoring become easy and data redundancy between information systems can be avoided tremendously thanks to real-time synchronization of sources.
As you can see, a serverless real-time analytics platform has numerous advantages for enterprises when compared to traditional server-based systems. If you are thinking about creating such a powerful yet flexible real-time analytics platform for your business, then drop us a line today. We will be glad to provide you with a suitable roadmap into a serverless future for your analytic systems.