When it comes to designing the big data framework within the organization, serverless computing is coming off as a perfect solution. Setting up a dedicated server to facilitate your custom requirements is history now. To deal with the complex analytics workload, organization are often re-architecting their IT infrastructure.
Moreover, the popular buzzword of pay as you go and pay for what you use is proving to be a major driving factor in accelerating the adoption of serverless architecture. With a sudden rise in adoption, it is becoming mainstream as more and more people are opting for the same.
Serverless is definitely proving to be a big benefit for the organizations across the world. However, there are very few who are leveraging it to enable their big data solutions. Let’s analyze whether serverless is right for powering big data solutions or not. If yes, what are the major benefits it brings to the table?
Understanding Serverless
Serverless is a popular term which houses two of the widely used technology, Function-as-a-Service (FaaS) and Backend-as-a-Service (BaaS). It is a software design pattern where you build and run your applications without having to manage the infrastructure.
In serverless, the code runs in a short-lived function which is triggered as per the requirement from various triggering sources. These functions are managed by the 3rd-party serverless providers. All you have to do is just write code and hit deploy.
Just as we moved from monolithic to microservices, the trend is moving towards nano services powered by functions. As discussed, one of the major reason for the popularity behind this technology is its billing structure. You are required to pay only for the time your function is running. This is proving to be a major cost benefit to the many organizations.
Challenges with Big Data Applications
-
Working with multiple data sources and platforms is a critical task. This includes data flowing in from multiple sources like application & website. Also, this data is generated in multiple formats as per the event, for example, structured and unstructured.
-
Managing infrequent and uneven traffic is another important factor which big data solutions have to tackle smoothly. Since traffic spikes vary from millions during the day and hundreds during the night, having good capacity to handle uneven traffic is imperative.
-
Another common challenge is working with inconsistent data. This is because data comes with various abnormalities and noise information.
Over time, we’ve observed a shift in the strategy of generating value from various user touch points. Currently, we are at a time where generating insights which are actionable is easy with data warehousing solutions like Big Query. These are such tools which make it easy for any size of the organization in getting started with big data solutions.
But along with that, we need to make sure that we implement these solutions with less investment in the infrastructure. Big Data is a potential serverless use case since it enables you to do that about which we will discuss in the next point.
Big Data & Serverless
Let’s discuss some of the benefits of serverless and why it makes a potential solution for the big data solutions.
#1. High Availability
Since functions are entirely managed by the serverless providers, they have proven track record of high fault tolerance and availability. This also lets you focus more on the application development, and it’s core features since infrastructure management is not one of your concerns.
#2. Reduced Time to Market
Unlike traditional architectural systems where deploying a cluster takes more than two days, infrastructure is managed by the serverless providers. This abstracts the time for you to set up and getting started with your system. Big data solutions powered by serverless takes only a few minutes in getting started.
#3. Flexible Pricing Model
If we talk about traditional systems, computation as well storage layer are coupled together. This is not the case with serverless architecture. This is a major benefit and enterprises are leveraging it quite well. This means you will be paying separately for storing your data and the consumption of the computing power. And computing power is billed as per the on-demand usage which is proving to be cost effective.
#4. Easy to deploy & Scale
With serverless, you can define your own rules of auto-scalability which lets your application scale out/in as per the workload. On top of this, as you’re paying only for the time, your function is running, which helps in cost reduction.
Things to Consider
Before you implement your big data solution on serverless architecture, there are few things which you need to consider. Since no solution is a perfect solution, let’s discuss them.
First and foremost, you need to figure out whether serverless is proving to be an inexpensive solution to your big data system or not. Secondly, whether your existing team is capable enough to implement it or you will be required to hire new talent for this particular solution. Also, you will need to figure out your strategy for the management of big data pipelines. Data security is also a huge concern once you move towards the data-centric solutions.
With ETL jobs, the data is fetched from various sources which extend the execution time significantly. And since serverless functions are short-lived, this clashes with the best practices. You may implement this but is a potential anti-pattern, and it will also increase the overall cost of your big data solution.
However, serverless and big data are going through an evolutionary phase, and the future beholds the potential solutions to the given problems. Saying this, serverless will reap you good benefits only when the problems are understood thoroughly, and the system is architected accordingly.
