In the world of Big Data, Data engineers always strive to find a way or method to analyze, process, and compute the Volume, Velocity, and Variety of data, and to provide Data scientists with a resilient backbone to conduct their analysis.
The Future of Big Data
With some guidance, you can craft a data platform that is right for your organization’s needs and gets the most return from your data capital.
Before the introduction of the cloud platforms, all the big data processing and managing of the cluster was done on-premises. However, with the introduction of cloud-based platforms such as Microsoft Azure, Amazon AWS, Google Cloud, etc. led to Big Data Managed Cluster to be deployed in the cloud.
This came with many difficulties such as improper utilization, underutilization, or overutilization on certain time periods. To abstract away the problems associated with Managed Cluster the best solution is Serverless Architecture, which has the following benefits:
- Truly pay for the application you used – Both storage layer and computation layer are decoupled. Still optimized for big data computation. The result is required to pay for as long as you keep the amount of data in storage layer and for the amount of time it takes to do the needed calculation.
- Decreased time of implementation – Unlike deploying managed cluster which takes hours to days, the Serverless big data application takes only a few minutes. One of the great examples is Microsoft Azure Data Lake Store (Storage) and Data Lake Analytics (Computation).
- Fault tolerance and availability – By default, Serverless (PAAS) architecture which is managed by cloud service provider, which provides fault tolerance, availability based on Service level agreement (SLA). So, no need for an administrator.
- Easy Scale & Auto Scale – Defined Auto-Scale rules enable to scale in and scale out application according to workload. Which helps significantly in reducing the cost.
Even though some Serverless architecture has drawbacks such as minimal risk in data security, limited flexibility, and problems of integration with in-house systems and applications, this innovative approach has the capabilities to be the most efficient application to many data engineers who prefer the modern alternatives to traditional architectures.
