Tag: AItech Article

The growing importance of agility and operational efficiency has helped introduce serverless solutions as a revolutionary concept in today’s data processing field. This is not just a revolution, but an evolution that is changing the whole face of infrastructure development and its scale and cost factors on an organizational level. Overall, For companies that are trying to deal with the issues of big data, the serverless model represents an enhanced approach in terms of the modern requirements to the speed, flexibility, and leveraging of the latest trends.

Understanding Serverless Architecture

Working with serverless architecture, we can state that servers are not completely excluded in this case; instead, they are managed outside the developers’ and users’ scope. This architecture enables developers to be detached from the infrastructure requirements in order to write code. Cloud suppliers such as AWS, Azure, and Google Cloud perform the server allocation, sizing, and management.

The serverless model utilizes an operational model where the resources are paid for on consumption, thereby making it efficient in terms of resource usage where resources are dynamically provisioned and dynamically de-provisioned depending on the usage at any given time to ensure that the company pays only what they have consumed. This on-demand nature is particularly useful for data processing tasks, which may have highly varying resource demands.

Why serverless for data processing?

Cost Efficiency Through On-Demand Resources

Old school data processing systems commonly involve the provision of systems and networks before the processing occurs, thus creating a tendency to be underutilized and being resource intensive. Meanwhile, server-less compute architectures provision resources in response to demand, while IaaS can lock the organization in terms of the cost of idle resources. This flexibility is especially useful for organizations that have prevaricating data processing requirements.

In serverless environments, cost is proportional to use; this means that the costs will be less since one will only be charged for what they use, and this will benefit organizations that require a lot of resources some times and very few at other times or newly start-ups. This is a more pleasant concept than servers that are always on, with costs even when there is no processing that has to be done.

To Know More, Read Full Article @ https://ai-techpark.com/serverless-architectures-for-cost-effective-scalable-data-processing/

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Access to real-time data and insights has become critical to decision-making processes and for delivering customised user experiences. Industry newcomers typically go to market as ‘real-time’ natives, while more established organisations are mostly at some point on the journey toward full and immediate data capability. Adding extra horsepower to this evolution is the growth of ‘mobile-first’ implementations, whose influence over consumer expectations remains formidable.

Nonetheless, sole reliance on real-time data presents challenges, challenges that predominantly circle matters of interpretation and accuracy.

In this article, we explore why inaccurate real-time data and analytics transpire, explain the commonplace misinterpretation of both, and look at some of the tools that help businesses progress toward true real-time data competency.

The Risks of Using Imperfect, Legacy, and Unauthorised Real-Time Data and Analytics

Businesses risk misdirecting or misleading their customers when they inadvertently utilise imperfect or legacy data to create content. Despite real-time capability typically boosting the speed and accessibility of enterprise data, mistakes that deliver inappropriate services can undermine customer relationships.

Elsewhere, organisations invite substantial risk by using data without proper authorisation. Customers will often question how a company knows so much about them when they are presented with content that’s obviously been put together using personal details they didn’t knowingly share. When such questions turn to suspicion, the likelihood of nurturing positive customer relationships shrinks.

Misinterpreting Data and the AI ‘Hallucination’ Effect

Real-time data’s speed and accessibility are also impeded when full contexts are absent and can lead to organisations making hasty and incongruent decisions. Moreover, if the data is deficient from the start, misinterpretation of it becomes rife.

Today, the risks of flawed data and human oversight are exacerbated by a novel problem. Generative AI technology is known to ‘hallucinate’ when fed with incomplete datasets. At significant risk to the organisation, these large language models fill any gaps by inventing information.

To Know More, Read Full Article @ https://ai-techpark.com/real-time-data-and-analytics/

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