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Implement An Efficient And Profitable Big Data Infrastructure

By TrendsTechBlog , in TECHNOLOGY , at October 23, 2020 Tags: , , ,

We are all aware of the high volume of data that is generated in and around business processes. Faced with the current critical situation, no company can afford the luxury of not taking advantage of this data to obtain valuable information that helps to consolidate or trace the business. We share with you tips to implement an efficient and profitable Big Data infrastructure with which to achieve this.

Without An Effective Big Data Strategy There Is Neither Analytics Nor Artificial Intelligence

The upgrade of the IT infrastructure is needed to access structured data and unstructured data. It is no longer enough to consult the data of corporate business solutions, and now it is necessary to incorporate the flow from emails, photos or videos to chat conversations, social media publications. The problem is that this new scenario demands greater capacities in three essential IT resources: storage, computing and network.

Only with the modernization of the data center focused on Big Data will it allow decision-makers to have a wide range of data to combine in their analytical solutions to get the most right. Moreover, without an IT restructuring, it will be impossible to apply Artificial Intelligence and easily access automated proposals with which to make decisions in real-time that allow everything from customer loyalty or discover new business opportunities, to avoid risks or optimize processes.

It is necessary to have an agile, flexible and scalable IT architecture to avoid bottlenecks in the delivery of the information requested by users, and at the same time capable of adapting to changing business requirements, aligning with market trends. All this without incurring in continuous and excessive costs.

Choosing The Best It Resources For Big Data

Mass data processing demand robust and efficient systems. IT vendors offer multiple valid options, but their choice is difficult without an internal technology team with sufficient knowledge and expertise. Trusting the process of consulting and deployment in Orbit Consulting Group simplifies this task and allows any company to have a Big Data & Analytics successful strategy.

Many organizations rely on IT infrastructure services in public Cloud to establish their Big Data IT architecture, taking advantage of its ease of scaling and avoiding large investments in on-premises solutions and complexities in deployment. But directing Big Data only to the Cloud is not a magic solution to meet your needs. The cloud environment has its limitations, as discussed below.

Public Cloud Issues For Big Data

The first stumbling block is that 100% compliance with all data privacy and confidentiality requirements required by both sectoral and general GDPR- type regulations is practically impossible in public clouds. Suppliers are the ones who set the rules, and it is difficult for them to fully adjust to the particularities of each company (SLAs, SLOs.).

On the other hand, the search for specific plans that adapt to existing budgets and the changing needs of the business and departments leads to the contracting of multiple cloud services from different providers. They are multi-cloud environments. But managing multi-cloud infrastructures is quite complex. Harmonizing each of the services so that corporate IT acts as a whole, securing it and, at the same time, containing costs so that they do not skyrocket difficult.

Deploy Hybrid Environments For Big Data And Analytics

This scenario is causing the majority to opt for Big Data strategies in hybrid IT environments. The combination of private Cloud and public Cloud makes it possible to rationalize the use of resources based on their criticality and the type of data being processed, placing in physical those instances of greater value that require greater protection, and overflowing in the public Cloud what is needed as a compliment.

Nobody is aware that managing and monitoring the Big Data infrastructure requires enough knowledge and experience in IT systems to balance teams according to workloads, seeking a continuous and agile flow between resources on-premises and in the Cloud. 

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