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Analysis

How To Move Big Data To The Cloud

By February 3, 2020March 3rd, 2020No Comments

More and more companies are leveraging big data. But implementing it can be a complicated technical process. Big data has benefits but in order to fully utilize it, enterprises have to be able to store and process it, and that is why most of the companies move their data to the cloud.

Using a cloud service to move and store your big data has many advantages, even though, some businesses still hesitate to make the transition or full transition because they think the risks are too high. Companies need to know how to handle these types of moves. The following best practices will help.

Prepare the management for transferring big data to the cloud

Getting key people behind the move is imperative. Every C-level executive should write down their list of advantages of combining big data with cloud services. For example, CIOs and CHROs could use data platforms for data discovery and integration, which represent a powerful tool for the business. They will be able to get answers they never were able to see in the past, such as why are products being returned or what influences employee satisfaction. Cloud advocates need to see how the implementation will help the company be more agile in responding to opportunities and challenges, as well as improving customer engagement and loyalty.

Workload evaluation

Big data applications are divided into three broad categories: storage, processing, and development. Many big data clouds are configured to support a combination of two or all three types. The best big data clouds will simplify the deployment plan and configuration process. But companies still can’t predict exactly what the mix of workloads they’ll need to use in the future. They should instead invest in the right category of big data, the one they use the most, and that will represent the foundation for change.

Technical approach

Before deciding what to do, companies need to do a sophisticated analysis of the existing data applications and workloads to determine which type of cloud is more suitable to be implemented. Companies need to identify all the data, analytics, cloud service layers, and the platform necessary to support the estimated workload. Enterprises also need to evaluate and test various cloud deployments and service models and then decide which is the best solution for their needs.

Privacy, security and compliance requirements

Big data on the cloud is a complicated system to control and manage. Where safety is a primary factor to consider, there is no reason to be concerned. In fact, the security vulnerabilities in big data cloud might have more to do with the unfamiliar platforms and the necessity to combine and harmonize distinct legacy security systems. Generally speaking, companies will face the need to adapt existing controls in order to fit the new platforms of data in the cloud. So, launching new controls that address the data domains managed in the cloud is definitely a must.

Ready to use

The readiness of the system will depend on the uses and the applications being changed. Providing a basis for reorganizing data management and IT analytic processes. In this way, the system will be able to support big data cloud initiatives. For example, firms could implement continuous deployment of big data applications, because the traditional development cycles are no longer applicable.