Big data and cloud, are they going hand in hand? As per a survey by Gartner, less than 50% of the organizations with big data programs are witnessed deploying cloud in some form or the other. Then one question that keeps on arising is why organizations aren’t utilizing the cloud for processing of data. The only answer that organizations give is that “it is challenging”. But, truly speaking it is dependent on the way the infrastructure grew up. Let’s look at some of the challenges of big data and the way to overcome these challenges.
Cloud and Data Centers both are different
On-premises data centers and Cloud both belong to different worlds. Many organizations often misunderstand both the terms. Cloud services are configured and priced differently compared to the regular servers deployed in the data centers. To choose and optimize cloud infrastructure for managing the big data workloads, new skills are required which must be acquired or learned. It is challenging to integrate the existing systems with cloud services.
Right Skills
One of the most critical challenges in the cloud is the lack of skills or expertise needed to operate in the cloud. Experts report that the significant shortages are witnessed in developer skills, cloud architecture and cloud DevOps. The same goes with the big data expertise and skills shortage. One thing that is observed is that cloud, and big data expertise is hard to find as well as pretty expensive.
A big leap from experimentation to production
Most of the organizations say that they have deployed cloud for big data. But, to make the cloud an integral part of the data process production was quite difficult. Along with system integration and complex data, a new cloud environment calls upon more operational hurdles and change management difficulties. Organizations are struggling with upholding and determining SLA’s (service level agreements) for cloud services. If other services are compared with cloud services, to support cloud services is a big time challenge.
Something that is widely misunderstood is cloud security
Initially, security secured a top position in challenges of the cloud. But now, cloud providers have evolved very much safe. Problems are faced because of cloud policies which are not defined correctly, ad hoc use of cloud causing human mistakes. Gartner forecasts that it will be because of customer’s fault that 95% of cloud security issues will occur. On the other hand, cloud security issues should be formally addressed. Moreover, it is vital that the security features should be appropriately used. Organizations should select Cloud vendors who have the ability to work with organization’s existing policies and management systems.
Challenging: Data Movement
Many discussions and debates take the place of moving large sized data workloads from data centers into the cloud. The real challenge is the movement of data to the cloud which is a seamless part of the data flow of the enterprise. Focus should be targeted on micro-batching updates, data pipelines and streamlining. If the organization wants to create an impression on production processes, they must take into consideration two-way and ongoing data movement. Many organizations wish to utilize the cloud for collecting and pre-processing the data and then shift the subgroups to data warehouses which are on-premises. It is more than moving data to cloud from data centers.
Standardization of Cloud Services still remaining
Many evolving tech markets often different model based on capabilities, pricing, etc. This is very much important for data processing systems in the cloud where buyers should take extra efforts to comprehend the numerous differences between numerous solutions for broader managed service offering. Some services are flat rate whereas some are PAYG (pay as you go). One more challenge piling up is selecting the cloud service provider which is will fit appropriately with organization’s existing policies and processes. For big data analytics, the cloud is the future because of its scale and economies and processing power. Deploying cloud for analytics will enable organizations to develop a competitive advantage. Value-added services are one of the biggest resources available today. Since organizations are slow to make a move to the cloud, service providers are coming up with new services to make the transition easy.
To make this facile, make the balance between cloud services you want and developing in-house strategic skills. Here are two best approaches:
1. Evaluation skills of cloud service provider
Assuming consistency across services is not right. Careful evaluation is crucial and make sure to consider industry expertise so that all options are covered. Test and examine integration capabilities of each and every service for determining how well the solution will fit appropriately into your existing infrastructure. Make sure you also carefully consider the security featured offered by the cloud service provider.
2. Which Strategic Skills are essential?
Something that is filling the gap enterprise and cloud is new solutions and services. Enterprise resources are supplemented by managed services while big data as a service acts as abstracting and automating the fundamental cloud intricacy. The services so provided act as an alternative to building a custom cloud data platform from the very beginning enabling one to give concentrated focus on data science and data strategy to interpret and use data. It is better to develop the in-house skills which will lead to having a sustainable advantage.
Keeping these approaches in mind, organizations can successfully transmit big data processing to the cloud.
This article is written by Raza Shaikh. He is an ebullient writer for host.co.in. Technical Writing is his forte. A complete fan adherent of technology. His obsession for latest trends and technologies is relentless. Follow him: Twitter | Google+.
