Why Windows SSD Cloud is better for Big Data
If youâ€™re company/individual with large data then definitely this post will be beneficial for you. Here this post explains why public cloud is better than other options.
According to a recent survey by Oracle, 80% of companies are planning to migrate their Large Data and analytics operations to the cloud. One of the main factors behind this was the success that these companies have had when dipping their toe into Large Data analytics. Another survey of US companies discovered that over 90% of enterprises had carried out a Large data initiative last year and that in 80% of cases, those projects had highly beneficial outcomes.
One of the issues with in-house, Large Data analyses is that it frequently involves the use of Hadoop. Whilst Apacheâ€™s open source software framework has revolutionised storage and Large Data processing, in-house teams find it very challenging to use. As a result, many businesses are turning to cloud vendors who can provide Hadoop expertise as well other data processing options.
One of the main reasons for migrating is that public cloud Large Data services provide clients with essential benefits. These include on-demand pricing, access to data stored anywhere, increased flexibility and agility, rapid provisioning and better management.
Software as a service (SaaS) has also made public cloud Large Data migration more appealing. By the end of 2017, almost 80% of enterprises had adopted SaaS, a rise of 17% from 2016, and over half of these use multiple data sources. As the bulk of their data is stored in the cloud, it makes good business sense to analyse it there rather than go through the process of moving back to an in-house datacentre.
Finally, the cloud enables companies to leverage other innovative technologies, such as machine learning, artificial intelligence and serverless analytics. The pace of developments these bring means that those companies who are late adopters of using Large Data in the public cloud find themselves at a competitive disadvantage. By the time they migrate, their competitors are already eating into their market.
Migrating huge quantities of data to the public cloud does raise a few obstacles. Integration is one such challenge. A number of enterprises find it difficult to integrate data when it is spread across a range of different sources and others have found it challenging to integrate cloud data with that stored in-house.
There are technical issues to overcome too. Particularly data management, security and the above-mentioned integration.
Before starting your migration, it is important to plan ahead. If you intend to fully move Large Data analyses to the public cloud, the first thing to do is to cease investment in in-house capabilities and focus on developing a strategic plan for your migration, beginning with the projects that are most critical to your business development.
Finally, you need to decide on the type of public cloud service that best fits your current and future needs. Businesses have a range of choices when it comes to cloud-based Large Data services, these include software as a service (SaaS) infrastructure as a service (IaaS) and platform as a service (PaaS); you can even get machine learning as a service (MLaaS). Which level of service you decide to opt for will depend on a range of factors, such as your existing infrastructure, compliance requirements, Large Data software and in-house expertise.
Migrating Large Data analytics to the public cloud offers businesses a raft of benefits: cost savings, scalability, agility, increased processing capabilities, better access to data, improved security and access to technologies such as machine learning and artificial intelligence. Whilst moving does have obstacles that need to be overcome, the advantages of being able to analyse Large Data gives companies a competitive edge right from the outset.
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