With ever greater numbers of companies using big data, we’re definitely starting to see the benefits of their migrating to the public cloud. There are some challenges with it as well, but overall it would seem the good outweighs the not-so-good quite handily though, and the consensus seems to be that it is often a much more ideal environment for large-scale storage, remote access and all without the need for extensive physical infrastructure.
Here at 4GoodHosting, in addition to being a leading Canadian web hosting provider we’re also equally as much of a cloud computing enthusiast as the rest of you. It’s hard not to be a fan of such a positive, game changing development in personal and business computing, and it seems that the shift to the public cloud is more sweeping every day.
Cloud-based Big Data Rising Big Time
A recent survey from Oracle found that 80% of companies intend to migrate their Big Data and analytics operations to the cloud. Powering this decision was the success that these companies have had when experimenting with Big Data analytics. Consider as well that over 90% of enterprises had carried out a big data initiative last year and in 80% of those instances the projects were seen to be very successful.
Further driving the public cloud are the many IaaS, PaaS and SaaS solutions that are now offered by cloud vendors, and the way they are so much more cost effective in their setup and delivery.
One of the main challenges with having data in-house is that it frequently involves the use of Hadoop. Apache’s open source software framework has revolutionized storage and Big Data processing, but in-house teams have challenges using it. Many businesses are therefore turning to cloud vendors able to provide Hadoop expertise along with other data processing options.
The Benefits of Moving to the Public Cloud
Tangible, immediate benefits are the number one reason for migrating. These include on-demand pricing, access to data stored anywhere, increased flexibility and agility, rapid provisioning and better overall management.
Add the unparalleled scalability of the public cloud and it quickly becomes ideal for handling Big Data workloads. Businesses now instantly have the entirety of the storage and computing resources they need, and – equally as importantly – only pay for what they use. Public cloud can also provide increased security that creates a better environment for compliance.
Software as a service (SaaS) applications have also made public cloud Big Data migration a more appealing choice for certain businesses. Almost 80% of enterprises had adopted SaaS by the end of last years, a 17% rise from the end of 2016, and over half of these use multiple data sources. The fact that the bulk of their data is stored in the cloud makes it so that it’s good business sense to analyze it there as opposed to going through the process of reverting to in-house data centre operations.
Next up is the similarly obvious benefit of decreasing the cost of data storage. While many companies might evaluate the cost of storing Big Data over a long period to be decidedly expensive compared to in-house storage, technology developments are already reducing these costs significantly and that can be expected to continue. Expect to see vast improvements in the public cloud’s ability to process that data with much larger volumes and at faster speeds too.
The cloud also enables companies to benefit even further by leveraging other innovative technologies – machine learning, artificial intelligence and serverless analytics – to name just a few. And there is some urgency to get onboard with this, as companies who are slow to migrate to Big Data in the public cloud will likely be quickly at a competitive disadvantage.
The Challenge of Moving Big Data to Public Cloud
Migrating huge quantities of data to the public cloud isn’t a breeze, however. Integration is one of the biggest challenges. It can be difficult to integrate data when it is spread across a range of different sources and many find it challenging to integrate cloud data with data that is stored in-house.
Workplace attitudes can factor in as well, and that can be anything from internal reluctance, or incoherent IT strategies to other organizational problems related to moving big data initiatives to the public cloud. Technical issues are less common, but they can exist as well. The most common of these are data management, security and integration.
Planning your Migration
It is important to plan ahead before starting your migration. Before moving big data analyses to the public cloud, it is advisable to cease your investment in in-house capabilities and instead focus on developing a strategic plan for your migration. This plan should begin with the projects that are most critical to your business development.
Moving to the cloud also presents the opportunity for you to move forward and make improvements to what you already have in place. Don’t plan to keep your cloud infrastructure as it currently is. You now have the ideal opportunity to create for the future and build something superior to your current setup. Take this chance to redesign your solutions taking all that you can from the cloud; automation, AI, machine learning, etc.
You’ll also need to decide on the type of public cloud service that is the best fit for your current and future needs. Businesses have plenty to choose from when it comes to cloud-based big data services, and these include software as a service (SaaS) infrastructure as a service (IaaS) and platform as a service (PaaS. There’s the option as well to get machine learning as a service (MLaaS). The level of service you decide on will be dictated by a range of factors, like your existing infrastructure, compliance requirements, and big data software, as well as the level of expertise you have in house.
Clearly there’s much pushing the migration of Big Data analytics to the public cloud, and it does offer businesses a whole host of benefits – cost savings, scalability, agility, increased processing capabilities, better data access, improved security and expanded access to technologies. Machine learning and artificial intelligence are at the forefront of those technologies, and the premise of their incorporation is decidedly exciting for most of us.