As global innovation is gaining momentum, businesses are shaping their growth strategies with the technology in mind. But applying data insights or predictive analytics in day-to-day operations is not enough today. Companies need to advance existing solutions to maximize the outcome. One of the ways to do so is switching to readily available and bespoke cloud solutions — the big data cloud perspective is quite promising here.
In fact, cloud adoption no longer requires time-consuming and tedious justification and validation by technology executives. Today, it’s merely a matter of “how quickly” and “to what extent, given the specific needs of the business”.
Cloud and big data have perfect synergy for several reasons. The scale of big data solutions makes cloud platforms with their serverless computing capabilities, instant scalability, and universal accessibility the perfect choice for projects of any complexity. At the same time, cloud service providers (CSPs) are pushing the envelope to deliver tools and services tailored for big data mining, analysis, and storage.
Let’s take a look at what’s in this promising combination for the adopters of cloud data services:
- Benefits of big data cloud computing in business applications
- Challenges for adopters of data cloud solutions
- Ace digitalization with big data cloud solutions
Benefits of big data cloud computing in business applications
According to Gartner, “By 2022, public cloud services will be essential for 90% of data and analytics innovation.” This figure is very likely to also account for cases where companies opt for multi-cloud and hybrid cloud scenarios that offer the required level of data security compliance and fault tolerance while leveraging all the apparent benefits of public cloud services.
Today’s hot trend in the industry is a very distinct convergence of big data and data analytics solutions — and major cloud-based platforms and services play an important role in this process. In fact, the practice of accessing, managing, and analyzing big data in the cloud is now referred to as “Big Data as a Service” or BDaaS. And yes, as you can see in the following chart, it’s growing at a rate that fully justifies the acronym.
North America big data as a service market size, by deployment, 2015 – 2025 (USD, mln)
Source: Grand View Research
So what are the business benefits of marrying big data to cloud computing to implement a more cohesive, all-in-one solution?
Unlike on-prem data centers that are inherently expensive and often underutilized, a big data cloud service offers the benefit of paying just for the resources consumed and not a penny more. This automatically results in tangible savings, given that the application is properly designed and configured for the cloud.
When you sign up with a big data cloud service, you delegate the upkeep hassle to the corresponding CSP: equipment maintenance, qualified technical staff, power bills, network troubleshooting, physical security, software updates, and so on. These organizations are typically very well-equipped for these tasks.
In case of conventional SQL-based data warehouses, the cost of constant upscaling and reconfiguration would be peaking and lots of effort would be going into dropping old (yet historically valuable) data to free up space. A cloud-based big data analytics solution based on such tried and tested technologies as Hadoop can bring substantial cost advantages for organizations dealing with an ever-growing amount of unstructured data.
One of the key advantages of working with big data in the cloud is its natural elasticity. A big data cloud can shrink and expand depending on the immediate workload and storage requirements, allowing the client organization to pay only for the resources used over a period of time (as mentioned above) and maintain a certain predefined target level of application performance.
Elasticity — often fully automated — also helps reduce resource management efforts that would normally be added to the overall cost of operation in case of a more conventional, on-prem setup. This capability comes in especially handy for resource-intensive applications prone to occasional/seasonal/situational spikes of user activity.
Some good examples would be streaming services or large e-commerce sites where spikes are observed during holidays, weekends, or after the release of popular titles or products.
Finally, the ability to dynamically match the demand also facilitates the process of working with cloud-based big data analytics, enabling data scientists and analysts to always have unobstructed, fast access to historical data.
Contextual reporting and decision intelligence
The advent of the big data analytics cloud may steal the glory from the best, most elaborate BI dashboards out there. The latter are usually complex, multi-layered, and require business users to know where to look for the information they need. The transition to cloud computing and big data allows for real-time, highly personalized, contextual reporting intended for particular managers, user roles, or technical experts.
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Contextual reporting can be based on a broad variety of technologies, including advanced ones like natural language processing (NLP), augmented analytics (use of AI and ML to help analyze data), real-time streaming anomaly detection, and many more.
The convergence of big data and cloud computing also creates fertile soil for practical data science in general and decision intelligence in particular. This complex discipline is a fusion of decision management and decision support manifested through the use of innovative, intelligent analytical systems based on big data.
Better business continuity and disaster recovery
Implementing effective fault-tolerance and business continuity mechanisms for on-prem data centers is a complex and expensive undertaking that not many companies can handle technically and financially. A big data cloud, however, comes with all of these features readily available as free or reasonably priced, low-maintenance options.
All major CSPs offer data redundancy as part of their standard service offering and take care of creating multiple copies of their clients’ data at multiple levels and in various geographically distributed data centers. Coupled with modern containerization technologies such as Kubernetes supporting one-click or fully automatic deployment, these measures guarantee fast and damage-free recovery of your applications and data.
Finally, every big data analytics cloud is reliably protected from most types of cybersecurity threats to an extent that is hardly attainable by in-house solutions. Additional cybersecurity consulting services can be obtained from corresponding CSPs or qualified third parties.
Cloud computing for big data dramatically eases the task of aggregating heterogenous data from any number of sources, which may include sensor arrays, IoT devices, remote databases, web applications, online partner networks, users, and many more. These data can then be processed with a high degree of parallelism and assigned to corresponding data pipelines.
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Cloud data services make it all possible by offering unmatched flexibility and performance that can be fine-tuned to enable real-time data processing for virtually any need.
Despite the obvious advantages of big data in cloud computing, the implementation of the necessary components and their integration is by no means a leisurely walk in the proverbial park. The challenges are plentiful and a weighted approach to creating a cloud and big data strategy is required.
Challenges for adopters of data cloud solutions
Losing control over data
As the size of your cloud and big data goes up, you may see a proportionate decline in the degree of control you have over them. There are still tons of cybersecurity threats out there and the human factor isn’t going anywhere. Human negligence and oversight are among the top factors leading to data leaks and damage, especially in large infrastructures with incomplete coverage by automation and monitoring tools.
Recommendation: create and maintain strict cloud usage policies; ensure timely security updates; use automation where possible.
Reliance on third parties
Clouds are super-reliable, but they aren’t infallible. Occasionally, important services go offline without prior warning and leave millions frantically trying to access their mailboxes, documents, and data.
Recommendation: big data in cloud computing requires users to consider native and implement custom/third-party monitoring tools combined with detailed risk mitigation and remediation plans. Adopting a multi-cloud approach may be an option as well.
Network can cause a bottleneck
Cloud computing for big data is rarely done on premises. When you move all or most of your data and analytics to the cloud, you risk becoming completely dependent on your Internet connectivity. If your primary and secondary lines go offline, you will be left with no access to your data cloud solutions (although the data itself will keep flowing into the cloud).
Recommendation: make sure you have an auxiliary line with an alternative ISP; leave critical components in your on-prem infrastructure; assess the risks of going offline; come up with a mitigation plan.
Ace digitalization with big data cloud solutions
The unification of big data and cloud computing is completely in line with the major trends in the big data domain. As companies continue to embrace various digital models, combining cloud computing and big data for the benefit of customers and employees, the greatest impact will be observed in the areas where CIOs and CTOs have managed to create a balanced and realistic cloud strategy coupled with a company-wide transformation roadmap.
Ready to upgrade the speed and flexibility of your data via the cloud? Reach out to our experts for a guided tour through your options and implementation scenarios. We’ve done it before and will gladly apply our knowledge and expertise to transform your business for higher operational effectiveness.