The fundamental value derived from data-driven processes has become progressively combined with analytics. Once viewed as a desired complement to intuitive decision-making, analytics has arisen as the focus of mission critical applications across industries for any number of use cases.
Yet as the reasons for applying analytics to business processes have multiplied, so has the complexity of deployments. Organizations regularly confront situations in which data is spread across a number of environments, making it burdensome to centralize for a single use case. Perhaps even more widespread is the reality in which it’s beneficial to deploy in another setting (such as with Linux platforms, in the cloud, or with containers), but monetary or technological deficiencies are cost-prohibitive.
The fact is today’s ever-shifting data space necessitates enterprise agility for analytics as much as for any other aspect of competitive advantage. Processing is optimized by performing analytics as close to data as possible, which may need to shift locations for disaster recovery (DR), scheduled downtime, or limited-time pricing offers in the cloud.
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By adopting an agile approach founded on smart availability, organizations can dynamically provision analytics in a multitude of environments to meet business use cases, seamlessly transferring data between on-premises settings (including both Windows and Linux machines), the cloud and containers.
Therefore, they garner decreased infrastructure costs, effective DR, and an overall greater yield for analytics—and that of data in general.
One of the more common ways in which smart availability progresses analytics is with cloud deployments. There are a number of advantages to going to the cloud for analytics, not the least of which are the pay-per-use pricing model, decreased infrastructure, and elastic scalability of cloud resources. There are also several software as a service (SaaS) and platform as a service (PaaS) options—some of which involve advanced analytics capabilities for machine learning and neural networks—for users without data science teams. Nevertheless, the most compelling motive for running analytics in the cloud is the alternative: endeavoring to scale on premises. Historically, scaling in physical environments involved an exponential curve with numerous unavoidable costs which frequently limited enterprise agility. By scaling in the cloud and with other modern measures, however, organizations experience a far more affordable linear curve.
This point is best illustrated by a healthcare example in which a large international healthcare group was using Microsoft SQL Server on premises for its online transaction processing (OLTP), yet they wanted to deploy a cloud model for business intelligence (BI). The choice was obvious: either ignore budget constraints by splurging on additional physical infrastructure (with all the requisite costs for licenses and servers) or deploy to the cloud for real-time data access of their present kit. The latter option decreased costs and maximized operational efficiency, as will most well-implemented cloud analytics solutions.
In this case and several others, optimizing cloud analytics involves continuously replicating on-premises data to the cloud. Shrewd organizations minimize these costs by opting for asynchronous replication; the aforementioned healthcare entity did so with approximately a second latency for near real-time access of its healthcare data. Replication to the cloud is often inexpensive or even free, making the data transfer component highly cost-effective. By making this data available for BI in the cloud, this organization affected several advantages. The most eminent was the reproducibility of a single dataset for multiple uses. Business users—in this case physicians, clinicians, nurses, etc.—are able to access this read-only data for intelligence to impact diagnosis or treatment options. Moreover, they do so while the original data is accessible to additional users on premises for functions related to OLTP.
This latter point is critical. With this paradigm, there are no performance issues compromising the work of those using on-premises data because of reporting—which might occur if each group was provisioning the same copy of the data for their respective uses. Instead, each party mutually benefits from this model. The healthcare group is aided by the primary data being stored on premises, which is important for compliance measures in this highly regulated industry. It’s also vital to note the flexibility of this architecture, which most immediately affects cloud users. Organizations can establish clusters in any of the major cloud providers such as Amazon Web Services, Azure, or any private or hybrid clouds they like. They can also readily transition resources between these providers as they see fit: perhaps according to use case or for discounted pricing. Even better, when they no longer need those analytics they can quickly and painlessly halt those deployments—or simply transfer them to other environments involving containers, for example.
Plus automatic failovers
The aforementioned healthcare group also gets a third advantage when utilizing the smart availability approach for running analytics in the cloud: automatic failover. In the event of any sort of downtime for on-premises infrastructure (which could include scheduled maintenance or any sort of catastrophic event), its data will automatically failover to the cloud using smart availability techniques. The ensuing continuity enables both groups of users to continue accessing data so that there is no downtime. Those primary workloads simply transfer to cloud servers, so workloads are still running. This advantage typifies the agility of the smart availability approach. Workloads are able to continuously run despite downtime situations. Furthermore, they run where users specify them to create the most meaningful competitive advantage. Most high availability methods don’t give users the flexibility of choosing between Linux or Windows settings. There’s also a simplicity of management and resiliency for Availability Groups facilitated by smart availability solutions, which provision resources where they’re needed without downtime.
Smart availability methods enable users to maximize analytic output by creating recurring advantages from what is essentially the same dataset. They allow users to move copies of that data to and between cloud providers for low latency analytics capabilities, with some of the most advanced techniques in use today. Moreover, this approach does so while maintaining critical governance and performance requisites for on-premises deployments. Best of all, it maintains these benefits while automatically failing over to offsite locations to preserve the continuity of workflows in an era in which information technology is anything but predictable.