The Recurring Advantages of Intelligent Availability
July 17, 2018

Don Boxley
DH2i

Share this

The essential value resulting from data-driven processes has become progressively linked with analytics. Once considered a desired complement to intuitive decision-making, analytics has developed into a main focus of mission-critical applications across industries for any number of use cases.

However, as the motives for employing analytics for business processes have increased, so has the intricacy of deployments. Organizations must now habitually confront circumstances in which data is spread across a plenitude of environments, making it arduous, error-prone and time-consuming to try to centralize for a single use case. Perhaps even more widespread is the reality in which it’s beneficial to deploy in multiple settings (such as with Linux platforms, in the cloud, or with containers), but budgetary or technological shortcomings make it unviable. Certainly, application performance oftentimes suffers as well.

The truth is today’s ever-shifting data space warrants 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 switch locations for disaster recovery (DR), scheduled downtime, or limited-time pricing offers in the cloud.

By embracing an agile approach predicated on what can be called “intelligent availability” organizations can dynamically provision analytics in a plethora of environments to satisfy numerous business use cases, seamlessly and rapidly transferring data between on-premises settings (including both Windows and Linux machines), the cloud and containers.

Consequently, they enjoy decreased infrastructure costs, effective DR, and an overall greater yield for analytics — and that of data in general.

Analytics in the Cloud

One of the more widespread methodologies in which intelligent availability improves 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 experts on staff.

Nonetheless, the most persuasive reason for running analytics in the cloud is facing the alternative: attempting to scale on premises. Customarily, scaling in physical environments involved an exponential curve with numerous unalterable costs which frequently limited application performance and enterprise agility. By scaling in the cloud and with other contemporary measures, however, organizations enjoy a far more affordable linear curve.

This point is best demonstrated by a healthcare example in which a well-known, global healthcare organization was using SQL Server on premises for its OLTP, yet wanted to deploy a cloud model for Business Intelligence (BI). The choice was clear: either ignore budget constraints by indulging in additional physical infrastructure (with all the unavoidable 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 the majority of well-implemented analytics solutions in the cloud.

The Upside

In this case and a number of others, optimizing cloud analytics involves continually 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 effected several advantages. The most prominent was the reproducibility of a single dataset for multiple uses. Business users — in this case physicians, clinicians, nurses, back-office staff, etc. — are able to access this read-only data for intelligence to impact diagnosis or treatment options, as well as for administrative/operational requirements (OLTP).

This latter point is extremely important. With this paradigm, there are no application performance issues compromising the work of those using on-premises data because of reporting — which could occur if each group was provisioning the same copy of the data for their respective uses. Instead, each user benefits mutually from this model.

The healthcare group is assisted by the primary data being stored on premises, which is important for compliance measures in this highly regulated industry. It’s also important 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 (AWS), Azure, or any private or hybrid clouds they like. They can also readily transition resources between these providers as they see fit: feasibly according to use case or for discounted pricing. Even better, when they no longer need those analytics they can speedily and painlessly halt those deployments — or simply migrate them to other environments involving containers, for example.

Plus Automatic Failovers

The above-mentioned healthcare group also gets a third advantage when utilizing an intelligent 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 intelligent 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 benefit typifies the agility of an intelligent availability approach. Workloads are able to run continuously despite downtime situations. What’s more, they run where users specify them to create the most meaningful competitive advantage. Most high availability methods don’t provide users with the flexibility of choosing between Windows or Linux settings. There’s also a simplicity of management and resiliency for Availability Groups facilitated by intelligent availability solutions, which provision resources where they’re needed without downtime.

Recurring Advantages

Intelligent availability solutions and methodologies 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. What’s more, this approach does so while maintaining critical governance and performance requirements for on-premises deployments. Perhaps 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.

Don Boxley is CEO and Co-Founder of DH2i
Share this

The Latest

August 17, 2018

As a Network Operations professional, you know how hard it is to ensure optimal network performance when you’re unsure of how end-user devices, application code, and infrastructure affect performance. Identifying your important applications and prioritizing their performance is more difficult than ever, especially when much of an organization’s web-based traffic appears the same to the network. You need insight to maximize performance — not inefficient troubleshooting, longer time to resolution, and an overall lack of application intelligence. But you can stay ahead. Follow these 10 steps to maximize the performance of your applications and underlying network infrastructure ...

August 16, 2018

IT organizations are constantly trying to optimize operations and troubleshooting activities and for good reason. Let's look at one example for the medical industry. Networked applications, such as electronic medical records (EMR), are vital for hospitals to provide outstanding service to their patients and physicians. However, a networking team can often not be aware of slow response times on the remotely hosted EMR application until a physician or someone else calls in to complain ...

August 15, 2018

In 2014, AWS Lambda introduced serverless architecture. Since then, many other cloud providers have developed serverless options. What’s behind this rapid growth? ...

August 14, 2018

This question is really two questions. The first would be: What's really going on in terms of a confusion of terms? — as we wrestle with AIOps, IT Operational Analytics, big data, AI bots, machine learning, and more generically stated "AI platforms" (… and the list is far from complete). The second might be phrased as: What's really going on in terms of real-world advanced IT analytics deployments — where are they succeeding, and where are they not? This blog will look at both questions as a way of introducing EMA's newest research with data ...

August 13, 2018

Consumers will now trade app convenience for security, according to a study commissioned by F5 Networks, The Curve of Convenience – The Trade-Off between Security and Convenience ...

August 10, 2018

Gartner unveiled the CX Pyramid, a new methodology to test organizations’ customer journeys and forge more powerful experiences that deliver greater customer loyalty and brand advocacy ...

August 09, 2018

Nearly half (48 percent) of consumers report that they currently use, or have used in the past, services of organizations that were involved in a publicly disclosed data breach and, of those, 48 percent have stopped using the services of an organization because of a breach, according to Global State of Digital Trust Survey and Index 2018, a new report from CA Technologies ...

August 08, 2018

Here's the problem: IT teams are in the dark. The only information they have available to them is based on what users decide to tell them about through calls to the help desk ...

August 07, 2018

Over the past year, the enterprise network grew significantly more complicated, creating new challenges for network professionals, according to IDG’s 8th annual State of the Network study. Internet of Things (IoT) projects, the demands of an increasingly mobile workforce, and an explosion of apps prompted network professionals to enhance their network infrastructure and the skillsets needed to support it. Network professionals are now being asked to help shape IT strategy ...

August 06, 2018

Retailers are already busy prepping to avoid an Amazon Prime type meltdown during the holiday shopping season. However, rather than focusing efforts on coping with surges in traffic to your website, you also need to be thinking about the ongoing speed of your site ...