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

October 19, 2018

APM is becoming more complex as the days go by. Server virtualization and cloud-based systems with containers and orchestration layers are part of this growing complexity, especially as the number of data sources increases and continues to change dynamically. To keep up with this changing environment, you will need to automate as many of your systems as possible. Open APIs can be an effective way to combat this scenario ...

October 18, 2018

Two years ago, Amazon, Comcast, Twitter and Netflix were effectively taken off the Internet for multiple hours by a DDoS attack because they all relied on a single DNS provider. Can it happen again? ...

October 17, 2018

We're seeing artificial intelligence for IT operations or "AIOps" take center stage in the IT industry. If AIOps hasn't been on your horizon yet, look closely and expect it soon. So what can we expect from automation and AIOps as it becomes more commonplace? ...

October 15, 2018

Use of artificial intelligence (AI) in digital commerce is generally considered a success, according to a survey by Gartner, Inc. About 70 percent of digital commerce organizations surveyed report that their AI projects are very or extremely successful ...

October 12, 2018

Most organizations are adopting or considering adopting machine learning due to its benefits, rather than with the intention to cut people’s jobs, according to the Voice of the Enterprise (VoTE): AI & Machine Learning – Adoption, Drivers and Stakeholders 2018 survey conducted by 451 Research ...

October 11, 2018

AI (Artificial Intelligence) and ML (Machine Learning) are the number one strategic enterprise IT investment priority in 2018 (named by 33% of enterprises), taking the top spot from container management (28%), and clearly leaving behind DevOps pipeline automation (13%), according to new EMA research ...

October 09, 2018

Although Windows and Linux were historically viewed as competitors, modern IT advancements have ensured much needed network availability between these ecosystems for redundancy, fault tolerance, and competitive advantage. Software that offers intelligent availability enables the dynamic transfer of data and its processing to the best execution environment for any given purpose. That may be on-premises, in the cloud, in containers, in Windows, or in Linux ...

October 04, 2018

TEKsystems released the results of its 2018 Forecast Reality Check, measuring the current impact of market conditions on IT initiatives, hiring, salaries and skill needs. Here are some key results ...

October 02, 2018

Retailers that have readily adopted digital technologies have experienced a 6% CAGR revenue growth over a 3-year period, while other retailers that have explored digital without a full commitment to broad implementation experienced flat growth over the same period ...

October 01, 2018

As businesses look to capitalize on the benefits offered by the cloud, we've seen the rise of the DevOps practice which, in common with the cloud, offers businesses the advantages of greater agility, speed, quality and efficiency. However, achieving this agility requires end-to-end visibility based on continuous monitoring of the developed applications as part of the software development life cycle ...