Unstructured Data: Cloud to the Rescue… Right?

One way to overcome a lot of the previously mentioned challenges and concerns with storing such large amounts of unstructured data is to move it to a public cloud provider. As for the previously discussed challenges of legacy NAS filers, most of those concerns would no longer be applicable. Essentially, you would be paying the cloud provider to worry about all the infrastructure concerns like capacity, performance, uptime, H/W support, and so on.

Should the dreaded day that your organization suffers a ransomware attack arrive, the severity of the damage could vary from a simple file system encryption to a total lockout. Depending upon the misfortune your IT team is dealt with, the recovery process could possibly consist of a total wipe of the servers, storage, backups, especially critical systems, logical configurations, and property. The most difficult thing to swallow is when you thought you had everything covered in your disaster recovery or backup systems—only to find out the opposite is true.

In prior blog posts, I’ve explained the difference between unstructured, structured, and semi-structured data, and shared the value of unstructured data. In this blog post, I would like to focus on what I believe are the primary barriers preventing organizations from tapping into their unstructured data for analysis.

Performance, Capacity, Scalability, Availability, Security, and Recoverability: These are all pillars of a multi-cloud data center strategy. In the past, applications were dependent on the on-premises data center infrastructure. “What you see is what you get” was pretty much how application developers had to deal with it. Today, the business dictates how an organization deploys data center resources to support an application. For a commercial business, the desire is for the application to generate significantly large amounts of data traffic that translates into revenue. For the public sector, it’s about getting information to and from the community at a rapid pace.

In my last blog post, I explained the value of unstructured data. In this post, I'd like to define structured and semi-structured data to further explain the meaning of unstructured data.

When employees were first sent to work from home, organizations scrambled to provide work-from-home software, devices, and infrastructure. As we transition to a new normal, it’s now important to reevaluate the initial infrastructure laid to support remote workers against a lasting, best-practice architecture. In the last entry, I shared the first three pillars for a thriving remote workforce. Let’s now explore the final three pillars.

Operating a remote workforce is no longer just a remote possibility. A work-from-home war is being quietly waged across many organizations throughout the country, as workers who were forced to quickly establish home office environments now wish to remain in their home rather than return to commuting to an office. This tipping point has deep and wide ramifications for organizations attempting to return to pre-COVID-19 productivity levels. 

I was recently asked by a colleague to build a portal to allow engineers to start and stop two Amazon EC2 instances that host software used for network discovery and mapping. The intent was to only have them running when necessary to reduce the cost of instances running when they are not in use.  Jokingly, I asked if he wanted me to build this portal on an EC2 instance. He didn’t catch the joke, so I explained that running one full-time instance to control two other instances was probably not going to be very effective at saving money.

Every 11 seconds, someone within the United States is infected with ransomware. That means that by the time you finish reading this blog post, almost 30 people will have their data locked and encrypted unless they pay a cybercriminal a ransom of hundreds or thousands of dollars (hopefully it’s not YOU!).

Everyone has heard about how fast data growth is expanding in our world the past few years. More and more data is being generated, and organizations are being asked to retain that data for longer periods of time. The reason for this data explosion should not be a mystery, as there are multiple popular modern technologies that are at the heart of it. We live in the age of the Internet of Things (IoT), where a dizzying array of machines, devices, and endpoints are connected to the internet and actively collecting, creating, and sharing more and more data.