Replatforming Your Organization for the Future of Data

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8 min read

Data is growing at an exponential rate (there will be 44 trillion gigabytes of it by 2020), but that doesn't mean that it's easy to access, analyze, or store.

So, as this massive amount of data becomes an even heavier burden for legacy systems to bear, it's becoming increasingly important that enterprises step into the future of data with a comprehensive data fabric and platform.

However, replatforming is easier said than done. Here, we'll discuss why replatforming is necessary, how you can go about it, and what MapR is doing to redefine modern data architecture.

Data fabric

Why Is Replatforming Necessary?

As already mentioned, a significant driving factor behind enterprises' need to replatform is related to the recent (and growing) data explosion. A tremendous amount of data is being generated every day - 2.5 quintillion bytes to be exact. A significant portion of this amount comes from IoT devices, which an increasing number of enterprises are using. In fact, the amount of data created by any device will reach 847 zettabytes per year by 2021.

And, as data grows and grows, outdated IT infrastructure is strained more and more. With legacy systems, companies can find themselves faced with a multitude of data-related problems, such as:

  • Data that's stuck in separated silos
  • Incompatible data formats
  • Data that's hard to access and replicate
  • Unreliable data storage and security
  • Slow data processing and analysis
  • A limited number of tools that can be used

When striving to overcome those challenges, a small or temporary fix is simply not enough. To successfully solve such problems, companies must replatform their entire IT infrastructure.

As Jim Scott, our Director of Enterprise Strategy and Architecture, says in an article for Infoworld, "the platforms that have dominated enterprise IT for nearly 30 years can no longer handle the workloads needed to drive businesses forward."

Plus, the potential of big data is nowhere close to being fully realized. When asked where he's placing his bets in the future of big data, our CEO John Schroeder told SiliconAngle:

"Machine learning/AI, containers, IoT, and hybrid cloud. There's no way we could have created applications around IoT or machine learning/AI with the technology of 20 years ago. We're probably in the third or fourth inning of big data, and we've got [a] long way ahead."

Machine Learning

Machine learning isn't only limited to industry giants like Google. On the contrary, it's becoming increasingly pervasive in companies' IT infrastructure. In fact, a survey conducted by ServiceNow found that 89 percent of CIOs are using or plan to use machine learning.

With big data, machine learning can be used to manage operations, detect intrusions, cleanse and normalize data, and identify patterns within large amounts of data.

In a blog post for MapR, Ronald van Loon explains that machine learning technology "does not force the user to learn how it can be operated but adapts itself to the user. It will become much more than giving birth to a new interface; it will lead to the formation of enterprise AI."

When implemented correctly and integrated with a comprehensive data platform, machine learning technologies will relieve the burdens placed on data scientists and analysts, thereby enabling them to do their jobs better and more efficiently.

Vital Replatforming Technologies

Scott puts it best: "The replatforming of enterprise IT infrastructure is no small undertaking."

After all, each enterprise can't simply build a new IT infrastructure from scratch. That's why it's important that companies use only the best technologies and tools, which are capable of fully transforming the way they manage data. Let's look at some of them.

Containers and Microservices

Containers have vastly changed the ways in which enterprises handle their data and applications. CIO's Paul Ruben explains them succinctly:

"Put simply, a container consists of an entire runtime environment: an application, plus all its dependencies, libraries and other binaries, and configuration files needed to run it, bundled into one package. By containerizing the application platform and its dependencies, differences in OS distributions and underlying infrastructure are abstracted away."

Containers are an especially unique solution in that they take up a very small amount of storage space (think megabytes, not gigabytes) and can be easily used and started up on an as-needed basis.

Containers also serve as an excellent place to deploy microservices. SmartBear.com gives a pretty good definition of microservices as a "method of developing software applications as a suite of independently deployable, small, modular services in which each service runs a unique process and communicates through a well-defined, lightweight mechanism to serve a business goal."

Microservices have the added benefit of being able to be modified individually without affecting the rest of the application. When microservices are used within containers, the result is a set of incredibly nimble components that are easy to manage, replace, and deploy.

Of course, containers and microservices are not without their drawbacks. As Jack Norris, our Senior Vice President of Data and Applications, points out in an interview with SDxCentral:

"Containers have made it harder in some cases to share data across an organization because if that container goes away, the data running inside of that container goes with it."

So, whether or not containers and microservices are a good fit for an enterprise depends on what that enterprise is trying to achieve. If lightweight, modular applications are the goal, then containers and microservices can provide an excellent solution.

Cloud Integration

Cloud computing is nothing new, and it's already used by the vast majority of companies — according to a 2018 survey conducted by RightScale, a staggering 96 percent of respondents use cloud.

However, using cloud computing is not the same thing as integrating cloud computing with your data. In a conversation with DevOps, Scott says that integrating the cloud with a data platform can "help enable customers to benefit from the concept of cloud neutrality."

Cloud neutrality essentially refers to a healthy amount of competition within the realm of cloud computing — every customer should be able to use whichever service they desire, without being prevented from using other services as well. A data platform with built-in cloud integration ensures this.

As an additional benefit, cloud integration also allows users to mirror data between on-premises and cloud infrastructures.

Replatforming with MapR

MapR's revolutionary data platform makes it easier than ever for enterprises to replatform their IT infrastructure.

Synchronoss, a prominent software company based out of New Jersey, moved to our data platform in an effort to replatform its infrastructure. Suren Nathan, the company's Vice President of Engineering, Analytics, and Digital Transformation, says of the change: "When you have the tools to manage things, the rest takes care of itself."

With the help of MapR, companies can move beyond inadequate legacy infrastructures to:

  • Create an edge-to-edge data fabric for managing files and containers
  • Benefit from machine learning and artificial intelligence technologies
  • Reliably secure and store data
  • Access data from anywhere, at any time
  • Analyze and process data in real time
  • Utilize open source engines and tools
  • Deploy large-scale applications
  • Scale up and down as needed

As data continues to boom and outdated infrastructures continue to keep up, the question is not will enterprises need to replatform, but when.


This blog post was published August 13, 2018.
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