Dataware for data-driven transformation

Data Fabric: The Future of Data Management

Contributed by

7 min read

"Data fabric" is a relatively new term, but its importance is not. For years, enterprises have struggled to integrate all their data into a single, scalable platform. A data fabric simply describes a comprehensive way to achieve that goal.

And, with data growing at a rate of 40 percent per year (and not showing any signs of slowing down), achieving that goal is more important than ever.

Let's learn more about what a data fabric is, why you should care, and how MapR is offering a data fabric that's more advanced than any other.

What Is Data Fabric?

As companies expand, use an increasing number of applications, and collect data into more and more silos, their need for a better data solution grows exponentially.

When data is isolated and relegated to silos, it becomes stagnant and inaccessible. Legacy systems only make it more difficult to access data, and the result is lowered productivity and efficiency.

Plus, it's not only silos that separate data — even state-of-the-art technologies like data centers and the cloud can inadvertently work to divide data into inconvenient clusters.

The cloud is particularly troublesome because it makes it difficult for companies to leverage data from public clouds as well as that which is stored on-site.

EnterpriseTech provides an excellent illustration of this problem:

"For example, you might have an analyst in Cleveland who needs to run a query on an exception report generated by a robotic arm in Munich, Germany. To detect anomalies, the data from that robotic arm needs to be consistent with your data warehouse. With global strong consistency, your data is consistent between the device – in this case the robotic arm – and the data center. The data fabric ensures this data is available and consistent regardless of where it originates and where it is accessed."

Just like an actual piece of fabric, a data fabric can be placed over a wide expanse of space. That's the main idea behind data fabrics, which can encompass a wide variety of data locations and sources.

What a data fabric does is enable the processing, management, analysis, and storage of almost any amount of data from a multitude of sources. The data fabric then enables applications and tools to access that data using an array of interfaces. It's also important to note that data fabrics leverage data in real time.

Why Should You Care?

As we mentioned above, enterprises to whom data is important often find that their current systems are too slow, too divided, and inefficient.

And, in larger companies, having multiple groups of people store and retrieve data in their own way results in a fragmented network of data that's far from unified.

Unlike other solutions, a data fabric is not a band-aid fix. Rather, it's a permanent and scalable way to bring all your data under the umbrella of one unified platform.

A data fabric is able to solve a number of problems, such as:

  1. Low data availability
  2. Little to no reliability in terms of storage and security
  3. Siloed data
  4. Poor scalability that's unable to adapt to different amounts of data
  5. Reliance on underperforming legacy systems

What Makes MapR's Data Fabric Unique?

MapR's Vice President of Technology Strategy, Crystal Valentine, puts it best in an interview with Forbes:

"From our founding, MapR has had a singular focus on building a platform with the capabilities to support next-gen applications. Our vision of a future in which applications can leverage large amounts of distributed data in real time to provide enormous competitive advantages is quickly becoming reality, and our strong growth numbers are testament to the fact that that is resonating with our customers."

Although other data fabric solutions are available, most focus on more basic functions, such as:

  • Storing and retrieving data from various sources
  • Accessing those sources, using one standard method or application program interface (API)
  • Integrating data both within and across sources
  • Applying processing or real-time analytics to data from any source
  • Syncing data with cloud storage
  • Providing backup and disaster recovery (DR) support

MapR's data fabric goes above and beyond by allowing you to:

  • Unify data silos
  • Deploy anywhere
  • Scale up
  • Replicate data across data centers, edge devices, and cloud instances
  • Search and explore data with Apache Drill
  • Safely secure your data with a single security model with granular controls
  • Easily move exabytes of data across edge, on-premises, and cloud deployments
  • ●Extend IoT applications to remote locations
  • Increase your data transfer speed
  • Make faster decisions
  • Effortlessly access data.
  • Reduce both operational and capital expenses

Plus, our roster of high-profile clients, who are winning at data with the MapR Data Fabric, speaks for itself. Our customers include Hewlett-Packard, SAP SE, NTT Security, Mediahub, and more.

In an article for DZone, Dan Kusnetzky says:

"In my industry research, I've come across a number of definitions of the data fabric notion. So far, the one being put forward by MapR Technologies seems to be the most comprehensive. Furthermore, the company has been enjoying some level of success as it persuades customers to adopt its approach. The company appears to understand the importance of distributed processing in the era of mobile, cloud, and the fast proliferation of IoT and how all of these are important members of enterprise computing applications. They also appear to understand that these devices also represent potential threats and have discussed how to bring them in safely. The company has also considered where and how data should be stored so that it is available broadly, but still secure and reliable."

Essentially, MapR goes beyond the basic requirements of a data fabric to provide our customers with a truly revolutionary product that makes data analysis, storage, and security fast, reliable, and easy.


This blog post was published August 08, 2018.
Categories

50,000+ of the smartest have already joined!

Stay ahead of the bleeding edge...get the best of Big Data in your inbox.


Get our latest posts in your inbox

Subscribe Now