A Faster, Cheaper Path to Hybrid Cloud with MapR Inside

Lower Cloud Costs. Avoid Cloud Lock-In. Future Proof Your Applications.


4 Critical Mistakes to Avoid on Your Journey to the Public Cloud

When it comes to AI and ML, the more high-quality data you’re training against the better. Yet cloud vendors have limited offerings at the edge, where much of your organization’s data will be created and analyzed in the years ahead.

With MapR in the cloud you can:

  • Data Scientist - IoT source
    Run any model close to the IoT source
  • Data Scientist - ML and AI Containerize AI and ML models and train them against all data
  • Data Scientist - faster to production Rate your models and push the best ones to production faster

If your organization is like most, you already have dozens, if not hundreds, of applications that leverage open APIs like POSIX and Kafka, which you’d need to rewrite to leverage cloud-specific services.

With MapR in the cloud you can:

  • Developers - applications anywhere Write applications once and run them anywhere, including other clouds
  • Developers - legacy support Continue to run legacy applications, like NAS-based apps, as is
  • Developers - stay ahead Spin up and test next-gen applications quickly and affordably

Cloud costs can grow rapidly and unexpectedly, especially as your cloud use cases increase. Plus, cloud vendors lock you in to their proprietary APIs, making it time consuming and expensive to move to best-of-breed services from other clouds.

With MapR in the cloud you can:

  • CxOs - lower bills 30% Lower annual cloud billings by up to 30%
  • CxOs - lower admin costs Lower administration costs
  • CxOs - avoid lock-in Avoid cloud lock-in
  • CxOs - go hybrid faster Go hybrid or multi-cloud faster

Which problem will you solve with MapR inside your public cloud?

The average enterprise has dozens or more existing applications running on-premises. These applications are typically written against open APIs, so making them work with cloud-specific services and APIs is not a simple matter of moving existing code to your cloud of choice. The MapR Data Platform solves this problem by supporting open APIs. By using MapR inside your public cloud, your dream of lift and shift becomes a reality.

MapR supports a wide range of open APIs, including NFS, POSIX, ReST, HDFS, HBase, JSON, and Kafka. Applications written against these APIs can continue running "as is" with MapR in the cloud. Without MapR, you would be forced to spend time and money rewriting your existing applications to work against cloud-specific services - that’s time and money better spent elsewhere. And, as your organization gets locked into a given cloud, so too do your developers’ skills.

There is widespread belief that moving to the public cloud lowers your IT costs. Though this might be true when you're getting started and experimenting with cloud services, cloud costs can quickly spiral out of control, with bandwidth and storage costs increasing rapidly as your use cases grow in number and complexity. With MapR inside your public cloud, you retain greater control of your cloud costs and can even reduce these costs by leveraging features that help minimize storage and bandwidth consumption.

MapR supports inline data compression ranging from 2x (lz4 compression) to 3x (zlib compression), which results in significant cost savings by using less disk-space and bandwidth, both of which get expensive quickly in most public cloud offerings. Additionally, by automatically tiering cold data to S3 cloud storage (available in the 6.1 release), costs are perfectly optimized for multi-temperature environments - all while keeping metadata in a single, unified namespace for easy accessibility and recall. Finally, eliminate the need to stitch together files, tables, and streams, and lower administration costs by using just a single cluster to store and analyze data in one place. With MapR in the cloud, you can meet production SLAs at a lower cost by not having to move data around.

Why choose just one public cloud vendor when you can choose them all? With the MapR Data Platform, your infrastructure, data, and applications can span edge, on-premises, and one or more clouds. This flexibility lets you maximize cost savings by leveraging cloud offerings with the lowest infrastructure prices. It also powers better disaster recovery and business continuity for your organization by lowering dependence on the infrastructure services of a single cloud vendor.

MapR’s unique capabilities make your hybrid and multi-cloud deployments possible. Consistent snapshots take a point-in-time picture of your files in one deployment (e.g., one public cloud), and mirrors copies those files to another deployment (e.g., another public cloud). Global replication similarly copies changes to tables and streams from one MapR deployment to another in real-time. And a unified, global namespace gives you a single view of all your data no matter where it sits.

Estimates at the low-end predict nearly 25 billion IoT devices by 2020. Few of these devices will have immediate access to cloud infrastructure or processing capabilities. MapR Edge addresses the need to capture and analyze data generated by IoT devices close to the source. MapR Edge provides secure local processing, quick aggregation of insights on a global basis, and the ability to push intelligence back to the edge for a faster and more significant business impact. With MapR Edge, you can act locally and learn globally.

Several unique capabilities from MapR let you simultaneously harness the power of data at the Edge while leveraging the power of the cloud. Optimized for IoT and edge environments, MapR Edge lets you store, process, and analyze data even when space is constrained or network connections are poor or unavailable. A single, unified namespace ties it all together, giving you a single view into all your data wherever it might be - edge, on-premises, and one or more clouds. Finally, the Edge to Cloud File Migrate feature lets you automatically move files from a MapR Edge cluster to cloud object storage in real time.

Before [MapR], we would have to schedule developer time to write a query to see if we even have data. The whole process could take months. Being able to scale the environment quickly and cost effectively helps us turn around answers to our customers’ questions much faster. Today we can cut through our data and run queries...and get the answer in a day, or even hours.

Terry Schutte, IDEXX


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