Why MapR?

Data is your most important digital asset, so make sure you treat it like a first-class citizen!

Successful data-driven transformers have a secret of success. They are calling it dataware: A new layer in the enterprise stack that radically simplifies the path to unlocking the value of their data.

Whether it's data at rest or data in motion, the future is about getting business value from all your data faster. Our goal is to help you avoid a costly patchwork of data silos with dataware that powers your data-intensive AI apps and puts both data and insight wherever you need it most – at the edge, in the cloud, in a data center, on an oil rig, in your car, at the scanner at the grocery store.

At MapR, we've created a data platform that harnesses, manages, protects all your data, and powers the next generation of AI and analytics applications that are essential for data-driven transformation.

Dataware stack

What Is Dataware?

Dataware is a new layer of the Enterprise IT stack an abstraction layer that allows data to be managed as a first-class enterprise resource decoupled from any other dependencies. It solves the challenges of today's complex data environment by letting you manage your entire data ecosystem from one platform.

The Industry's Next Generation Data Platform for AI & Analytics

Avoid a costly patchwork of data silos by implementing dataware, a new layer in the software stack that lets you consume, manage, analyze, and power your data-driven apps on an enterprise scale.

Dataware puts data and intelligence wherever you need it most.

With dataware, you can focus more on leveraging your data instead of managing your data.

Dataware provides a consistent approach to enable the consumption of a wide variety of data from files, database tables to event streaming data. It makes it simple to secure and manage and orchestrate your data no matter where it is. It provides a consistent way to deal with a myriad of issues which otherwise need many point solutions which only add to complexity.

The MapR Data Platform delivers the power of dataware by addressing key problems:

  1. Rapid proliferation of applications and tools. Organizations today might be running dozens or even hundreds of applications, each supported by a growing number of tools that need access to data. It is critical that an organization's data be accessible to these applications and tools in an open, yet secure way - wherever the data might be.

  2. Data silos make it difficult to orchestrate data. Traditionally, data has been sitting in disparate systems across the organization. In order for this data to be used by the people and applications that need it most, this data often had to be moved around through time-intensive, complex\ operations. Consistently securing and governing this data as it's moved around, in turn, quickly becomes untenable. Organizations need a simple way to orchestrate, secure, and govern all their data.

  3. Hard to manage data from edge to cloud. Data today sits in on-premises environments, in one or more clouds, and at the edge - where the vast majority of data will be generated in the coming years. Historically, it has been extremely difficult to administer and process all of this data consistently, given that each deployment "repository" has it's own unique set of controls and management functionality. Organizations need a simple, consistent way to manage their data across diverse environments.

From ingestion to processing, dataware optimizes the entire data lifecycle to power applications that simultaneously require real-time analytics, machine learning, and AI. Dataware provides complete flexibility in leveraging the underlying infrastructure, whether it's on-premises, multi-cloud, hybrid, or a containerized infrastructure.

The MapR Data Platform solves these issues through a visionary architecture which does the following critical functions:

Key Dataware Function MapR Unique Value
Universal access to data A broad range of enterprise data is accessible by different applications, groups or team members in a secure way globally.
Data workload independence Supports multiple processing technologies on one platform with access to platform-wide data unlike others.
Global data multi-tenancy Supports groups and users with secure access to only the data they are enabled for. Auditable logs. Other platforms do not do this.
Data processing isolation Processing and data can be placed as desired for performance, or regulatory needs to specific racks or locations, or GPU enabled systems delivering optimized performance.
Data security Highly distributed architecture provides data protection and point in time recovery for files, database, stream data on a global basis. Data is encrypted in motion & at rest
Data performance & temperature management Automated multi-temperature data management to move data based on policies to warm or cold tiers into S3 Cloud while maintaining metadata whilst providing global transparent access to user applications.
Data portability Data can be seamlessly moved between on-premises to one or more clouds for disaster recovery, archiving, cloud bursting or supporting required end use application agility.
Global Data Deployment Fully distributed system can be deployed in multiple locations including embedded IoT applications, and in multiple clouds or premises.

Dataware gives you end-to-end control over data security, data placement, data access, data tenancy, and data controls completely independent from any other layer in the enterprise IT stack. This reduces your TCO for managing data across silos, workloads, and tools and, perhaps more importantly, gives you the ability to gain competitive insights from data even faster.

The MapR Data Platform delivers the power of dataware to fuel data-driven innovation. The MapR Data Platform was built from the ground up to handle the diversity of data types and compute engines for AI and analytics in an increasingly streaming and real-time world.

MapR: The industry's next-generation data platform for AI and analytics.

WEBINAR

Data Management for AI and Machine Learning: Putting Dataware to Work