Dataware for data-driven transformation

MapR Database for Global Data-Intensive Applications

MapR Database is a high performance NoSQL (“Not Only SQL”) database management system built into the MapR Data Platform. It is a highly scalable multi-model database that brings together operations and analytics, and real-time streaming and database workloads to enable a broader set of next-generation data-intensive applications in organizations.


Today’s digital economy demands a new way of running business. “Data first” thinking and real-time response are becoming key for organizations to outperform the competition. Organizations are trying to build next-gen applications that provide rich contextual digital user experiences to attract, engage, and retain the customers. They are spending significant efforts on modernizing the core business processes, uncovering real-time insights, and enabling automated decision making to cut down costs and innovate faster.

MapR Database is an extremely scalable, reliable, globally distributed database. Built into the MapR Data Platform, MapR DatabaseTop 10 Reasons Developers Choose MapR Database supports the most stringent speed, scale, and reliability requirements without compromises across multiple edge, on-premises, and cloud environments. It is the NoSQL database for building powerful, intelligent, and mission-critical applications.

Top 10 Reasons Developers Choose MapR Database


Multi-Model Flexibility

MapR Database supports multiple data models including wide-column, document, key value, and time-series on a unified foundation.

Native JSON Simplicity with Expressive Queries

MapR Database is a highly scalable document database with native JSON support. It provides intuitive and expressive OJAI query language to build powerful applications.

Extreme Performance and Effortless Horizontal Scale

In recent benchmarks validated by ESG, MapR Database was observed to be 2.5X faster than Cassandra and 5.5X faster than HBase on average, across all workloads.

Strong Consistency No Data Loss

MapR Database has strong consistency by default and always. MapR Database has in-sync replication (factor 3) always on, and once data is acknowledged, it will never be lost or corrupted.

Extreme High Availability

MapR Database inherits the enterprise features of underlying platform with production ready failure

Global Multi-Master Replication

MapR Database supports immediate replication of write operations from any active MapR Database cluster to other active MapR Database clusters (active/active)..

Optimized Multi-Tenancy for 1000s of Apps

MapR platform provides volume and topology based placement controls to enable multiple MapR Database applications to run securely and independently in the same cluster.

In-Place SQL and Advanced Analytics/ML

MapR Database is natively integrated with machine learning and analytical processing to enable advanced analytics, data exploration, and interactive SQL.

Integrated Streaming for Real-Time Data Ingest, Processing, and Integration

MapR Database is integrated with MapR-ES out of the box for real-time data flows. MapR-ES is a global event streaming system that enables real-time data ingestion and stream processing.

Robust Security and Fine Grained Access Control

MapR Database allows security policies on the sub-document and the element level.You can set strict policies only for the confidential elements, instead of whole documents.

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How Your Business Benefits from MapR Database

New Revenue Opportunities

The native JSON data model and the multi-model flexibility allows developers to choose the appropriate model for a specific use case. As a result, the development process is simplified and time-to-market for new applications and features is reduced. New applications and features can drive significant revenue opportunities for organizations.

Engaged Customers

Personalized and contextual user experience built with MapR Database can drive higher user engagement and improve net promoter score (NPS). Happy and engaged customers mean less churn, more lifetime customer value, and new products received well by customers.

Intelligent Business Processes

With in-place analytics and machine learning, MapR Database enables intelligent applications that can automate decision making in real-time. Organizations benefit from intelligent business processes that require less manual intervention, accelerate decision making, and foster innovation. It also reduces event-to-action cycle with real-time decision making.

Always-On Business

MapR Database is built on the fast, scalable, and reliable foundation provided by the MapR Data Platform. The MapR Platform provides enterprise-grade high availability and disaster recovery for business continuity. The platform also provides snapshots to allow applications to recover from user errors and data corruption.

Lower TCO

MapR Database benefits from the effortless scalability and volume/topology-based multi-tenancy of the underlying platform. Effortless horizontal scale reduces the hardware and administrative expense. Multi-tenancy allows multiple applications to run securely and independently in the same cluster, reducing the hardware cost further.

Instant Business Insights

MapR Database is integrated with MapR Event Store for Apache Kafka out of the box. MapR Event Store is a global event streaming system that enables real-time stream processing embedded in MapR Database applications. As a result of data being made available and analyzed in real-time using MapR Event Store, BI teams across the organization can deliver instant business insights and enable immediate actions.

MapR Database Capabilities Overview


Use Cases

Core Business Applications

These applications are fundamental to running a business. For example, fraud prevention is a key process for payment processing. Inventory management, risk analysis, churn detection, and biometric verifications are examples core business applications. Organizations want to build next-gen core applications that support real-time business processes and optimize these business processes using analytics/ML.


  • Performance and scalability issues with RDBMS systems
  • Data is not available to be processed in real time
  • Big data systems fail to provide mission critical SLA guarantees
  • Stitching together multiple technologies with fragile pipelines

Why MapR Database

  • MapR Database is built on the MapR Data Platform and scales linearly
  • Integrating streaming and analytics/ML enable real-time data processing
  • Mission critical reliability and performance at scale with mixed workloads
  • Global multi-master replication for business continuity

Single View

Businesses typically use multiple enterprise applications, which means data related to a single business entity can often lie in multiple data silos. “Single view” means providing one place to find all information about a business entity. The most common example of this use case is Customer 360.


  • Data is created in different formats, at different speeds
  • Scalability of traditional systems
  • Multiple-silos of information
  • Data is not available in real-time

Why MapR Database

  • Native JSON schema flexibility to handle different sources
  • Granular, efficient operations providing high performance and throughput
  • Effortless scale to handle trillions of documents, millions of tables
  • Fine grained security to manage who can access data
  • Expressive and efficient ultra low latency queries via OJAI and native secondary indexes
  • Integrated streaming to build real-time views
  • Built-in analytics/ML for predictive user experiences

Operational Data Hub/Real-Time BI

Bring all data together in a data hub in real-time to provide real-time business insights. MapR Database is required for what sounds like a data lake use case due to the frequently changing data from applications and transactional systems. In data lakes, relational data would typically be stored in Parquet or Avro files and accessed through Hive. Parquet is a write-once file format, which means once written, the file cannot be updated. Therefore, frequently changing data can be stored and updated in MapR Database.


  • Multiple data sources, variety of data formats
  • Frequently changing data
  • Complex and time consuming ETL processes
  • Complexity of landing data in real time

Why MapR Database

  • Schema-less flexibility for dynamically changing schemas
  • Multi-model support for multiple data formats
  • Extremely high throughput that can easily ingest frequently changing data
  • Integrated stream processing for real-time data transformation and ingest
  • ANSI SQL and BI tools integration through Drill
  • Built-in analytics and ML with Spark and Hadoop

For building analytics-as-a-service, MapR Database provides ultra low latency performance, effortless scale, and high availability. Many MapR customers report extreme high availability and uptime.


IoT refers to applications that primarily operate on data created by sensors, devices, and machines. The use cases include predictive maintenance, real-time operations dashboards, alerting, real-time tuning of devices, and quality assurance.


  • Data is created across various geographic locations, in different formats
  • Extreme speed of data creation across millions of devices
  • Data security across multiple channels and data stores

Why MapR Database

  • Extreme ingest speeds
  • Schema-less model to accommodate different data formats
  • Effortless scale to accommodate massive volumes of data
  • Integrated streaming for real-time processing
  • Integrated edge computing for analyzing information at the edge
  • Support for time-series data
  • End-to-end data security

Contextual User Experience

This use case involves customizing user experience based on user activity. Common examples include personalized recommendations on video-on-demand services and e-commerce.


  • Capturing, consumption, and analysis of massive volumes of user activity
  • Real-time analysis and decision making

Why MapR Database

  • Easily manage massive volumes of dynamic data for analysis of user activity
  • Multi-data center and cloud replication for always-on operations
  • Ultra low latency performance at extreme scale (billions of requests per day)
  • Built-in analytics and ML
  • Integrated streaming to capture and process data in real-time

List/Metadata Management

This use case consists of catalogs or other metadata used to manage key business entities from enterprises and online services. These entities could be SKUs, inventory parts, sensors, stock trades, playlists, or measurement results.


  • Adding new attributes to existing items could require restructuring of schema in RDBMS
  • Database scale
  • Lacking flexible queries on large-scale datasets

Why MapR Database

  • Dynamic schemas and JSON flexibility make adding a new attribute to existing items easy
  • Effortless scale and high performance
  • Multi-data center and cloud replication for always-on operations
  • Fine grained security controls
  • Flexible and efficient queries on any field via secondary indexes
  • ANSI SQL and BI integration with Drill for complex querying

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Case Studies

Technical Documentation:

Perspective Series


In the press