Database For Global Data-Intensive Applications

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How a modern scalable database can be a key element to operationalize big data with contextual user engagement and intelligent business processes


Today's digital economy demands a new way of running business. Data first thinking and real-time response are key for organizations to outperform the competition. Organizations are trying to provide contextual digital user experiences to attract, engage, and retain customers. They are spending significant efforts to modernize the core business processes, uncover real-time insights, and enable automated decision making to reduce costs and innovate faster. Data is the foundational enabler in all these next generation “data-intensive” applications and databases are the foundational components of application architecture. MapR has unique vision and database technology to enable these breakthrough applications by operationalizing data across on-premises, edge, and multi-cloud environments with no complex tradeoffs or compromises.


Enabling digital transformation requires organizations to deal with extreme scale of data and applications. Modern applications need the ability to capture and interact with diverse types of data such as IoT and leverage them to maximize their business outcomes. In addition to deriving value from new datatypes, organizations need to move their core business applications from after-the-fact insights and processing to real-time processing and in-the-moment proactive actions by infusing Ml/AI driven data intelligence. Without a scalable and reliable database technology to apply analytic insights to business in real-time, much of the value of digital transformation initiatives is lost.


Historically, databases were not designed for building data-intensive applications. The following often come up as the key concerns in organizations when evaluating databases.

  1. Databases are no longer islands. In order to enable next-generation applications, the database must be part of a broader data platform that includes analytic processing for in-place intelligence and streaming for real-time data flows to establish a continuum of data, insights, and operations coming together.
  2. Database selection at organizations today is often a decision of complex tradeoffs of critical requirements. Is write ingest speed critical or read? Is data consistency or performance critical? Is it OK to lose the data because app availability is more important? Do I need wide column performance or JSON flexibility? The result is that while there are many new databases on the market, they often are used in niche purpose-built use cases or as part of auxiliary applications while most of the mission-critical business apps stay in well trusted traditional RDBMS systems not leveraging modern technology trends, thus not fully achieving the desired business transformation.
  3. Every database app is a silo in its own infrastructure, which leads to hundreds to thousands of non-integrated apps a daunting reality. Databases traditionally are not built for running multiple apps simultaneously while still meeting SLAs. The result is greater complexity and infrastructure costs and the need to interconnect these apps using fragile data pipelines.


MapR has developed critical success criteria for selecting database solutions based on our experience crafting modern data management and application strategies with global organizations. These ten critical issues must be addressed in order to attain the degree of capability needed by organizations aiming to develop a core competency in operationalizing their data:

  1. Multi-Model Flexibility to handle diverse datatypes on a unified foundation and give developers choice.
  2. Simplicity and Speed of Application Development to embrace the JSON simplicity and schema-flexibility end to end from database to application development to query layer.
  3. Strong Data Consistency and No Data Loss to serve as a system of record.
  4. Extreme Performance for All Workloads to enable realtime experiences and processes. It is not sufficient to excel in either ingest speeds or read speeds.
  5. Vast Scalability to scale in any dimension without limits and without impacting SLAs—thousands of nodes, trillions of documents, and millions of tables.
  6. Extreme High Availability to enable “always-on” business.
  7. Global Applications to synchronize data between an arbitrary number of separate clusters and seamlessly span on-premises, edge, and multi-cloud environments to form a global database for new age apps.
  8. Multi-Tenancy and Fine Grained Security Built In to support a common architecture across the platform to securely run hundreds to thousands of apps without impacting each other.
  9. In-Place Analytics to allow “in-place” data exploration, BI, machine learning, and data processing without moving data.
  10. Out-of-the-Box Global Pub/Sub Stream Transport to ingest and process data in real time and integrate applications.
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Database For Global Data-Intensive Applications