MapR Big Data Pricing & Demo

MapR big data software platform that improves the efficiency of AI/ML applications with its scalable databases, big data storage, and advanced analytics. It’s suitable for data scientists looking for an integrated solution that can handle distributed object storage, NoSQL databases, and Apache Kafka Event store, all at once. MapR offers open-source SDK, cloud deployment, and platform APIs. 

MapR Pricing:

While the MapR platform is mostly open-source, it charges for additional services for getting businesses to speed with big data requirements. MapR pricing is available on a quote by quote basis and can be obtained by contacting the MapR sales team to select the paid plan.

MapR Big Data Software Demo:

It offers a free demo of the platform in a number of ways, including a live walk-through, a sandbox environment, an on-premise client, or a cloud VM. Know more about the MapR demo and MapR Pricing at the MapR website. 

MapR Big Data Software Features:

The noteworthy features that make MapR a reliable Big Data Management platform

• Data Security and Governance

• The platform employs industry-grade authorization, authentication, and audit policies for improved security.
• MapR comes with Kerberos protocol, which uses cryptography for achieving network security.

• Reliable Storage and Continuity Measures

• Users can leverage all the product offerings in a highly scalable fashion. 
• Contingency measures such as failover and fallback support are also integrated into all the products.

• Data Tiering for better usability

• MapR achieves reduced costs and higher workload efficiencies by specifically treating each data set according to its processing needs.  
• This improves resource usage and reflects in its performance efficiency. 

• Distributed Computing

• MapR uses distributed computing for creating highly available (HA) architecture that makes big data tasks faster for professionals.
• The storage system can operate at an exabyte level, offering scalability and reliability, at the same time.