As fraud and security breaches are becoming more frequent and sophisticated, traditional security solutions are not able to protect company assets. MapR enables organizations to do real-time analysis on an unlimited amount of data of any type. Security teams can widen the scale and accelerate the speed of threat analysis, and improve risk assessment by building sophisticated machine learning models.
Note: If you’re seeking information on product-level security features in the MapR Converged Data Platform, please see our webpage on Hadoop and Big Data Security.
Architecture for Security and Risk Management
Specific use cases include protecting against infrastructure risks as well as consumer-oriented risks across different industries:
- Security Information and Event Management (SIEM): Analyze and correlate large amounts of real-time data from network and security devices to manage internal and external security threats, improve incident response time and compliance reporting.
- Application Log Monitoring: Improve analysis of application log data to better manage system resource utilization, security vulnerabilities, and diagnose or preempt production application problems.
- Network Intrusion Detection: Monitor and analyze network traffic to detect, identify, and report on suspicious activity or intrusions.
- Fraud Detection: Use pattern/anomaly recognition on larger volumes and greater variety of data to detect and prevent fraudulent activities by internal or external parties.
- Risk Modeling: Improve risk assessment and associated scoring by building sophisticated machine learning models on Hadoop that can take into account hundreds or even thousands of indicators.
- Easy data ingestion: Copying data to and from the MapR cluster is as simple as copying data to a standard file system using the Direct Access NFS™ capabilities of the MapR Converged Data Platform. Applications can therefore ingest data directly into the MapR cluster in real time without the need for staging areas or redundant clusters just to ingest data.
- Existing applications work: Due to the POSIX-compliant MapR File System integrated into the MapR Converge Data Platform, any application works directly on MapR without undergoing code changes. Existing tools, scripts, custom utilities and applications are good to go on day one.
- Multi-tenancy: Support multiple user groups, any and all enterprise data sets, and multiple applications in the same cluster. Data modelers, developers and analysts can all work in unison on the same cluster without stepping on each other's toes.
- Business continuity: The MapR Converged Data Platform provides integrated high availability (HA), data protection, and disaster recovery (DR) capabilities to protect against both hardware failure as well as site-wide failure.
- Global scale: Scalability is key to the MapR Converged Data Platform so the analytics can operate at both data-at-rest and data-in-motion. MapR provides the only data platform that scales to trillions of files, millions of event streams and petabytes of raw data without compromising performance.
- High performance: The MapR Converged Data Platform was designed for high performance with respect to both high throughput and low latency for Apache Hadoop and Apache Spark applications. In addition, Apache Hadoop running on the MapR platform requires significantly fewer servers versus other Hadoop distributions, leading to architectural simplicity and lower capital and operational expenses.
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