MapR Announces New Solutions; Expands its Services Reach

By CIOReview | Friday, July 3, 2015

SAN FRANSCISCO, CA: MapR, a provider of Apache Hadoop distribution, announces new auto-provisioning template and Spark based quick start solutions for Apache Hadoop as the company marches ahead delivering the solutions for companies such as Liaison Technologies and Razorsight.

The New Offerings:

Auto-Provisioning Templates
MapR Auto-Provisioning Templates is a software module to accelerate the provisioning and deployment of big data solutions based on the MapR distribution including Apache Hadoop. It applies software-defined concepts that enable organizations to deploy appliance-like convenience along with flexibility and choice of hardware to build a customized enterprise-grade data platform.

Auto-Provisioning Templates support configurations for data lakes, data exploration environments and operational analytics. Users can deploy templates via MapR installer that provides auto layout, rack awareness and health checks.

Quick Start Solutions
MapR announces three new Apache Spark-based Quick Start Solutions for MapR distributions including Apache Hadoop. The solutions enable faster development of real-time big data applications on log data for security analytics, time-series data for real-time dashboards as well as to build clinical applications on human genome data.

Real-time Security Log Analytics: This solution will enable security teams to accelerate deployment leveraging Spark on MapR to gain visibility into their environment and detect anomalous behavior as quickly as possible.

Time Series Analytics: Devices and sensors that are continuously transmitting data points are often associated with time component and IoT is adding to the rate of generated data. Application developers and architects can leverage the Time Series Analytics solution to quickly develop and deploy real-time alerting and dashboarding applications on time-series data. The solution unites reliable NoSQL database, MapR-DB together with Apache Spark to support real-time aggregation capabilities.

Genome Sequencing: The Genome Sequencing Solution leverages Apache Hadoop and Spark enabling organizations to accelerate deployment. It reduces latency of converting a sequenced genome to clinically actionable information, decreases costs and improves reliability and performance of the system.

The company’s offerings have been recently implemented by companies such as Liaison Technologies and Razorsight

Liaison Technologies and MapR

Liaison Technologies, a cloud-based data solutions provider, announces using of MapR Distribution including Hadoop in its ALLOY platform –a data Platform as a Service (dPaaS) integration and data management solution – to support big data integration and management.

MapR adds increased scalability, performance and flexibility for managing big data to the ALLOY platform through its enterprise-class Hadoop Distribution. Building ALLOY on big data technologies also allows for virtually limitless scale, providing a future-proof solution.

“MapR has some very unique operational capabilities and a superior distributed data platform with MapR-FS and MapR-DB. Their technology allows us to choose the best approach and tools for each customer and use case,” said Brad Anderson, VP, Big Data Informatics, Liaison Technologies. “They give the ALLOY platform a significant technical advantage.”

Razorsight Implements MapR Distribution

Razorsight, a provider of cloud-based predictive analytics solutions, is utilizing MapR Distribution including Hadoop, along with Apache Spark to take advantage of big data storage and compute.

A central data lake for online and archive data is built using MapR. To move datasets in and out of the cluster seamlessly Razorsight leverages MapR NFS gateway. Spark as an in-memory processing engine handles enriching and transforming the source data to prepare records for advanced modeling. The new platform has enabled Razorsight to expand into new solution areas for its telecommunications service provider customers.

“We selected MapR for several reasons.  First, having the flexibility of the full Spark stack as part of the Hadoop distribution was very important. Second, MapR provided production-class Hadoop with enterprise support. And third, the NFS gateway was critical for us to integrate ingestion and data flow pipelines with HDFS for easy, high-speed access,” said Suren Nathan, CTO, Razorsight.