Company Overview of MapR Technologies, Inc.
MapR Technologies, Inc. provides enterprise-grade distributed data platform to store and process big data. Its platform supports and brings speed to Hadoop, NoSQL, database, and streaming applications in one distribution for Hadoop. The company offers MapR M7, which provides distribution for Apache Hadoop with Hadoop database to run online and analytical processing on one platform; M5 edition, provides enterprise-grade Hadoop performance and management capabilities; and M3 edition for production use. It serves advertising, media, entertainment, data warehouse, financial services, government, healthcare, manufacturing, oil and gas, retail, technology and information services, and telecommunic...
2833 Junction Avenue
San Jose, CA 95134
Founded in 2009
Key Executives for MapR Technologies, Inc.
Co-Founder, Chairman of The Board and Chief Executive Officer
Co-Founder, Chief Technology officer and Director
Chief Application Architect
Senior Vice President of Product Management
Compensation as of Fiscal Year 2015.
MapR Technologies, Inc. Key Developments
Razorsight Launches Predictive Analytics Solution on MapR Technologies with Apache Spark
Jun 16 15
MapR Technologies, Inc. announced that Razorsight is using the MapR Distribution including Hadoop, along with Apache Spark, to take advantage of big data storage and compute. Razorsight evolved its technology stack with Hadoop from MapR to scale cost effectively and to generate valuable insights using data science and predictive analytics for communications service providers. Razorsight uses MapR to build a central data lake as a primary data store for both online and archived data. Since the launch of this new stack in late third quarter of 2014, the production cluster has received, processed and analyzed more than 40 terabytes of telecommunications service provider and industry data, which includes data generated from the mobile devices, mobile, fixed and cable networks, content providers, Internet of Things, broadband applications and VOIP. Razorsight leverages the MapR NFS gateway to move these data sets in and out of the cluster seamlessly, making it extremely easy and intuitive to integrate Hadoop into the overall data flow. Razorsight then uses Spark as an in-memory processing engine to enrich and transform the source data to prepare the analytical records for advanced modeling. Additionally, Razorsight’s end-users and business analysts continue to use existing business intelligence and visualization tools on their downstream data warehouse. The new platform has enabled Razorsight to expand into new solution areas for its telecommunications service provider customers. For example, Razorsight’s sales and marketing solution is designed to improve the customer experience, reduce churn and identify the next best offer. The marketing team at Virgin Mobile Latin America has deployed Razorsight’s sales and marketing solution in multiple countries to support its expansion there. Razorsight’s predictive analytics will help them tailor targeted marketing campaigns based on a particular customer’s propensity to churn.
MapR Technologies, Inc. Launches Spark-Based Quick Start Solutions for Hadoop
Jun 15 15
MapR Technologies, Inc. announced at Spark Summit the immediate availability of three new Apache Spark-based Quick Start Solutions for the MapR Distribution including Apache Hadoop. These solutions help customers take advantage of the rapid application development and in-memory processing capabilities of the Spark engine along with the enterprise-grade capabilities of the Hadoop distribution. 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. The three Quick Start Solutions – Real-timeSecurity Log Analytics, Time Series Analytics, and Genome Sequencing - will enable customers who are new to Spark to realize faster time-to-value with their implementations. The solutions can be customized to specific requirements and use cases with the help of world-class data scientists and engineers from MapR Professional Services, who have deep experience building Spark and Hadoop-based applications for customers. Each new Quick Start Solution is tailored around a specific solution area and includes data ingest modules, professional services and a small Hadoop cluster that can easily be expanded based on the solution requirements. Details of the three new solutions include: Real-time Security Log Analytics combines the power of the highly reliable MapR Distribution with the Apache Spark stack to support real-time analysis of large volumes of security data, which can help in early detection of advanced persistent threats and unknown threats. The solution augments existing Security Information and Event Management (SIEM) solutions by providing cost-effective storage and processing for deep analytics and by predicting anomalous behavior within the environment to identify unknown threats. Time Series Analytics brings the reliable, top-ranked NoSQL database, MapR-DB, together with Apache Spark to support rapid ingestion and extraction of data along with real-time aggregation capabilities. The solution helps faster development of real-time monitoring applications and alert systems on various types of IoT-style data including time-series data coming from machines, sensors and devices. Genome Sequencing leverages Apache Hadoop and Spark for large-scale parallel processing of genome data providing lower latency compared to HPC and homegrown solutions. The solution reduces latency of converting a sequenced genome to clinically actionable information and supports flexibility and extensibility of various computational algorithms that can be utilized. The end result is faster development of downstream clinical applications at a lower overall cost compared to alternatives.
MapR Technologies, Inc. Introduces New Auto-Provisioning Templates to Speed the Deployment of Hadoop Clusters
Jun 9 15
MapR Technologies, Inc. announced at Hadoop Summit a new software module to accelerate the provisioning and deployment of big data solutions. The comprehensive MapR Auto-Provisioning Templates apply software-defined concepts that will enable organizations to quickly deploy a cluster with appliance-like convenience and with the flexibility and choice of building a custom, enterprise-grade data platform. The MapR Auto-Provisioning Templates provide organizations with flexibility to deploy purpose-built big data solutions on their hardware infrastructure of choice, whether it be directly on hardware servers from a variety of vendors, a virtualized private cloud, or a public cloud provider. The Auto-Provisioning Templates provide the simplicity of appliances yet also support the efficiency and hardware diversity that production Hadoop clusters typically require. The Auto-Provisioning Templates also let customers expand their deployment at increments they define and need, rather than at the homogeneous “stair-step” increments that a rack-based appliance requires. Auto-Provisioning Templates define the software, network and hardware attributes of a single node, as well as support diverse definitions required across many nodes. Auto-Provisioning Templates easily support the deployment of the following configurations: Data Lake: Common Hadoop Services Includes the most common services deployed in an Apache Hadoop cluster, including YARN, MapReduce, Spark, and Hive all on top of the big & fast MapR Data Platform for getting started with a Hadoop data lake; Data Exploration: Interactive SQL with Apache Drill Provides services needed for users to perform schema-free interactive exploration of their data, including Apache Drill; Operational Analytics: NoSQL Database with MapR-DB Deploys the MapR distributed NoSQL database, enabling both operational HBase applications to read and write data at high rates, and analytic applications to perform in-situ data processing. Users deploy MapR Auto-Provisioning Templates via the MapR Installer, which further simplifies the deployment of MapR software by providing: Auto-layout - Hides the complexity of deciding how to best distribute Hadoop and other services across servers, selecting the optimal layout for the services selected and hardware provided; Rack awareness - Automatically distributes critical services across failure domains; Health checks - Executes a suite of tests on all servers to ensure they will perform optimally after installation, and warns users of potential issues.
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