MapR Enhances its Real-Time Processing Capabilities for Big Data Analysis

MapR Logo 300x70 MapR Enhances its Real Time Processing Capabilities for Big Data AnalysisThe big data platform MapR just introduced version 5.0 of its Hadoop distribution based on version 2.7 of the open source framework designed for the processing of very large volumes of data with the support for Docker containers. MapR 5.0 also relies on the Yarn resource manager.

This version strengthens the operational capacity real-time platform. In particular, it extended the highly reliable data transport framework used in the function table MapR-DB Replication (which allows replication between multiple data centers) to provide data to external motors and synchronize in real time.

Compared to other Hadoop distributions, MapR extends the functionality of the framework on security aspects (data protection, user authentication, disaster recovery), but also high availability and performance. Version 5.0 brings further improvements in governance, with a full audit access to data through JSON and Apache Drill Views of support for secure access to data analyze.

More and more companies deploy multiple applications on the same Hadoop cluster. In this context, the latest MapR manages automated synchronization of storage, databases and search index.

To facilitate the deployment of Hadoop clusters, the publisher has also included new models of self-provisioning to set up a cluster as if it were an appliance without using specific hardware. These models can be deployed using the MapR installer. Among the possible configurations, there are the Lake Data services, data mining (Interactive SQL with Apache Drill) and analysis of operational data (basic and MapR NoSQL-DB).

The Apache project will help in the analysis and the use of batch processes and their pipelines with rapid and extensive calculations. The announced distribution automatically synced storage, databases and search indices to allow complex real-time applications. It also has new auditing capabilities.

MapR Technologies intends to continue its growth in big data and analytics-segment. In the context of the MapR database now has the ability to the table replication to synchronize data in real time and make it available for external calculators. The first case that is based on Lucene search platform Elasticsearch is supported to enable synchronized full-text search indexes automatically.

Last year, MapR and Apache Spark integrated their technologies to offer its users an all-around the clock support for Spark to develop the solution and related projects at a faster rate and to integrate more innovative changes. In addition, the two companies are working together on a rapid development of the software and other complementary innovative new features. This will pay off for MapR customers and the Hadoop community well over the coming years.

Recently, Oracle released a new software product that is designed to help big data demands. This product called Oracle Big Data Spatial and Graph provides new analytical capabilities for Hadoop and NoSQL. Oracle created the product so that it can process data natively on Hadoop and parallel on MapReduce using structures in memory.


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IBM Watson Ecosystem Strengthen the Hybrid Cloud Capabilities

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IBM hybrid cloud 300x199 IBM Watson Ecosystem Strengthen The Hybrid Cloud CapabilitiesIBM has introduced new hybrid cloud capabilities for Watson to help companies to connect their data with web applications in the cloud. Watson Hybrid Cloud solution will use Watson Explorer as the default platform for application development, combining enterprise data sources in the application through a scalable environment that keep local and private data secure.

With the new hybrid cloud capabilities, users can more quickly extract business intelligence from data housed in multiple environments – traditional or cloud. In addition, IBM has announced the addition of 270 new partners to the ecosystem of Watson, as well as new specialized cognitive apps in different sectors such as entertainment, energy, health and tourism in order to save time, risk, and ensure regulatory compliance.

Cognitive application developers often face the challenge of where to locate the data and can not move them all to a public cloud for different reasons, either because of their huge amount, or by privacy requirements, regulations or security. Using the IBM hybrid cloud capabilities, users can quickly extract business intelligence data hosted securely in multiple environments.

The platform allows companies to develop applications in seconds and with minimal configuration. Watson Hybrid Cloud solution will use Watson Explorer as the default platform for application development, combining enterprise data sources in the application through a scalable environment that keep local and private data securely.

IBM Watson Analytics automates some steps of the analysis, such as data preparation, predictive analysis and visualization in an understandable way for different departments within a company, such as marketing, sales, operations, finance and human resources.

IBM has also renewed its portfolio with new products that allow companies to integrate hybrid cloud solutions in their organization, and meet new workloads generated by the rise of mobile, social and analytical technologies. The new IBM Power Systems, IBM Spectrum Storage, IBM Systems z, IBM Middleware, IBM SoftLayer and OpenStack software, promise to help customers easier navigation between these environments and more valuable for the development of business information.

Last month, IBM launched a new division called IBM Watson Health allowing patients, physicians, researchers and insurance sectors to deal effectively with health data. The unit offers a secure platform for open cloud for doctors, researchers, insurance companies and solution-oriented health and wellness, allowing anonymity to share and combine data concerning health companies.

MarketsandMarkets, the consulting and market research firm, has estimated that the growth of hybrid cloud will reach about $ 84.67 billion in 2019. The results of this study show the increasing adoption of the hybrid cloud computing model.

The main features of the hybrid cloud computing model are the standards and shared services, packaged solutions, self-service, scalable, price based on use, accessible via the Internet, standard UI technologies and publishing services and API, which result in a more efficient use of IT assets , greater agility to launch new services and greater cost efficiency.

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