Oracle Hadoop Based Analytical Tools to Explore the Spatial Big Data Processing

Oracle Oracle Hadoop Based Analytical Tools to Explore the Spatial Big Data ProcessingThe elephant of Apache Hadoop is increasingly acclaimed by thousands of developers and companies around the world. As big data and the demands of real-time analytics increase globally, the emergence of Hadoop has created new oceans to explore data.

Now, Oracle has 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.

Users of the Oracle database have long had access to graphical tools and analytic space, which are used to discover relationships and analyze data sets involving location. With the intention to meet diverse data sets and minimize the need for data movement, Oracle created the product so that it can process data natively on Hadoop and parallel on MapReduce using structures in memory.

There are two main components. One is a graph of property distributed to more than 35 high-performance analytic functions, parallel and in memory. The other is a collection of functions and services of spatial analysis to evaluate data based on how close or far you find something, whether it falls within a border or region, or for processing and displaying data and geospatial imagery. Analysts can then discover relationships and connections between clients, organizations and assets.

The Property Graph Data Management and Analysis facilitate the work on big data with the opportunity to develop models in real time, thanks to parallel in-memory analytics. Graphs are flexible and easy to evolve while the metadata is stored as part of the new graphs and reports findings can be added on the fly. With space instruments, users can take the data with location information, enrich them and use them to harmonize the whole environment.

According to the Oracle post, “With the spatial capabilities, users can take data with any location information, enrich it, and use it to harmonize their data. For example, Big Data Spatial and Graph can look at datasets like Twitter feeds that include a zip code or street address, and add or update city, state, and country information. It can also filter or group results based on spatial relationships: for example, filtering customer data from logfiles based on how near one customer is to another, or finding how many customers are in each sales territory. These results can be visualized on a map with the included HTML5-based web mapping tool. Location can be used as a universal key across disparate data commonly found in Hadoop-based analytic solutions.”

The Big Data Discovery analytic tool is the Oracle’s framework of big data Hadoop processing to profile, explore, analyze and find correlations in data from a Hadoop system. Last month, Oracle extended its middleware Data Integrator, which referred to specialists for database and data warehousing to engage in activities associated with big data. The Oracle Data Integrator solution for big data aim of helping companies to make data without learning Scala, Oozie or ETL, allowing to generate transformations in these languages ??with simple mappings.


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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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