Pentaho Big Data Analytics實(shí)現(xiàn)了通過(guò)單一平臺(tái)提供可視化工具,能提取及合成數(shù)據(jù),并且實(shí)現(xiàn)可視化和分析的功能,并改變你經(jīng)營(yíng)業(yè)務(wù)的方式。無(wú)論是什么數(shù)據(jù)源,什么樣的分析要求或者部署環(huán)境,Pentaho都能允許你對(duì)大數(shù)據(jù)進(jìn)行深層次的分析。
Within a single platform our solution provides visual tools to extract and prepare your data plus the visualizations and analytics that will change the way you run your business. Regardless of the data source, analytic requirement or deployment environment, Pentaho allows you to turn big data into big insights.
完整的大數(shù)據(jù)平臺(tái)
緊密的耦合數(shù)據(jù)集成與商業(yè)分析平臺(tái)加速了大數(shù)據(jù)的價(jià)值實(shí)現(xiàn)。
完整的大數(shù)據(jù)分析平臺(tái)
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全方位的分析方案:數(shù)據(jù)訪問(wèn)權(quán)限及數(shù)據(jù)可視化與預(yù)測(cè)分析方案的集成。
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通過(guò)Pentaho自適應(yīng)的大數(shù)據(jù)層支持最廣泛的數(shù)據(jù)源,充分利用每個(gè)數(shù)據(jù)源的獨(dú)特的優(yōu)勢(shì)。
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允許用戶生成混合的大數(shù)據(jù),并將它們用于更全面,更精確的分析方案。
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基于開(kāi)放標(biāo)準(zhǔn)的構(gòu)架,支持?jǐn)U展和與現(xiàn)有設(shè)施集成。
交互式的分析方案、報(bào)表、可視化功能和面板
Pentaho 允許商業(yè)用戶和分析師在不依賴IT和開(kāi)發(fā)人員的情況下,通過(guò)多個(gè)維度輕松的實(shí)現(xiàn)數(shù)據(jù)的可視化,分析和反饋。
強(qiáng)大的大數(shù)據(jù)分析方案和反饋功能
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交互式可視化分析有數(shù)據(jù)鉆取、lasso濾波、縮放和易于審查的屬性高亮顯示。
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實(shí)時(shí)可用的交互式可視化功能庫(kù)。
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為大數(shù)據(jù)量的快速思維分析提供了極大規(guī)模的的內(nèi)存數(shù)據(jù)緩存。
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自主交互式反饋到高容量,高度特化的企業(yè)報(bào)表。
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支持任何數(shù)據(jù),包括混合了企業(yè)數(shù)據(jù)的顯示面板。
高容量數(shù)據(jù)處理
提升大數(shù)據(jù)開(kāi)發(fā)速度,在集群上獲取更好的性能。
高效的數(shù)據(jù)處理
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支持本地連接到領(lǐng)先的Hadoop,NoSQL和analytic數(shù)據(jù)庫(kù)。
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可視化的MapReduce任務(wù)設(shè)計(jì)器縮短了開(kāi)發(fā)周期。
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支持對(duì)非結(jié)構(gòu)化數(shù)據(jù)進(jìn)行編制,建模和搜索。
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強(qiáng)大的多線程數(shù)據(jù)集成引擎,加速作業(yè)執(zhí)行。
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支持集群,實(shí)現(xiàn)跨結(jié)點(diǎn)的分布式處理。
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獨(dú)特的Hadoop內(nèi)部執(zhí)行過(guò)程,實(shí)現(xiàn)極高效的性能。
自適應(yīng)大數(shù)據(jù)層
提高了對(duì)流行數(shù)據(jù)商店的最新版本和功能的訪問(wèn)權(quán)限和集成能力。
自適應(yīng)大數(shù)據(jù)層用于投資保護(hù)
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一旦有了數(shù)據(jù)的訪問(wèn)權(quán)限,之后在任何地方都可以處理,合并和使用它。
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支持從Cloudera、Hortonworks、MapR到Intel等最新的Hadoop分布。
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包含針對(duì)Cassandra、MongoDB等NoSQL數(shù)據(jù)庫(kù)的簡(jiǎn)單插件。
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支持連接到Amazon Redshift和Splunk等專業(yè)的數(shù)據(jù)商店。
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高度的靈活性,降低了大數(shù)據(jù)體系變化所帶來(lái)的風(fēng)險(xiǎn)和孤立點(diǎn)。
Pentaho Instaview:從大數(shù)據(jù)發(fā)掘想法只需3步
Pentaho Instaview只需簡(jiǎn)單的3步就可以帶領(lǐng)用戶從數(shù)據(jù)走向分析方案,縮短訪問(wèn)和搜索大容量、多樣化數(shù)據(jù)的時(shí)間。
Instaview用于從混合大數(shù)據(jù)集中發(fā)掘想法
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針對(duì)包括Hadoop,Cassandra,HBase,MongoDB等先進(jìn)的大數(shù)據(jù)源提供自助式分析方案。
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提高了大數(shù)據(jù)的訪問(wèn)權(quán)限,去除了分離大數(shù)據(jù)可視化工具這一要求。
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允許IT輸入和管理終端用戶的大數(shù)據(jù)的訪問(wèn)權(quán)限,快速部署大數(shù)據(jù)分析方案。
強(qiáng)大的數(shù)據(jù)挖掘和預(yù)測(cè)分析方案
精密的分析模型使得組織機(jī)構(gòu)可以通過(guò)歷史績(jī)效規(guī)劃未來(lái)成果。
數(shù)據(jù)挖掘等等
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功能強(qiáng)大的算法,包括分類,回歸,聚類和關(guān)聯(lián)性分析。
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使用預(yù)測(cè)模型標(biāo)記語(yǔ)言(PMML)導(dǎo)入第三方模型。
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使用Pentaho庫(kù)對(duì)模型進(jìn)行存儲(chǔ)和版本控制。
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使用Pentaho數(shù)據(jù)集成對(duì)Hadoop 集群內(nèi)外的模型優(yōu)化。
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算法并入了Pentaho的可視化界面。
Comprehensive Big Data Platform
A tightly coupled data integration and business analytics platform accelerates the realization of value from big data.
Complete Big Data Analytics Platform
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Full array of analytics: data access and integration to data visualization and predictive analytics.
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Supports the broadest spectrum of big data sources with Pentaho adaptive big data layer, which takes advantage of the specific and unique capabilities of each source.
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Empowers users to architect big data blends at the source and stream them directly for more complete and accurate analytics.
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Open, standards based architecture, easy to integrate with or extend existing infrastructure.
Interactive Analysis, Reporting, Visualizations and Dashboards
Pentaho empowers business users and analysts to easily visualize, analyze, and report on data across multiple dimensions without depending on IT or developers.
Powerful Big Data Analytics and Reporting
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Interactive analysis, drill through, lasso filtering, zooming, and attribute highlighting for greater insight.
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Out-of-the box library of interactive visualizations.
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Extreme scale in-memory data caching for speed-of-thought analysis of large data volumes.
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Self-service interactive reporting to high volume, highly formatted enterprise reports.
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Dashboards from any big data source including data blended with enterprise data sources.
High-Volume Data Processing
Speed development time for big data and achieve exceptional in-cluster performance.
High performance data processing
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Native connectivity to leading Hadoop, NoSQL and analytic databases.
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Visual designer for MapReduce jobs to reduce development cycles.
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Data preparation, modeling and exploration of unstructured data sets.
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Powerful, multi-threaded data integration engine for fast execution.
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Cluster support, enabling distributed processing of jobs across multiple nodes.
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Unique in-Hadoop execution for extremely fast performance.
Adaptive Big Data Layer
Accelerate access and integration to the latest versions and capabilities of popular big data stores.
Investment Protection with Adaptive Big Data Layer
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Ability to access data once - and then process, combine and consume it anywhere.
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Support for latest Hadoop distributions from Cloudera, Hortonworks, MapR and Intel.
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Simple plug-ins to NoSQL databases such as Cassandra and MongoDB.
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Connections to specialized data stores such as Amazon Redshift and Splunk.
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Greater flexibility and insulation from changes in the big data ecosystem.
Pentaho Instaview: 3 Steps from Big Data to Big Insights
Pentaho Instaview takes users from data to analytics in three simple steps, reducing the time to access and explore large volumes of complex and diverse data.
Instaview for insights from blended big data sets
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Self-service analytics for the leading big data stores including Hadoop, Cassandra, HBase, MongoDB and more.
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Broadens data access to data analysts and removes the need for separate big data visualization tools.
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Allows IT to streamline and manage end user access to big data stores and deploy big data analytics faster.
Powerful Data Mining and Predictive Analytics
Sophisticated analytical modeling empowers organizations to plan for future outcomes by understanding historical business performance.
Data Mining and More
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Powerful algorithms such as classification, regression, clustering and association.
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Import of third-party models using Predictive Modeling Markup Language (PMML).
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Storing and versioning of models using the Pentaho repository.
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Operationalization of models inside or outside of a Hadoop cluster.
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Incorporation of algorithms into Pentaho's visual interface.