{"id":478,"date":"2015-08-28T07:28:43","date_gmt":"2015-08-28T04:28:43","guid":{"rendered":"http:\/\/kusuaks7\/?p=83"},"modified":"2025-02-03T15:06:14","modified_gmt":"2025-02-03T15:06:14","slug":"enterprise-data-management-using-hadoop-mapr","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/enterprise-data-management-using-hadoop-mapr\/","title":{"rendered":"Enterprise Data Management using Hadoop with MapR"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tWith the emergence of production-level Hadoop with MapR distribution, business data\u0097that was too large, too complex, and too expensive to capture, store,\u00a0 and analyze\u0097can now be accessed, retrieved, and processed for in-depth insights at 1\/10 to 1\/50 of the assumed or actual cost calculated on one terabyte of data. The Chief Information Officers have suddenly assumed a role of a guardian angels, guiding separate units within an organization to reject individual data silos, and move towards storing and processing their precious business data on a decentralized Hadoop system.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe real caveat is MapR, which enables single-platform predictive analytics<\/i>, full search and discovery and advanced database operations. The MapR\u0092s?enterprise-grade search?feature is perfectly compatible with Hadoop data. Moreover, MapR can index and search standard data files without any intermediate conversion.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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<\/b>Hadoop\u00a0<\/b>with MapR: Single-platform data management system\u00a0<\/b><\/h3><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tApache Hadoop with MapR distribution provides a highly scalable, production-oriented data management platform that supports and performs data storage, data processing, and data analytics functions equally well without taking a heavy toll on IT resources. With MapR, data scientists may create any type of big data\u00a0applications across the enterprise functions\u0097and that too, on a single platform.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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<\/h3>\n

Production-level Hadoop<\/b>: Transition from data storage technology to complete data management \u00a0<\/b><\/h3><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tSo, your Hadoop aced all tests during the design and setup phase. What about Hadoop\u0092s resilience to withstand the total data-center ecosystem?<\/i>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIn an enterprise data management environment, individual systems are frequently known to fail or underperform among the rest of the systems in the network architecture. This is where MapR distribution plays a critical role in ensuring that Hadoop maintains its reputation from a conceptual ideology to a production-level success story.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nIn fact, MapR is uniquely positioned to enable Hadoop to deliver mission-critical data management and data analytics services that have data security, performance reliability, and fast, low-footprint recovery built into it. MapR is exceptional in handling enterprise tasks like interactive querying, instantaneous data streaming, and complex analytics.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The jewels on the crown: Hadoop<\/b>\u00a0on Apache selling points<\/b><\/h3><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tFast <\/b>data access and seamless integration\n<\/b>With MapR, \u00a0Hadoop can be accessed very easily as a network attached storage (NAS) via NFS. This method enables speedy data access and management by eliminating intermediate steps existing in traditional database systems. Yes, users can perform all the traditional, file system operations with the mounted MapR cluster. Also, because of a distributed architecture, data clogging or operational bottlenecks are virtually absent.\u00a0<\/b>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tMore data more analysis\n<\/b>As Hadoop enables users to process <\/b>bring structured, semi-structured, and unstructured data together \u0097on a single platform, users are able to conduct accurate analyses to predict customer behavior and operational metrics. MapR facilitates real-time monitoring, risk modeling, fraud identification, With MapR, \u00a0Hadoop can be easily accessed as a network attached storage (NAS) via NFS. This method enables speedy data access and management by eliminating intermediate steps existing in traditional database systems. Yes, users can perform all the traditional, file system operations with the mounted MapR cluster. Plus, data clogging or operational bottlenecks are virtually absent\u00a0because of a distributed architecture.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tZero-failure business applications\n<\/b>The Hadoop architectural framework is so fault tolerant and trustworthy, that MapR guarantees high availability (HA) across Hadoop projects. MapR has built redundancy at every layer of Hadoop including protection from node failures, MapReduce failures as well as data access point failures. Thus, more and more organizations are building business-critical applications that require low latency and transactional consistency. <\/i>Some example of business applications on the Hadoop platform include real-time marketing discounts, or cable advertising optimization using a set-top box data.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nMinimal deployment and operational costs\n<\/b>Hadoop with MapR is the most reliable, production-ready that facilitates data storage, analytics, and business applications on a single platform. An enterprise can run many applications on one Hadoop cluster\u0097at drastically reduced operational costs.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tScalability at your pace\n<\/b>MapR supports earlier versions of most of the ecosystem projects\u0097leaving organizations free to choose their own timetable for scaling up or down their big data capabilities. MapR tests and validates open-source projects before including them in its distribution.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tMapR updates the open source projects every month, thus maintaining the most updated distribution at any point of time.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tUnflinching support to open standards\n<\/b>MapR does not impose any type of vendor lock-in through its file-system layer, so the end-users are free to customize their installation. MapR support extends not only to Hadoop\u0092s interfaces, but also to other industry standards such as NFS, ODBC, POSIX, LDAP, and so on. MapR support for SQL involves an open approach, supporting the broadest set of SQL-on-Hadoop projects.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tOne platform for big data applications\n<\/strong>MapR architecture supports and enables direct processing of files and tables. With its support of NFS, MapR makes it seamless to integrate existing applications and solutions. Other built-in features of MapR comprise HA, heterogeneous hardware cluster, data protection and disaster, multi-tenancy and volume support, to name a few.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

With the emergence of production-level Hadoop with MapR distribution, business data that was too large, too complex, and too expensive<\/p>\n","protected":false},"author":11,"featured_media":2726,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[143,144],"ppma_author":[1606],"class_list":["post-478","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-hadoop","tag-nosql-amp-newsql"],"authors":[{"term_id":1606,"user_id":11,"is_guest":0,"slug":"cameron-turner","display_name":"Cameron Turner","avatar_url":{"url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2024\/09\/cameron.jpeg","url2x":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2024\/09\/cameron.jpeg"},"user_url":"","last_name":"Turner","first_name":"Cameron","job_title":"","description":""}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/478","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=478"}],"version-history":[{"count":4,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/478\/revisions"}],"predecessor-version":[{"id":37224,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/478\/revisions\/37224"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/2726"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=478"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=478"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=478"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=478"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}