![\"Differences\"](\"https:\/\/i0.wp.com\/timoelliott.com\/blog\/wp-content\/uploads\/2011\/09\/apple-orange-edw-hana-banner.jpg?resize=690%2C310&ssl=1\" "\\"Differences\\"")
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Many thanks to William Gardella<\/a>\u00a0and others for the content below:<\/p>\n Traditional enterprise data warehousing and Hadoop\/Big Data are like apples and oranges \u2013 the well-known and trusted approach being challenged by a zesty newcomer (sweet oranges were introduced to Europe sometime in the 16th century<\/a>). Is there room for both? How will these two very different approaches co-exist?<\/p>\n This post is an attempt to summarize the current state of play with Hadoop, \u201cBig Data\u201d and Enterprise BI, and what it means to existing users of enterprise business intelligence. See the list of articles at the end of the post for more detailed materials.<\/p>\n Hadoop<\/a> is open-source software that enables reliable, scalable, distributed computing on clusters of inexpensive servers. It is:<\/p>\n Hadoop is designed to process terabytes and even petabytes of unstructured and structured data. It breaks large workloads into smaller data blocks that are distributed across a cluster of commodity hardware for faster processing. And it\u2019s part of a larger framework of related technologies:<\/p>\n Image: William Gardella<\/em><\/p>\n Yes. According to a recent Ventana survey<\/a>:<\/p>\n Currently, Hadoop has carved out a clear niche next to conventional systems<\/a>. Hadoop is good at handling batch processing of large sets of unstructured data, reliably, and at low cost. It does, however, require scarce engineering expertise, real-time analysis is challenging, and it much less mature than traditional approaches. As a result, Hadoop is not typically being used for analyzing conventional structured data such as transaction data, customer information and call records, where traditional RDBMS tools are still better adapted:<\/p>\n \u201cHadoop is real, but it\u2019s still quite immature. On the \u201creal\u201d side, Hadoop has already been adopted by many companies for extremely scalable analytics in the cloud. On the \u201cimmature\u201d side, Hadoop is not ready for broader deployment in enterprise data analytics environments\u2026\u201d James Kobelius, Forrester Research.<\/p><\/blockquote>\n Today, Hadoop is being used as a:<\/p>\n Most existing vendors in the data warehousing space have announced integrations between their products and Hadoop\/MapReduce, or their intention to provide them \u2013 for example, SAP has announced that they intend to implement MapReduce in the next version of Sybase IQ<\/a>.<\/p>\n It\u2019s clear that Hadoop will become a key part of future enterprise data warehouse architectures:<\/p>\n \u201cThe bottom line is that Hadoop is the future of the cloud EDW, and its footprint in companies\u2019 core EDW architectures is likely to keep growing throughout this decade. \u201c James Kobelius, Forrester Research<\/a><\/p><\/blockquote>\n But (despite some of the almost religious fervor of its backers) Hadoop is unlikely to supplant the role of traditional data warehouse and business intelligence<\/a>:<\/p>\n \u201cThere are places for the traditional things associated with high-quality, high-reliability data in data warehouses, and then there\u2019s the other thing that gets us to the extreme edge when we want to look at data in the raw form\u201d\u00a0 Yvonne Genovese, Gartner Inc.<\/p><\/blockquote>\n Companies will continue to use conventional BI for mainstream business users to do ad hoc queries and reports, but they will supplement that effort with a big-data analytics environment optimized to handle a torrent of unstructured data \u2013 which, of course, has been part of the goal of enterprise data warehousing for a long time.<\/p>\n Hadoop is particularly useful when<\/a>:<\/p>\n Hadoop provides a new, complementary approach to traditional data warehousing that helps deliver on some of the most difficult challenges of enterprise data warehouses. Of course, it\u2019s not a panacea, but by making it easier to gather and analyze data, it may help move the spotlight away from the technology towards the more important limitations on today\u2019s business intelligence efforts: information culture and the limited ability of many people to actually use information to make the right decisions.<\/p>\n Traditional enterprise data warehousing and Hadoop\/Big Data are like apples and oranges. Is there room for both? How will these two very different approaches co-exist in the future?<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[3],"tags":[100,160,173,198,204,213,350,421,437,556,911],"class_list":["post-12214","post","type-post","status-publish","format-standard","hentry","category-bi-20","tag-analytics","tag-bi","tag-big-data","tag-business-analytics","tag-business-intelligence","tag-businessobjects","tag-data-warehouse","tag-edw","tag-enterprise-data-warehousing","tag-hadoop","tag-sap"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p3X9RF-3b0","_links":{"self":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts\/12214","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/comments?post=12214"}],"version-history":[{"count":0,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/posts\/12214\/revisions"}],"wp:attachment":[{"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/media?parent=12214"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/categories?post=12214"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/timoelliott.com\/blog\/wp-json\/wp\/v2\/tags?post=12214"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}What is Hadoop?<\/h3>\n
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![\"hadoop-architecture\"](\"https:\/\/i0.wp.com\/timoelliott.com\/blog\/wp-content\/uploads\/2011\/09\/hadoop-architecture.jpg?resize=690%2C294&ssl=1\" "\\"hadoop-architecture\\"")
<\/p>\nAre Companies Adopting Hadoop?<\/h3>\n
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How is it Being Use in Relation to Traditional BI and EDW?<\/h3>\n
![\"hadoop-vs-traditional\"](\"https:\/\/i0.wp.com\/timoelliott.com\/blog\/wp-content\/uploads\/2011\/09\/hadoop-vs-traditional_thumb.png?resize=201%2C164&ssl=1\" "\\"hadoop-vs-traditional\\"")
<\/a>To considerably over-simplify: if we consider what\u2019s called the 3 \u2018V\u2019s of the data challenge: \u201cVolume, Velocity, and Variety\u201d (and there\u2019s a fourth, Validity), then traditional data warehousing is great at Volume and Velocity (especially with the new analytic architectures), while Hadoop is good at Volume and Variety.<\/p>\n\n
![\"Differences\"](\"https:\/\/i0.wp.com\/timoelliott.com\/blog\/wp-content\/uploads\/2011\/09\/apple-orange-edw-hana-together.jpg?resize=690%2C310&ssl=1\" "\\"Differences\\"")
<\/p>\nWhat Does The Future Look Like?<\/h3>\n
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Does Hadoop and Big Data Solve All Our Data Problems?<\/h3>\n
References \/ Suggested Reading<\/h3>\n
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