{"id":465,"date":"2015-08-28T07:28:43","date_gmt":"2015-08-28T04:28:43","guid":{"rendered":"http:\/\/kusuaks7\/?p=70"},"modified":"2025-02-27T07:20:54","modified_gmt":"2025-02-27T07:20:54","slug":"big-data-database-sql-nosql","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/big-data-database-sql-nosql\/","title":{"rendered":"Big Data Database To SQL or NoSQL?"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tA\u00a0Wikibon study on the big data market<\/a>\u00a0conducted around the first quarter of 2013 attempted to divide the market\u0097based on the database market shares it holds. The study report forecasts the size and projected revenue of SQL and NoSQL database markets as related to big 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\tSince 2011, both SQL and NoSQL database technologies have made strong contributions to the growth of the big data market. Currently it may appear that the enterprises are broadly divided into SQL and NoSQL factions, but the fact remains that each of these database technologies has its relative merits and demerits. They both have particular characteristics that fit specific types of big data applications.\u00a0While SQL databases are known for data consistency, a richer programming environment, and easier testing and maintenance; NoSQL is better for scalable solutions and cost-friendly, big data applications.<\/span>\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\tThe challenge \u00a0most enterprises face today is selecting the relevant database technology for specific business application scenarios. Both SQL and NoSQL databases have value within their respective fit areas in both transactional and analytics big data.<\/em>\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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Market forecasts for SQL databases for big data\u00a0<\/strong><\/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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A relational database scripted by IBM, SQL upholds a host of industry standards, which are somewhat ambiguous! As SQL database vendors do not follow the same standards, vendor lock-in is a common problem. The leading SQL software vendors include IBM, Oracle and Microsoft, who offer very costly products with the capability to handle highly complex queries and transactions.<\/p>\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 Wikibon study shows that Oracle software is so costly that the combined cost of servers and the infrastructural software constitutes only 10% of the total cost of big data database implementation. This cost constraint creates a serious challenge to traditional SQL in big data systems. Moreover, the technical limitation of SQL database technology has been discussed in great detail in the Wikibon study on the big data market<\/a>.\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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\u00a0<\/strong>\n

Market forecasts for NoSQL databases\u00a0<\/strong>for big data\u00a0<\/strong><\/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\tThe birth and evolution of the NoSQL databases happened as an answer to challenges posed by big data. The large online businesses like Facebook and Google discovered NoSQL by accident\u0097when they could not handle huge volumes of data with the conventional SQL databases. The drawbacks of the NoSQL systems are lack of schema or data consistency, difficulty in testing and maintaining, and absence of a structured query language.\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\tHowever, NoSQL databases are apt for processing very high volumes data, while ensuring performance and speed of execution. The NoSQL database market for big data has maintained high growth rate since 2011.\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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\u00a0Top big data database vendors in ranked order<\/strong><\/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\tThe top 6 vendors endorse SQL databases:\n
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  1. IBM commands a sizeable 18% of the big data database revenues\u0097generating business of $215 million.<\/li>\n \t
  2. SAP also enjoys 18% of the big data database revenues at $190 million from its SQL products.<\/li>\n \t
  3. HP with Vertica commands 12% of the big data database revenues, at $150 million from SQL.<\/li>\n \t
  4. HP with Terradata \u0097generating $122 million from its wide installation base.<\/li>\n \t
  5. EMC generates $105 million, which amount to 9% of the big data database revenues.<\/li>\n \t
  6. MarkLogic, though trailing in the sixth position, is the leading NoSQL vendor. It commands a $43 million market amounting to 4% of the big data database market.<\/li>\n<\/ol>\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":"

    A\u00a0Wikibon study on the big data market\u00a0conducted around the first quarter of 2013 attempted to divide the market based on<\/p>\n","protected":false},"author":11,"featured_media":2652,"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-465","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\/465","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=465"}],"version-history":[{"count":4,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/465\/revisions"}],"predecessor-version":[{"id":37328,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/465\/revisions\/37328"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/2652"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=465"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=465"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=465"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=465"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}