{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T23:58:52Z","timestamp":1781308732656,"version":"3.54.1"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T00:00:00Z","timestamp":1659744000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T00:00:00Z","timestamp":1659744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Big Data and Cloud Computing as two mainstream technologies, are at the center of concern in the IT field. Every day a huge amount of data is produced from different sources. This data is so big in size that traditional processing tools are unable to deal with them. Besides being big, this data moves fast and has a lot of variety. Big Data is a concept that deals with storing, processing and analyzing large amounts of data. Cloud computing on the other hand is about offering the infrastructure to enable such processes in a cost-effective and efficient manner. Many sectors, including among others businesses (small or large), healthcare, education, etc. are trying to leverage the power of Big Data. In healthcare, for example, Big Data is being used to reduce costs of treatment, predict outbreaks of pandemics, prevent diseases etc. This paper, presents an overview of Big Data Analytics as a crucial process in many fields and sectors. We start by a brief introduction to the concept of Big Data, the amount of data that is generated on a daily bases, features and characteristics of Big Data. We then delve into Big Data Analytics were we discuss issues such as analytics cycle, analytics benefits and the movement from ETL to ELT paradigm as a result of Big Data analytics in Cloud. As a case study we analyze Google\u2019s BigQuery which is a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data. As a Platform as a Service (PaaS) supports querying using ANSI SQL. We use the tool to perform different experiments such as average read, average compute, average write, on different sizes of datasets.<\/jats:p>","DOI":"10.1186\/s13677-022-00301-w","type":"journal-article","created":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T16:02:46Z","timestamp":1659801766000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":133,"title":["Big data analytics in Cloud computing: an overview"],"prefix":"10.1186","volume":"11","author":[{"given":"Blend","family":"Berisha","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Endrit","family":"M\u00ebziu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isak","family":"Shabani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,8,6]]},"reference":[{"key":"301_CR1","first-page":"62","volume":"III","author":"M Hillbert","year":"2011","unstructured":"Hillbert M, Lopez P (2011) The world\u2019s technological capacity to store, communicate and compute information. Science III:62\u201365","journal-title":"Science"},{"key":"301_CR2","volume-title":"Gigaom Blog","author":"J Hellerstein","year":"2019","unstructured":"J. Hellerstein,\u201c Gigaom Blog,\u201d2019. Available: https:\/\/gigaom.com\/2008\/11\/09\/mapreduce-leads-the-way-for-parallelprogramming\/. Accessed 20 Jan 2021"},{"key":"301_CR3","volume-title":"Statista","author":"Statista","year":"2020","unstructured":"Statista,\u201cStatista,\u201c2020. Available: https:\/\/www.statista.com\/statistics\/871513\/worldwide-data-created\/. Accessed 21 Jan 2021"},{"key":"301_CR4","volume-title":"Data age 2025: the evolution of data to-life critical","author":"D Reinsel","year":"2017","unstructured":"Reinsel D, Gantz J, Rydning J (2017) Data age 2025: the evolution of data to-life critical. International Data Corporation, Framingham"},{"key":"301_CR5","volume-title":"Forbes","author":"Forbes","year":"2020","unstructured":"Forbes, \u201cForbes\u201d, 2020. Available: https:\/\/www.forbes.com\/sites\/bernardmarr\/2018\/05\/21\/how-muchdata-do-we-create-every-day-the-mind-blowing-stats-everyone-shouldread\/?sh=5936b00460ba"},{"key":"301_CR6","doi-asserted-by":"crossref","unstructured":"Kaisler S, Armour F, Espinosa J (2013) Big data: issues and challenges moving forward, Wailea, Maui, HI, s.n, pp 995\u20131004","DOI":"10.1109\/HICSS.2013.645"},{"key":"301_CR7","volume-title":"Wikipedia","author":"Wikipedia","year":"2018","unstructured":"Wikipedia,\u201c Wikipedia,\u201d 2018. Available: https:\/\/www.en.wikipedia.org\/wiki\/Bigdata\/. Accessed 4 Jan 2021"},{"key":"301_CR8","volume-title":"ZDNet","author":"D Gewirtz","year":"2018","unstructured":"D. Gewirtz,\u201c ZDNet,\u201d 2018. Available: https:\/\/www.zdnet.com\/article\/volume-velocity-and-varietyunderstanding-the-three-vs-of-big-data\/. Accessed 1 Jan 2021"},{"key":"301_CR9","unstructured":"Weathington J (2012) Big Data Defined. Tech Republic.\u00a0https:\/\/www.techrepublic.com\/article\/big-data-defined\/"},{"key":"301_CR10","unstructured":"PCMagazine,\u201c PC Magazine,\u201d 2018. Available: http:\/\/www.pcmag.com\/encyclopedia\/term\/62849\/big-data. Accessed 9 Jan 2021"},{"key":"301_CR11","volume-title":"Big Data Architect\u2019s Handbook, Packt","author":"SMF Akhtar","year":"2018","unstructured":"Akhtar SMF (2018) Big Data Architect\u2019s Handbook, Packt"},{"key":"301_CR12","volume-title":"WhishWorks","author":"WhishWorks","year":"2019","unstructured":"WhishWorks, \u201cWhishWorks\u201d, 2019. Available: https:\/\/www.whishworks.com\/blog\/data-analytics\/understanding-the3-vs-of-big-data-volume-velocity-and-variety\/. Accessed 23 Jan 2021"},{"key":"301_CR13","unstructured":"Yadav S, Sohal A (2017) Review paper on big data analytics in Cloud computing. Int J Comp Trends Technol (IJCTT) IX. 49(3);156-160"},{"key":"301_CR14","volume-title":"The data warehouse toolkit: the definitive guide to dimensional modeling","author":"R Kimball","year":"2013","unstructured":"Kimball R, Ross M (2013) The data warehouse toolkit: the definitive guide to dimensional modeling, 3rd edn. John Wiley & Sons","edition":"3"},{"key":"301_CR15","volume-title":"LaprinthX","author":"LaprinthX","year":"2018","unstructured":"LaprinthX, \u201cLaprinthX,\u201d2018. Available: https:\/\/laptrinhx.com\/better-faster-smarter-elt-vs-etl-2084402419\/. Accessed 22 Jan 2021"},{"key":"301_CR16","volume-title":"XPlenty","author":"Xplenty","year":"2019","unstructured":"Xplenty, \u201cXPlenty, \u201d, 2019. Available: https:\/\/www.xplenty.com\/blog\/etl-vs-elt\/#. Accessed 20 Jan 2021"},{"key":"301_CR17","volume-title":"Forbes","author":"Forbes","year":"2018","unstructured":"Forbes,\u201cForbes,\u201d,2018. Available: https:\/\/www.forbes.com\/sites\/forbestechcouncil\/2019\/11\/06\/fivebenefits-of-big-data-analytics-and-how-companies-can-getstarted\/?sh=7e1b901417e4. Accessed 13 Jan 202"},{"key":"301_CR18","volume-title":"EDHEC","author":"EDHEC","year":"2019","unstructured":"EDHEC, \u201cEDHEC, \u201d, 2019. Available: https:\/\/master.edhec.edu\/news\/three-ways-educators-are-using-bigdata-analytics-improve-learning-process#. Accessed 6 Jan 2021"},{"key":"301_CR19","volume-title":"BigQuery","author":"Google Cloud","year":"2020","unstructured":"Google Cloud, \u201cBigQuery, \u201d, 2020. Available: https:\/\/cloud.google.com\/bigquery. Accessed 5 Jan 2021"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-022-00301-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-022-00301-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-022-00301-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T16:03:32Z","timestamp":1659801812000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofcloudcomputing.springeropen.com\/articles\/10.1186\/s13677-022-00301-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,6]]},"references-count":19,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["301"],"URL":"https:\/\/doi.org\/10.1186\/s13677-022-00301-w","relation":{},"ISSN":["2192-113X"],"issn-type":[{"value":"2192-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,6]]},"assertion":[{"value":"8 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"24"}}