{"id":4407,"date":"2013-11-17T20:27:23","date_gmt":"2013-11-17T14:57:23","guid":{"rendered":"http:\/\/ivyproschool.com\/blog\/?p=4407"},"modified":"2024-11-11T17:05:00","modified_gmt":"2024-11-11T11:35:00","slug":"a-rundown-on-analytics-friendly-big-data-technologies","status":"publish","type":"post","link":"https:\/\/ivyproschool.com\/blog\/a-rundown-on-analytics-friendly-big-data-technologies\/","title":{"rendered":"A Rundown on Analytics friendly Big Data technologies"},"content":{"rendered":"

The rise in unstructured data is driving organizations to optimise large workloads of data to enhance greater accuracy and prediction. Core software platforms and programming models focused on Distributed File System (DFS) for near real-time BD processing are being developed. With these software ecosystems as the mainstay,\u00a0established IT vendors are moving into the world of Big Data with offerings of software models and services. This calls for a certain level of proficiency in the software frameworks that drive Big Data analytics today<\/p>\n

Components of Big Data technologies<\/b><\/p>\n

\"BD<\/p>\n

An overview cannot be complete without these software underpinnings that form the core of the Big Data framework and computing environments; or the products that leverage these software ecosystems for data processing, storage and analytics. Products and services from the Big Data vendors leverage both proprietary and open source software and enterprise cloud platforms for their products, with built-in analytics engine.<\/p>\n

Apache Hadoop –\u00a0 the bedrock of\u00a0 Big Data legacy
\n<\/b><\/p>\n

The Apache Hadoop<\/a> is an open source project designed to run on commodity hardware. The extensive ecosystem allows cheap processing and insights into huge volumes of structured and unstructured data. Thus, Hadoop has been the driving force behind the growth of the Big Data Analytics industry, with spin-offs focused on in-database analytics in a BI environment. So it becomes increasingly necessary for those working with Big Data products or in a Big Data domain, to acquire Hadoop skills.<\/p>\n

R\u00a0 <\/strong>– from core statistical computing to an enterprise<\/strong> driven algorithm<\/strong><\/p>\n

The Statistical programming language \u2018R<\/a>,\u2019 is a long-time favoured tool for statistical presentations and analysis. With its open source platform, the full spectrum of R packages related to business analytics has caught the imagination of enterprise analytics. What\u2019s more, you can select the packages you need to process analytical tasks with ease, such as code examples on web and social media analytics, data mining, clustering and regression models. As an open source model, it lends its ability to be paired with other technologies and Big Data products. For traditional analysts, statisticians or wannabe Big Data learner, R offers an excellent learning curve.<\/p>\n

SAS<\/b><\/p>\n

The SAS<\/a> is a proprietary software suite focused on advanced analytics with implementation in a data management environment, with predictive insights as the driving force. It has been the most widely used software tool in sectors like insurance, finance, public health, scientific research, IT, retail and businesses. SAS is the standard statistical analysis software applied in many SMEs as well as the larger organizations that leverage the power of traditional SAS and Hadoop, to unleash the value of Big Data Analytics. SAS finds popularity amongst students of data science, analytics and IT who want to develop skillsets in the SAS environment.<\/p>\n

SPSS<\/b><\/p>\n

Another traditional statistics software package that is now a part of the IBM environment, the SPSS <\/a>is a widely used program for statistical analysis and reporting. Although its applications find use in almost every industry today, SPPS is radical to market researches, survey initiatives, business geographics and sociology studies. The IBM SPSS products leverage the SPSS workbench for data mining, desktop analytics and in-depth insights. As it is easy to learn and implement, SPSS finds popularity amongst the students of data science and analytics.<\/p>\n

Something old something new<\/b><\/p>\n

Big Data calls for technical skills that you will not find in organisational data centers or your traditional IT courses. You need additional certifications to work with the above software frameworks which complement expertise in database architecture, advanced Excel, noSQL and VBA.<\/p>\n","protected":false},"excerpt":{"rendered":"

The blog gives an overview of the components of big data technologies – both the software platforms and programming models as well as the Big Data offerings from IT vendors that leverage these software technologies. A short brief onf Haddop, R, SAS and SPSS is also included.<\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5,552],"tags":[22,71,43,67,72,64,68,70,69,65,73,30,66],"class_list":["post-4407","post","type-post","status-publish","format-standard","hentry","category-data-analytics","category-technology","tag-analytics","tag-apache-hadoop","tag-big-data","tag-big-data-vendors","tag-components-of-big-data-technologies","tag-hadoop","tag-infographics","tag-open-source","tag-proprietary-software","tag-r-ststistical-software","tag-rundown","tag-sas","tag-spss"],"yoast_head":"\nEssential Big Data Technologies for Analytics Success<\/title>\n<meta name=\"description\" content=\"Discover core Big Data tools like Hadoop, R, SAS, and SPSS that drive modern analytics, offering insights from vast datasets for competitive advantage.\" \/>\n<meta name=\"robots\" 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