Notes about Cutting-Edge Technologies and Everything

Datanami  is pleased to announce the results of its fourth annual Readers’ and Editors’ Choice Awards, which recognizes the companies, products, and projects that have made a difference in the big data community this year. These awards, which are nominated and voted on by Datanami readers, give us insight into the state of the community. We’d like to thank our dedicated readers for weighing in on their top picks for the best in big data. It’s been a privilege for us to present these awards, and we extend our congratulations to this year’s winners. Best Big Data Product or Technology: Machine Learning Readers’ Choice: Elastic Editor’s Choice: SAS Visual Data Mining & Machine Learning Best Big Data Product or Technology: Internet of Things Readers’ Choice: SAS Analytics for IoT Editor’s Choice:  The Striim Platform Best Big Data Product or Technology: Big Data Security Readers’ Choice: Cloudera Enterprise Editor’s Choice: Elastic Stack Best Big ...

The Five Use Cases and 15 Critical Capabilities of an Analytics and BI Platform We define and assess product capabilities across the following five use cases: Agile, centralized BI provisioning: Supports an agile IT-enabled workflow, from data to centrally delivered and managed analytic content, using the platform’s self-contained data management capabilities. Decentralized analytics: Supports a workflow from data to self-service analytics, and includes analytics for individual business units and users. Governed data discovery: Supports a workflow from data to self-service analytics to system of record (SOR), IT-managed content with governance, reusability and promotability of user-generated content to certified data and analytics content. OEM or embedded analytics: Supports a workflow from data to embedded BI content in a process or application. Extranet deployment: Supports a workflow similar to agile, centralized BI provisioning for the external customer or, in the pu...

The best trained soldiers can’t fulfill their mission empty-handed. Data scientists have their own weapons  —  machine learning (ML) software. There is already a cornucopia of articles listing reliable machine learning tools with in-depth descriptions of their functionality. Our goal, however, was to get the feedback of industry experts. And that’s why we interviewed data science practitioners — gurus, really  — regarding the useful tools they choose for  their  projects. The specialists we contacted have various fields of expertise and are working in such companies as Facebook and Samsung. Some of them represent AI startups (Objection Co, NEAR.AI, and Respeecher); some teach at universities (Kharkiv National University of Radioelectronics). The AltexSoft data science team joined the discussion, too. And if you’re looking for a particular type of tools, just skip to your sector of interest: Languages used in machine learning Data analytics an...

Modern analytics and business intelligence platforms represent mainstream buying, with deployments increasingly cloud-based. Data and analytics leaders are upgrading traditional solutions as well as expanding portfolios with new vendors as the market innovates on ease of use and augmented analytics. Details >>   (Provided by Looker here )

Excited about new Tableau 10.5 with Hyper added as a data engine and Linux support. New features: https://www.tableau.com/products/new-features Hyper: https://www.tableau.com/products/technology

Details >> (provided by Tableau here )

(originally this article was created and posted by me on March 7, 2016 at datasciencecentral.com, now I am transferring it here) I have spent many hours planning and executing in-company self-service BI implementation. This enabled me to gain several insights. Now that the ideas became mature enough and field-proven, I believe they are worth sharing. No matter how far you are in toying with potential approaches (possibly you are already in the thick of it!), I hope my attempt of describing feasible scenarios would provide a decent foundation.   All scenarios presume that IT plays its main role by owning the infrastructure, managing scalability, data security, and governance. Scenario 1. Tableau Desktop + departmental/cross-functional data schemas. This scenario involves gaining insights by data analysts on a daily basis. They might be either independent individuals or a team. Business users’ interaction with published workbooks is applicable, but limited to simple filterin...