Notes about Cutting-Edge Technologies and Everything

While traditional data warehouses often took years to build, deploy, and reap benefits from, today's organizations want simple, agile, integrated, cost-effective, and highly automated solutions to support insights. In addition, traditional architectures are failing to meet new business requirements, especially around high-speed data streaming, real-time analytics, large volumes of messy and complex data sets, and self-service. As a result, firms are revisiting their data architectures, looking for ways to modernize to support new requirements. DMA is a modern architecture that minimizes the complexity of messy data and hides heterogeneity by embodying a trusted model and integrated policies and by adapting to changing business requirements. It leverages metadata, in-memory, and distributed data repositories, running on-premises or in the cloud, to deliver scalable and integrated analytics. Adoption of DMA will grow further as enterprise architects look at overcoming data challeng...

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...

Analytics at the speed of transactions has become an important agenda item for organizations. Translytical data platforms, an emerging technology, deliver faster access to business data to support various workloads and use cases. EA pros can use them to drive new business initiatives. Forrester identified the 12 most significant translytical vendors — Aerospike, DataStax, GigaSpaces, IBM, MemSQL, Microsoft, NuoDB, Oracle, Redis Labs, SAP, Splice Machine, and VoltDB — and researched, analyzed, and scored them against 25 criteria. Details >> (the link is provided by DataStax here ) The Forrester Wave™: Translytical Data Platforms, Q4 2017:

Details >> (shared by MemSQL here )