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Computational Storage Reinvigorates Storage in a Novel Way

In this special guest feature, Scott Shadley, VP at NGD Systems, discusses the SNIA Computational Storage (CS) working group’s technical progress since its inception and how they plan to make computational storage mainstream over the next year. The group just finished a long, painstaking process to establish formal definitions and terminology to properly categorize and standardize computational storage technologies. This work was done by the body’s over 40 participating companies and 148 individual members.

“Above the Trend Line” – Your Industry Rumor Central for 11/11/2019

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, financial results, industry alignments, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

Companies Are Bringing Data Back from the Cloud. Now They Need a Place to Put It

In this special guest feature, Shridar Subramanian, Vice President of Global Product Management and Marketing at StorageCraft, discusses the trend called “cloud data repatriation,” and how it appears to be gaining steam. A rethinking is happening where companies are looking to return at least some of their core data and applications to their on-premises data centers.

Do You Actually Need a Data Lake?

In this contributed article, Eran Levy, Director of Marketing at Upsolver, sets out to formally define “data lake” and then goes on to ask whether your organization needs a data lake by examining 5 key indicators. Data lakes have become the cornerstone of many big data initiatives, just as they offer easier and more flexible options to scale when working with high volumes of data that’s being generated at a high velocity – such as web, sensor or app activity data.

Why Databases are Failing the Modern Economy

In this contributed article, industry luminary Andrew “Flip” Filipowski discusses the state of database technology in terms of how in spite of the massive increase in demand for data, the industry never underwent a complete overhaul of the way databases work. Instead, programmers have used patchwork retrofitting to adjust databases to modern-day needs, solving problems with countless point solutions instead of rethinking the way databases work from the ground up.

Okera Delivers Industry’s First Real-Time Actionable Insights into Data Lakes

Okera, a leading active data management platform that enables companies to discover, audit, and protect data at scale, announced Okera Spotlight for Amazon Web Services (AWS) users, the first and only solution to provide full visibility with real-time and continuous audit of your Amazon Simple Storage Service (S3) data lake.

Micron Introduces Comprehensive AI Development Platform

Micron Technology, Inc. (Nasdaq: MU), announced a powerful new set of high-performance hardware and software tools for deep learning applications with the acquisition of FWDNXT, a software and hardware startup. When combined with advanced Micron memory, FWDNXT’s (pronounced “forward next”) artificial intelligence (AI) hardware and software technology enables Micron to explore deep learning solutions required for data analytics, particularly in IoT and edge computing.

Hope is Not a Strategy for Deriving Value from a Data Lake

In this contributed article, Christian Kleinerman, VP of Product at Snowflake, discusses how the data lake has evolved since arriving on the technology scene 10+ years ago and why organizations need more than “hope” to unlock the potential it promises.

The insideBIGDATA IMPACT 50 List for Q4 2019

The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List of the most important movers and shakers in our industry. These companies have proven their relevance by the way they’re impacting the enterprise through leading edge products and services. We’re happy to publish this evolving list of the industry’s most impactful companies!

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads – Part 3

Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. In this technology guide, insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads, we’ll see how traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the-art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model evaluation.