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Why Storage Is the Unsung Hero for AI

Pure Storage

Deep learning revolution (2012-2017): Models like AlexNet and ResNet pushed storage demands, while Word2Vec and GloVe advanced natural language processing, shifting to high-speed NVMe storage for terabyte-scale data sets. They lack the agility, performance, and scalability required to support AIs diverse and high-volume data requirements.

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The Rise of GraphQL

Advancing Analytics

Introduction: 2012 was a transformative year with the release of the transformers paper that underpins much of the recent hype behind ‘LLM’. It was originally designed to describe the capabilities of data models for client-server applications. With a world wide industry market cap of $7.1 What Scenarios would I use it in?

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Cities, Cultural Heritage and the Culture of Responding to Floods

Emergency Planning

Powerful floods struck Puerto Lumbreras again in 2012. Florence is an interesting case, as the floods that so severely damaged it in 1966 had world-wide repercussions, especially regarding the city's art and architectural treasures. It needs awareness, application, seriousness and solidarity. Let us hope that it becomes popular.

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The SSD Trap: How a Storage Solution’s Reliance on SSDs Can Impact You (Part 1 of 2)

Pure Storage

From 2012 to 2019, AFAs have risen in popularity and now drive approximately 80% or more of all storage shipments for performant application environments. This dichotomy continues to hamstring the innovation cycle for COTS SSDs and their application to enterprise markets. HDDs have essentially been left in the magnetic dust.

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National Coding Week: Upskill Your Coding Knowledge with Pure Storage

Pure Storage

How to Attach/Detach SQL Server Databases with PowerShell : Here are some scripts developed for the SQL Server 2012 Reference Architecture to automate how you attach and detach SQL Server databases. Concurrent Programming Case Study: Comparing Python, Go, and Rust: Writing applications that leverage concurrency is a challenge.

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Exploring the Differences: Multi-dimensional Modeling vs Relational Modeling in Analytical Systems

Advancing Analytics

To scale out we need a scaled storage layer, such as a Data Lake, to which the architecture is typically based on Hadoop, HDFS. Seldom we find the need for a relational system in a Data Lakehouse architecture - now I will caveat and say that is there is regularly need for a semantic layer, but that can take many different appearances.

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SIA New Member Profile: Sitasys AG

Security Industry Association

BS : Sitasys was founded in 2012 as a spinoff of Ascom. We want to give users the keys to managing their security operations and applications from a centralized platform with an unexceptional best experience at all times. Tell us the story of your company. Sitasys envisions democratizing access to alarm management.