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Causal AI use cases for modern observability that can transform any business

Dynatrace

Healthcare. For example, causal AI can help public health officials better understand the effects of environmental factors, healthcare policies, and social factors on health outcomes. Retailers can analyze how factors such as demand, competition, and market trends affect pricing.

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Kubernetes in the wild report 2023

Dynatrace

Redis is an in-memory key-value store and cache that simplifies processing, storage, and interaction with data in Kubernetes environments. Note: The survey excluded all commercial observability offerings, including Dynatrace. Databases : Among databases, Redis is the most used at 60%.

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Observations on the Importance of Cloud-based Analytics

All Things Distributed

AWS is enabling innovations in areas such as healthcare, automotive, life sciences, retail, media, energy, robotics that it is mind boggling and humbling. This is being called Video Content Analysis (VCA) and it has many application areas from retail to transportation. Cloud enables secure analytics.

Analytics 136
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5 industries that demonstrate how blockchains go beyond finance

O'Reilly

Gem and Tierion are startups working different aspects of data storage, verification and sharing (both partnered with Philips Healthcare), while Hu-manity.co More than 100 companies are involved with IBM’s Food Trust network, including many consumer packaged goods companies and grocery retailers.

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Dutch Enterprises and The Cloud

All Things Distributed

Due to the exponential growth of the biology and informatics fields, Unilever needs to maintain this new program within a highly-scalable environment that supports parallel computation and heavy data storage demands.

Cloud 130
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What is Greenplum Database? Intro to the Big Data Database

Scalegrid

High performance, query optimization, open source and polymorphic data storage are the major Greenplum advantages. Polymorphic Data Storage. Greenplum’s polymorphic data storage allows you to control the configuration for your table and partition storage with the freedom to execute and compress files within it at any time.

Big Data 321
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10 Lessons from 10 Years of Amazon Web Services

All Things Distributed

This becomes an even more important lesson at scale: for example, as S3 processes trillions and trillions of storage transactions, anything that has even the slightest probability of error will become realistic. If customers have many tiny files, then storage and bandwidth don’t amount to much even if they are making millions of requests.

AWS 147