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Artificial Intelligence in Cloud Computing

Scalegrid

Exploring artificial intelligence in cloud computing reveals a game-changing synergy. AI algorithms embedded in cloud architecture automate repetitive processes, streamlining workloads and reducing the chance of human error. <p>The post Artificial Intelligence in Cloud Computing first appeared on ScaleGrid.</p>

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What is a Distributed Storage System

Scalegrid

A distributed storage system is foundational in today’s data-driven landscape, ensuring data spread over multiple servers is reliable, accessible, and manageable. Understanding distributed storage is imperative as data volumes and the need for robust storage solutions rise.

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

Scalegrid

In this blog post, we explain what Greenplum is, and break down the Greenplum architecture, advantages, major use cases, and how to get started. It’s architecture was specially designed to manage large-scale data warehouses and business intelligence workloads by giving you the ability to spread your data out across a multitude of servers.

Big Data 321
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AWS re:Invent 2023 guide: Generative AI takes a front seat

Dynatrace

Across the cloud operations lifecycle, especially in organizations operating at enterprise scale, the sheer volume of cloud-native services and dynamic architectures generate a massive amount of data. Causal AI is an artificial intelligence technique used to determine the precise underlying causes and effects of events. Using

AWS 206
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What is a data lakehouse? Combining data lakes and warehouses for the best of both worlds

Dynatrace

While data lakes and data warehousing architectures are commonly used modes for storing and analyzing data, a data lakehouse is an efficient third way to store and analyze data that unifies the two architectures while preserving the benefits of both. Unlike data warehouses, however, data is not transformed before landing in storage.

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The history of Grail: Why you need a data lakehouse

Dynatrace

Grail architectural basics. The aforementioned principles have, of course, a major impact on the overall architecture. A data lakehouse addresses these limitations and introduces an entirely new architectural design. This decoupling ensures the openness of data and storage formats, while also preserving data in context.

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Any analysis, any time: Dynatrace Log Management and Analytics powered by Grail

Dynatrace

Modern IT environments — whether multicloud, on-premises, or hybrid-cloud architectures — generate exponentially increasing data volumes. Teams have introduced workarounds to reduce storage costs. Stop worrying about log data ingest and storage — start creating value instead. Limited data availability constrains value creation.

Analytics 231