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Time Series Analysis: VAR-Model-As-A-Service Using Flask and MinIO

DZone

It is the second of a series of articles that is built on top of that project, representing experiments with various statistical and machine learning models, data pipelines implemented using existing DAG tools, and storage services, both cloud-based and alternative on-premises solutions.

Storage 264
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What is Greenplum Database? Intro to the Big Data Database

Scalegrid

It can scale towards a multi-petabyte level data workload without a single issue, and it allows access to a cluster of powerful servers that will work together within a single SQL interface where you can view all of the data. High performance, query optimization, open source and polymorphic data storage are the major Greenplum advantages.

Big Data 321
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Introducing Netflix’s Key-Value Data Abstraction Layer

The Netflix TechBlog

Our goal was to build a versatile and efficient data storage solution that could handle a wide variety of use cases, ranging from the simplest hashmaps to more complex data structures, all while ensuring high availability, tunable consistency, and low latency. Developers just provide their data problem rather than a database solution!

Latency 261
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Reducing Your Database Hosting Costs: DigitalOcean vs. AWS vs. Azure

Scalegrid

In this article, we are going to compare three of the most popular cloud providers, AWS vs. Azure vs. DigitalOcean for their database hosting costs for MongoDB® database to help you decide which cloud is best for your business. See performance tests to determine the impact of the Meltdown CPU kernel patch on your MongoDB servers.

Azure 344
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Using Diskspd to test SQL Server Storage Subsystems

SQL Shack

In this article, we will learn how to test our storage subsystems performance using Diskspd. The storage subsystem is one of the key performance factors for SQL Server because SQL Server storage engine stores database objects, tables, and indexes on the physical files.

Storage 82
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Why growing AI adoption requires an AI observability strategy

Dynatrace

AI requires more compute and storage. Training AI data is resource-intensive and costly, again, because of increased computational and storage requirements. As a result, AI observability supports cloud FinOps efforts by identifying how AI adoption spikes costs because of increased usage of storage and compute resources.

Strategy 228
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The Anna Key-Value Store Now Has 355x the Performance of DynamoDB for the Dollar

High Scalability

They've posted about Anna's new superpowers in Going Fast and Cheap: How We Made Anna Autoscale : Using Anna v0 as an in-memory storage engine, we set out to address the cloud storage problems described above. Each storage server collects statistics about the requests it serves, the data it stores, etc. Related Articles.

Storage 199