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Presentation: Magic Pocket: Dropbox’s Exabyte-Scale Blob Storage System

InfoQ

Facundo Agriel dives into the architecture of Magic Pocket, some early key design patterns, and the challenges of operating such a system at this scale. By Facundo Agriel

Systems 52
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Building an elastic query engine on disaggregated storage

The Morning Paper

Building an elastic query engine on disaggregated storage , Vuppalapati, NSDI’20. This paper presents Snowflake design and implementation along with a discussion on how recent changes in cloud infrastructure (emerging hardware, fine-grained billing, etc.) But the ephemeral storage service for intermediate data is not based on S3.

Storage 112
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How To Implement Video Information and Editing APIs in Java

DZone

It must be said that this video traffic phenomenon primarily owes itself to modernizations in the scalability of streaming infrastructure, which simply weren’t present fifteen years ago.

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Key Advantages of DBMS for Efficient Data Management

Scalegrid

Despite initial investment costs, DBMS presents long-term savings and improved efficiency through automated processes, efficient query optimizations, and scalability, contributing to enhanced decision-making and end-user productivity. By implementing data abstraction techniques, these challenges can be addressed more effectively.

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Optimize your environment: Unveiling Dynatrace Hyper-V extension for enhanced performance and efficient troubleshooting

Dynatrace

Secondly, determining the correct allocation of resources (CPU, memory, storage) to each virtual machine to ensure optimal performance without over-provisioning can be difficult. This presents a challenge for IT operations teams, specifically in identifying and addressing performance issues or planning how to prevent future issues.

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Dynatrace and Red Hat expand enterprise observability to edge computing

Dynatrace

Observability on edge devices presents unique challenges compared to traditional data-center or cloud-based environments. But there’s more than just a need for minimizing resource (CPU, memory, storage) and network (bandwidth) consumption for observability at the edge. Remote management and automated alerting are, therefore, crucial.

Retail 258
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Improved Alerting with Atlas Streaming Eval

The Netflix TechBlog

While Atlas is architected around compute & storage separation, and we could theoretically just scale the query layer to meet the increased query demand, every query, regardless of its type, has a data component that needs to be pushed down to the storage layer.

Storage 288