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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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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
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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.

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Boost DevOps maturity with observability and a data lakehouse

Dynatrace

Data scale and silos present challenges to DevOps maturity DevOps teams often run into problems trying to drive better data-driven decisions with observability and security data. Research has found that 99% of organizations have embraced a multicloud architecture.

DevOps 183
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Percona Live Presents: An Open-Source, Cloud Native Database

Percona Community

During our presentation at Percona Live 2019 Intel and its software partners will introduce the audience to the work we’re doing to enable an open-source framework, we call Cloud Native Database. We will discuss Rockset’s RocksDB-Cloud library and how it works with Facebook’s MyRocks storage engine.

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Getting answers from data starts with automated log acquisition, at any scale

Dynatrace

All this is easier said than done because: Kubernetes-based dynamic architecture is becoming the norm. If a more granular rule is present on the host level, that rule will precede any blanket rule on, for example, the tenant level. Dynamic landscape and data handling requirements result in manual work. Host group. Try it out yourself.

Storage 226