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Probabilistic Data Structures for Web Analytics and Data Mining

Highly Scalable

Statistical analysis and mining of huge multi-terabyte data sets is a common task nowadays, especially in the areas like web analytics and Internet advertising. Analysis of such large data sets often requires powerful distributed data stores like Hadoop and heavy data processing with techniques like MapReduce.

Analytics 191
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What is IT automation?

Dynatrace

Automating IT practices without integrated AIOps presents several challenges. This kind of automation can support key IT operations, such as infrastructure, digital processes, business processes, and big-data automation. Big data automation tools. The challenges of automating IT and how to combat them.

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DynatraceGo! APAC 2021: Lessons in thick data and keeping pace with the market

Dynatrace

She dispelled the myth that more big data equals better decisions, higher profits, or more customers. Investing in data is easy but using it is really hard”. The fact is, data on its own isn’t meaningful. Tricia quoted the statistic that companies typically use 3% of their data to inform decisions.

DevOps 246
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An overview of end-to-end entity resolution for big data

The Morning Paper

An overview of end-to-end entity resolution for big data , Christophides et al., It’s an important part of many modern data workflows, and an area I’ve been wrestling with in one of my own projects. ACM Computing Surveys, Dec. 2020, Article No. More sophisticated methods may also split and merge blocks.

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Bulldozer: Batch Data Moving from Data Warehouse to Online Key-Value Stores

The Netflix TechBlog

Data scientists and engineers collect this data from our subscribers and videos, and implement data analytics models to discover customer behaviour with the goal of maximizing user joy. Therefore, we must efficiently move data from the data warehouse to a global, low-latency and highly-reliable key-value store.

Latency 248
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Experiences with approximating queries in Microsoft’s production big-data clusters

The Morning Paper

Experiences with approximating queries in Microsoft’s production big-data clusters Kandula et al., I’ve been excited about the potential for approximate query processing in analytic clusters for some time, and this paper describes its use at scale in production. VLDB’19. Approximate query support.

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What is a Distributed Storage System

Scalegrid

Challenges and Considerations in Distributed Storage Deployment Although distributed storage systems offer significant advantages, they also present distinct challenges that must be addressed. These distributed storage services also play a pivotal role in big data and analytics operations.

Storage 130