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Presentation: Azure Cosmos DB: Low Latency and High Availability at Planet Scale

InfoQ

Mei-Chin Tsai, Vinod discuss the internal architecture of Azure Cosmos DB and how it achieves high availability, low latency, and scalability. By Mei-Chin Tsai, Vinod Sridharan

Latency 52
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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

Whether in analyzing A/B tests, optimizing studio production, training algorithms, investing in content acquisition, detecting security breaches, or optimizing payments, well structured and accurate data is foundational. Users configure the workflow to read the data in a window (e.g. data arrives too late to be useful).

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Optimizing data warehouse storage

The Netflix TechBlog

This article will list some of the use cases of AutoOptimize, discuss the design principles that help enhance efficiency, and present the high-level architecture. Some of the optimizations are prerequisites for a high-performance data warehouse. Both automatic (event-driven) as well as manual (ad-hoc) optimization.

Storage 203
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How LinkedIn Serves Over 4.8 Million Member Profiles per Second

InfoQ

The new solution achieved over 99% hit rate, helped reduce tail latencies by more than 60% and costs by 10% annually. LinkedIn introduced Couchbase as a centralized caching tier for scaling member profile reads to handle increasing traffic that has outgrown their existing database cluster. By Rafal Gancarz

Cache 83
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Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data…

The Netflix TechBlog

Evolving to Auto Remediation: Service Architecture Methodology To address the above-mentioned challenges, our basic methodology is to integrate the rule-based classifier with an ML service to generate recommendations, and use a configuration service to apply the recommendations automatically: Generating recommendations.

Tuning 210
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Friends don't let friends build data pipelines

Abhishek Tiwari

Unfortunately, building data pipelines remains a daunting, time-consuming, and costly activity. Not everyone is operating at Netflix or Spotify scale data engineering function. Often companies underestimate the necessary effort and cost involved to build and maintain data pipelines.

Latency 63
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5 data integration trends that will define the future of ETL in 2018

Abhishek Tiwari

In 2018, we anticipate that ETL will either lose relevance or the ETL process will disintegrate and be consumed by new data architectures. Unified data management architecture. A unified data management (UDM) system combines the best of data warehouses, data lakes, and streaming without expensive and error-prone ETL.