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Deployment of Exabyte-Backed Big Data Components

LinkedIn Engineering

The historical upgrade system couldn’t adapt to architectural changes like the introduction of an observer namenode (Now handling a massive influx of read requests – 150K QPS – from services such as Trino ), ZKFC auto-failover, HDFS federation , etc.

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AI at Scale isn’t Magic, it’s Data – Hybrid Data

Cloudera

In the article, Bret Greenstein, data, analytics and AI partner at PwC identifies that, “No matter how organizations move toward scaling AI in the coming year, it’s important to understand the significant differences between using AI as a ‘proof of concept’ and scaling those efforts.” But it isn’t just aggregating data for models.

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Building Real-time Machine Learning Foundations at Lyft

Lyft Engineering

In early 2022, Lyft already had a comprehensive Machine Learning Platform called LyftLearn composed of model serving , training , CI/CD, feature serving , and model monitoring systems. However, streaming data was not supported as a first-class citizen across many of the platform’s systems — such as training, complex monitoring, and others.

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AWS Glue-Unleashing the Power of Serverless ETL Effortlessly

ProjectPro

Do ETL and data integration activities seem complex to you? Read this blog to understand everything about AWS Glue that makes it one of the most popular data integration solutions in the industry. Did you know the global big data market will likely reach $268.4 Businesses are leveraging big data now more than ever.

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Using Metrics Layer to Standardize and Scale Experimentation at DoorDash

DoorDash Engineering

Challenges of ad-hoc SQLs Our initial goal with Curie was to standardize the analysis methodologies and simplify the experiment analysis process for data scientists. Core Data Models / Semantics We placed a strong emphasis on identifying the most comprehensive and effective core data models for users to create their own metrics.

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Evolution of ML Fact Store

Netflix Tech

An example of data about members is the video they had watched or added to their My List. An example of video data is video metadata, like the length of a video. These facts are managed and made available by services like viewing history or video metadata services outside of Axion. Was data corrupted at rest?

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Evolution of Streaming Pipelines in Lyft’s Marketplace

Lyft Engineering

The team needed better infrastructure to make the dynamic pricing system more reactive for the following reasons: Decrease end-to-end latency that would make the system more reactive to marketplace imbalances. Simplifying pipeline creation We saw an explosion of use cases at Lyft after our systems became GA.

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