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Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and…

The Netflix TechBlog

Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and Efficiency By: Di Lin , Girish Lingappa , Jitender Aswani Imagine yourself in the role of a data-inspired decision maker staring at a metric on a dashboard about to make a critical business decision but pausing to ask a question?—?“Can

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A Recap of the Data Engineering Open Forum at Netflix

The Netflix TechBlog

This talk will delve into the creative solutions Netflix deploys to manage this high-volume, real-time data requirement while balancing scalability and cost. To handle errors efficiently, Netflix developed a rule-based classifier for error classification called “Pensive.”

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Orchestrating Data/ML Workflows at Scale With Netflix Maestro

The Netflix TechBlog

As Big data and ML became more prevalent and impactful, the scalability, reliability, and usability of the orchestrating ecosystem have increasingly become more important for our data scientists and the company. Another dimension of scalability to consider is the size of the workflow.

Java 208
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Conducting log analysis with an observability platform and full data context

Dynatrace

Causal AI—which brings AI-enabled actionable insights to IT operations—and a data lakehouse, such as Dynatrace Grail , can help break down silos among ITOps, DevSecOps, site reliability engineering, and business analytics teams.

Analytics 196
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Data Engineers of Netflix?—?Interview with Dhevi Rajendran

The Netflix TechBlog

Dhevi joined Netflix in July 2020 and is one of many Data Engineers who have onboarded remotely during the pandemic. In this post, Dhevi talks about her passion for data engineering and taking on a new role during the pandemic. One great thing about working with data is the impact you can create as an engineer.

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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. Backfill: Backfilling datasets is a common operation in big data processing. append, overwrite, etc.).

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Post: InterviewCamp.io, Scrapinghub, Fauna, Sisu, Educative, PA File Sight, Etleap, Triplebyte, Stream

High Scalability

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). this is going to be a challenging journey for any backend engineer! T riplebyte lets exceptional software engineers skip screening steps at hundreds of top tech companies like Apple, Dropbox, Mixpanel, and Instacart. Apply here.

Education 105