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Data Engineers of Netflix?—?Interview with Pallavi Phadnis

The Netflix TechBlog

Interview with Pallavi Phadnis This post is part of our “ Data Engineers of Netflix ” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Pallavi Phadnis is a Senior Software Engineer at Netflix. What is your favorite project?

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A Recap of the Data Engineering Open Forum at Netflix

The Netflix TechBlog

Last but not least, thank you to the organizers of the Data Engineering Open Forum: Chris Colburn , Xinran Waibel , Jai Balani , Rashmi Shamprasad , and Patricia Ho. If you are interested in attending a future Data Engineering Open Forum, we highly recommend you join our Google Group to stay tuned to event announcements.

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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. Logs are automatically produced and time-stamped documentation of events relevant to cloud architectures.

Analytics 246
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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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Orchestrating Data/ML Workflows at Scale With Netflix Maestro

The Netflix TechBlog

by Jun He , Akash Dwivedi , Natallia Dzenisenka , Snehal Chennuru , Praneeth Yenugutala , Pawan Dixit At Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transformations.

Java 211
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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

For example, a job would reprocess aggregates for the past 3 days because it assumes that there would be late arriving data, but data prior to 3 days isn’t worth the cost of reprocessing. Backfill: Backfilling datasets is a common operation in big data processing. append, overwrite, etc.).

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Post: 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. Who's Hiring?

Education 105