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Data Engineers of Netflix?—?Interview with Pallavi Phadnis

The Netflix TechBlog

Data Engineers of Netflix?—?Interview 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.

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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

It also improves the engineering productivity by simplifying the existing pipelines and unlocking the new patterns. In this blog post, we talk about the landscape and the challenges in workflows at Netflix. Backfill: Backfilling datasets is a common operation in big data processing. data arrives too late to be useful).

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

The Netflix TechBlog

Finally, imagine yourself in the role of a data platform reliability engineer tasked with providing advanced lead time to data pipeline (ETL) owners by proactively identifying issues upstream to their ETL jobs. Design a flexible data model ? —?Represent Enable seamless integration?—? push or pull.

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

The Netflix TechBlog

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. Right Sizing is in progress and will be covered with more details in a dedicated technical blog post later. Stay tuned.

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Less is More: Engineering Data Warehouse Efficiency with Minimalist Design

Uber Engineering

Once identified, … The post Less is More: Engineering Data Warehouse Efficiency with Minimalist Design appeared first on Uber Engineering Blog. In our experience, optimizing for operational efficiency requires answering one key question: for which tables does the maintenance cost supersede utility?

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Optimizing data warehouse storage

The Netflix TechBlog

We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits. 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.

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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. In this blog, we use step and job interchangeably.

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