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Last Mile Data Processing with Ray

Pinterest Engineering

Raymond Lee | Software Engineer II; Qingxian Lai | Sr. Software Engineer; Karthik Anantha Padmanabhan | Manager II, Engineering; Se Won Jang | Manager II, Engineering Photo by Claudio Schwarz on Unsplash Our mission at Pinterest is to bring everyone the inspiration to create the life they love.

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30+ Free Datasets for Your Data Science Projects in 2023

Knowledge Hut

Whether you are working on a personal project, learning the concepts, or working with datasets for your company, the primary focus is a data acquisition and data understanding. Your data should possess the maximum available information to perform meaningful analysis. What is a Data Science Dataset?

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Data Engineering Weekly #173

Data Engineering Weekly

[link] Meta: Composable data management at Meta Meta writes about its transition to a composable data management system to improve interoperability, reusability, and engineering efficiency. Zillow writes about its approach to building easy-to-use data that adopts certified datasets, aka data products.

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Modern Data Engineering

Towards Data Science

Platform Specific Tools and Advanced Techniques Photo by Christopher Burns on Unsplash The modern data ecosystem keeps evolving and new data tools emerge now and then. In this article, I want to talk about crucial things that affect data engineers. It was created by Spotify to manage massive data processing workloads.

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Open-Sourcing AvroTensorDataset: A Performant TensorFlow Dataset For Processing Avro Data

LinkedIn Engineering

To remove this bottleneck, we built AvroTensorDataset , a TensorFlow dataset for reading, parsing, and processing Avro data. AvroTensorDataset speeds up data preprocessing by multiple orders of magnitude, enabling us to keep site content as fresh as possible for our members. avro", "part-00001.avro"], Default is zero.

Datasets 102
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Functional Data Engineering — a modern paradigm for batch data processing

Maxime Beauchemin

Batch data processing  — historically known as ETL —  is extremely challenging. In this post, we’ll explore how applying the functional programming paradigm to data engineering can bring a lot of clarity to the process. It’s time-consuming, brittle, and often unrewarding.

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Improving Recruiting Efficiency with a Hybrid Bulk Data Processing Framework

LinkedIn Engineering

Data consistency, feature reliability, processing scalability, and end-to-end observability are key drivers to ensuring business as usual (zero disruptions) and a cohesive customer experience. With our new data processing framework, we were able to observe a multitude of benefits, including 99.9%