Remove product data-orchestration
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Data Orchestration Trends: The Shift From Data Pipelines to Data Products

Simon Späti

Data consumers, such as data analysts, and business users, care mostly about the production of data assets. On the other hand, data engineers have historically focused on modeling the dependencies between tasks (instead of data assets) with an orchestrator tool. How can we reconcile both worlds?

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Data Orchestration Trends: The Shift From Data Pipelines to Data Products

Simon Späti

Data consumers, such as data analysts, and business users, care mostly about the production of data assets. On the other hand, data engineers have historically focused on modeling the dependencies between tasks (instead of data assets) with an orchestrator tool. How can we reconcile both worlds?

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A Notebook is all I want or Don't

Data Engineering Weekly

People have reservations about using tools like Jupytor Notebook for the production pipeline for a good reason. Let’s take a few common criticisms about running Notebook in production. Dependency management is also bound to the individual scope of the Notebook and prevents production-grade code quality.

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Data News — Week 24.03

Christophe Blefari

We are deep in the winter, it's time for comfy Data News to read near the fire 🔥 This week, on Monday, I started my annual university lecture. It deeply shows how OpenAI products are—or might be—used in order to win races. Then CastorDoc explains why a data catalog can help you overlook how to be compliant.

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Data News — Week 23.37

Christophe Blefari

Facing the News ( credits ) Hello Data News readers. If you're late to the party and you need fresh views on LLMs Daniel wrote an introduction demystifying the Large Language Models and Jesse wrote about LLMs impact from a Data Engineering perspective. Also productivity ≠ speed, but speed is important.

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Deployment of Exabyte-Backed Big Data Components

LinkedIn Engineering

Co-authors: Arjun Mohnot , Jenchang Ho , Anthony Quigley , Xing Lin , Anil Alluri , Michael Kuchenbecker LinkedIn operates one of the world’s largest Apache Hadoop big data clusters. Historically, deploying code changes to Hadoop big data clusters has been complex.

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Supporting Diverse ML Systems at Netflix

Netflix Tech

Berg , Romain Cledat , Kayla Seeley , Shashank Srikanth , Chaoying Wang , Darin Yu Netflix uses data science and machine learning across all facets of the company, powering a wide range of business applications from our internal infrastructure and content demand modeling to media understanding.

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