Remove Data Remove Data Pipeline Remove Engineering Remove Metadata
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Data Engineering Best Practices - #2. Metadata & Logging

Start Data Engineering

Data Pipeline Logging Best Practices 3.1. Metadata: Information about pipeline runs, & data flowing through your pipeline 3.2. Introduction 2. Setup & Logging architecture 3. Obtain visibility into the code’s execution sequence using text logs 3.3. Monitoring UI & Traceability 3.5.

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Data Pipeline Observability: A Model For Data Engineers

Databand.ai

Data Pipeline Observability: A Model For Data Engineers Eitan Chazbani June 29, 2023 Data pipeline observability is your ability to monitor and understand the state of a data pipeline at any time. We believe the world’s data pipelines need better data observability.

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CI/CD for Data Pipelines: A Game-Changer with AnalyticsCreator

Data Science Blog: Data Engineering

Continuous Integration and Continuous Delivery (CI/CD) for Data Pipelines: It is a Game-Changer with AnalyticsCreator! The need for efficient and reliable data pipelines is paramount in data science and data engineering. They transform data into a consistent format for users to consume.

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How to learn data engineering

Christophe Blefari

Learn data engineering, all the references ( credits ) This is a special edition of the Data News. But right now I'm in holidays finishing a hiking week in Corsica 🥾 So I wrote this special edition about: how to learn data engineering in 2024. Who are the data engineers?

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Our First Netflix Data Engineering Summit

Netflix Tech

Engineers from across the company came together to share best practices on everything from Data Processing Patterns to Building Reliable Data Pipelines. The result was a series of talks which we are now sharing with the rest of the Data Engineering community!

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1. Streamlining Membership Data Engineering at Netflix with Psyberg

Netflix Tech

By Abhinaya Shetty , Bharath Mummadisetty At Netflix, our Membership and Finance Data Engineering team harnesses diverse data related to plans, pricing, membership life cycle, and revenue to fuel analytics, power various dashboards, and make data-informed decisions. What is late-arriving data? Let’s dive in!

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Eliminate Friction In Your Data Platform Through Unified Metadata Using OpenMetadata

Data Engineering Podcast

Summary A significant source of friction and wasted effort in building and integrating data management systems is the fragmentation of metadata across various tools. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform.

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