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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. Understand resource usage by tracking Metrics 3.4.

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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! In this video, Sr.

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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. Missing data? What are the common challenges faced by engineers and data practitioners in organizing the metadata for their systems?

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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. Psyberg: The Game Changer!

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Metadata Management And Integration At LinkedIn With DataHub

Data Engineering Podcast

Summary In order to scale the use of data across an organization there are a number of challenges related to discovery, governance, and integration that need to be solved. The key to those solutions is a robust and flexible metadata management system. If you hand a book to a new data engineer, what wisdom would you add to it?

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Data Engineering Best Practices - #1. Data flow & Code

Start Data Engineering

Ensure data is valid before exposing it to its consumers (aka data quality checks) 3.3. Avoid data duplicates with idempotent pipelines 3.4. Write DRY code & keep I/O separate from data transformation 3.5. Know the when, how, & what (aka metadata) of pipeline runs for easier debugging 3.

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