3/ Good reads & videos
- In this article, Ronald Angel has shared how @MiroHQ leverages Agile concepts to drive data engineering projects.
"Agile Data Engineering at Miro"
Here's the link๐
https://t.co/jol59PLJGF
[NEW RELEASE] 97 Things Every Data Engineer Should Know -- Edited by Tobias Macey, host of @DataEngPodcast, this book presents 97 concise & useful tips for cleaning, prepping, wrangling, storing, processing, & ingesting data. https://t.co/WdiN6vNQHT #OReillyExperts#NewBook
Moving from buckets to vectors: How to use Machine Learning to quantify how similar vehicles are to each other https://t.co/es1e17rZ5p via @autotraderlife
This Professional Development workshop will describe how @AutoTrader_UK formed a cross-functional cross-tribe team to focus on User Analytics and how they used Snowplow Analytics to move our capabilities on.
Book now: https://t.co/TSE93WIyVR
We're about to get started with Managing Data Downtime on @OReillyMedia! ๐ฅณ๐ Tune in to learn modern data teams apply DevOps and Observability to their data pipelines. https://t.co/7jsqfeFKPk #data#dataquality#dataobservability#devops
@joshsh Dragon seems super useful. I can see clear use cases for me at work; would love to see it open sourced.
Thanks for the link to the paper. It's super interesting to see ideas from RDF/OWL catching on in the data space.