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SF Data Weekly - 7 Deadly Data Viz Sins, BitTorrent Insights and the Golden Path

This week's pick is a deep dive into data visualizations that look nice but don't impart meaningful i
August 21 · Issue #151 · View online
SF Data Weekly
This week’s pick is a deep dive into data visualizations that look nice but don’t impart meaningful information – and instead of just complaining, the author gives you alternatives that are more meaningful (and still look good).
We also have a couple of pieces where we think data engineers and analysts can learn from software engineers at Github and Spotify. And we have a fun piece that examines BitTorrent downloads to see which countries care about global climate change.
Stay healthy!

Our Pick
7 Deadly Sins of Data Visualization | by Slava Shestopalov | Aug, 2020 | Muzli - Design Inspiration
Data Pipelines
Big Data Pipeline Recipe. Introduction | by Javier Ramos | Aug, 2020 | ITNEXT
The GitHub Blog: Why Write ADRs
Data Storage
Japan's Largest Mobile Payment Company Migrates from Aurora to a Scale-Out Database | PingCAP
Data Analysis
Data cleaning is finally being automated | by Jeremie Harris | Aug, 2020 | Towards Data Science - A new location database for the Internet
What makes a data analyst excellent? | by Cassie Kozyrkov | Aug, 2020 | Towards Data Science
Data Visualization
Can I Get That Viz in Excel Please? | by Vanitha Lucas | Nightingale | Aug, 2020 | Medium
Which Countries are Concerned about Climate Change? | by Deepak Karunakaran | Aug, 2020 | Towards Data Science
Data-driven Products
How We Use Golden Paths to Solve Fragmentation in Our Software Ecosystem : Spotify Engineering
Enhancing customer safety by leveraging the scalable, secure, and cost-optimized Toyota Connected Data Lake | Amazon Web Services
Data Engineering Jobs
Intermix - build better data products with analytics into your data warehouse. Complete visibility into your data platform, who is touching your data, and how it’s being used.
Xplenty. - the leading data integration platform to bring all your data sources together. Trusted by companies like Gap, Samsung, Ikea, and Masterclass for their data pipelines.

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