|
October 1 · Issue #209 · View online |
|
Our pick this week is what the author calls an “unhinged rant” about visualizations that look good but don’t tell you anything about the state of the world. We also have perfect workflows using Prefect, and Nim instead of Python. Stay healthy!
|
|
|
Towards a Theory of B.S. Visualization | Tableau Research
From the author: “In this unhinged rant, I lay out my suspicion that a lot of visualizations are b.s.: charts that do not have even the common decency to intentionally lie but are totally unconcerned about the state of the world or any practical utility. I suspect that b.s. charts take up a large fraction of the time and attention of actual visualization producers and consumers, and yet are seemingly absent from academic research into visualization design.”
|
|
Scaling Your Prefect Workflow to the Cloud | by Richard Pelgrim | Sep, 2021 | Towards Data Science
Prefect is a popular open-source Python library for automated workflow orchestration.
|
How to get real-time data insights to Amazon Redshift and Snowflake | FlyData
Real-time data replication is needed by many industries including eCommerce, Fintech, Healthcare, and SaaS. If you need real-time data insights, FlyData could be a good solution. [Sponsored]
|
|
Comparing BigQuery Processing and Spark Dataproc | by Vignesh Raj K | The PayPal Technology Blog | Sep, 2021 | Medium
PayPal is in the process of migrating its analytical workloads to Google Cloud Processing (GCP). This post covers the pros and cons of an on-premises Spark/Hadoop solution versus the GCP cloud solution.
|
Creating a Metadata Architecture From the Ground Up | by Marcelo Alves Baratela | QuintoAndar Tech Blog | Sep, 2021 | Medium
Part of data storage is deciding which data to store and how long to store it. This piece examines the path one Brazilian technology company took to create a data governance framework.
|
|
Make Python Faster with CFFI Python Bindings | by Dimitris Poulopoulos | Sep, 2021 | Towards Data Science
Make Python run faster with CFFI and ctypes.
|
Why I Use Nim instead of Python for Data Processing :: Benjamin D. Lee — Research Software Engineer
Nim is as easy to write as Python and as fast as C, and Benjamin Lee makes the case using examples from his experience at NIH.
|
|
How to Iterate on a CDC Health Advisory Graphic | Nightingale
A nice walk-through of a potential redesign of the CDC’s COVID data visualizations.
|
Data Visualization Has a Taxonomy Problem | by Elijah Meeks | Noteable | Sep, 2021 | Medium
A taxonomy is simply a classification system, and this piece argues that data visualization is lacking one, and it’s a problem.
|
|
How Airbnb Enables Consistent Data Consumption at Scale | by Shao Xie | The Airbnb Tech Blog | Sep, 2021 | Medium
By building an API layer that serves metrics, Airbnb has created an interface layer between upstream data models and downstream applications.
|
How NortonLifelock built a serverless architecture for real-time analysis of their VPN usage metrics | Amazon Web Services
This post presents a reference architecture and optimization strategies for building serverless data analytics solutions on AWS using Amazon Kinesis Data Analytics. It also shows the design approach that the engineering team at NortonLifeLock took to build out a petabyte-scale operational analytics platform that processes usage data for their VPN services.
|
|
|
Did you enjoy this issue?
|
|
|
|
In order to unsubscribe, click here.
If you were forwarded this newsletter and you like it, you can subscribe here.
|
|
650 California St., San Francisco, CA 94108
|