SF Data Weekly - Data Artists, Sharding and Levenshtein Distance

If you think you’re a data artist (and who doesn’t), this piece is for you.

Part 5 of this series delves into the details of replicating SET tables from Teradata and optimizing INSERT-SELECT statements. The first four parts of this series are similarly detailed and comprehensive.

We discuss two of the top e-commerce solutions, Magento 2 vs. Shopify Plus, so you can make the right decision for your business. [Sponsored]

Sharding was one of the first ways databases were distributed to improve performance. Why recent innovations have made it one of the best.

A data mesh architecture is a decentralized approach that enables domain teams to perform cross-domain data analysis on their own. This site is a deep dive into theory and implementation of data mesh.

A simple but important set of tips and tricks to optimize your SQL queries.

Data Sampling forms the essential part of the majority of research, scientific and data experiments. This piece shares sample code to create random and representative samples in Python.

How Levenshtein distance works, and how to use Levenshtein distance in building a plagiarism detection pipeline.

A step-by-step color palette tutorial for Seaborn, Altair and ggplot2.

The top ten visualizations and three finalists selected to compete in the 2022 Iron Viz Championship at Tableau Conference.

Beike is China’s combination of Zillow and MLS. This piece traces the history of their move from Hive + MySQL to Kylin, including details of their contribution to the Kylin project.

Two Twitter engineers explain how feature propagation can be an efficient and scalable approach for handling missing features in graph machine learning applications.