SF Data Weekly - Don't Hire Too Many Data Engineers Plus Metrics at Uber

Adding additional engineers to a messy data problem is usually counterproductive. This piece argues that data quality (cleansing and consolidation) is best handled by the knowledge worker.

An introduction to SAYN, an open source data processing framework, built to be simple yet flexible.

Using a set of AWS tools to create a complex workflow to load your dataset – including the relatively new AWS Step Functions.

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Hive is a data warehouse and query interface on top of Hadoop’s native Map-Reduce, which allows SQL-style queries in the Hive Query Language. This piece is a good overview plus the source of a couple Winnie-the-Pooh memes.

The City of Chicago is the first city in the country to publish anonymized rideshare data from companies including Uber, Lyft, and Via.

Exploring real-time analytics based on combining the popular CDC tool, Debezium, with the real-time OLAP datastore, Apache Pinot.

This complex, well-done visualization of COVID-19 vaccination data is a good one for the PowerBI aficionados.

A discussion with data visualization designer Maarten Lambrechts about his work on the 2020 Atlas of Sustainable Development Goals for the World Bank.

How Uber implemented uMetric, their unified metric platform, with the goal of making sure the entire organization is aware and agrees on the key metrics that drive making decisions and measuring progress.