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

Revue
 
This week's pick is an interesting counterpart to the notion that adding more engineers to a problem
 
January 15 · Issue #172 · View online
SF Data Weekly
This week’s pick is an interesting counterpart to the notion that adding more engineers to a problem will make it better – worth a read if only for a different perspective.
We also have pieces on Hive, metric standardization at Uber, and a complex visualization in PowerBI. Stay healthy!

Our Pick
Hiring additional data engineers is a problem | Mammoth Analytics
Data Pipelines
Introducing SAYN: A Simple Yet Powerful Data Processing Framework | by Robin Watteaux | Jan, 2021 | Towards Data Science
Building complex workflows with Amazon MWAA, AWS Step Functions, AWS Glue, and Amazon EMR | Amazon Web Services
Xplenty | Simplified ETL & ELT to BigQuery, Snowflake, Redshift & Azure
Data Storage
Apache Hive Class: Tasting The Honey | by Or Bar Ilan | Jan, 2021 | Medium
Data Analysis
Assessing batch-processed analytics of anonymized rideshare data | by Farhan Juneja | Jan, 2021 | Medium
Change Data Analysis with Debezium and Apache Pinot | by Kenny Bastani | Apache Pinot Developer Blog | Jan, 2021 | Medium
Data Visualization
COVID-19 Global Vaccination - DataChant
Visualizing the Sustainable Development Goals | by Claire Santoro | Nightingale | Jan, 2021 | Medium
Data-driven Products
The Journey Towards Metric Standardization | Uber Engineering Blog
Data Engineering Jobs
User Researcher - Vouch Insurance
GTM Data Scientist - Intercom


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