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SF Data Weekly - Antifragile DWH Patterns, Lookout + Redshift and Kafka

March 18 · Issue #231 · View online
SF Data Weekly
This week’s pick is a piece on using antifragile principles when designing a data warehouse.
We also have an introduction to using Amazon Lookout to detect outliers in Redshift data, as well as a basic introduction to Kafka. Stay Healthy!

Our Pick
7 Antifragile Principles for a Successful Data Warehouse | by Iliana Iankoulova | Mar, 2022 | Picnic Engineering
Data Pipelines
Kafka | All you need to know. | by Himanshu Tripathi | Mar, 2022 | DataDrivenInvestor
Microsoft SQL Server Data Integration in Less Than 5 Minutes |
Data Storage
Build and deploy custom connectors for Amazon Redshift with Amazon Lookout for Metrics | Amazon Web Services
10 Quick SQL Tips After Writing Daily in SQL for 3 Years | by Andreas Martinson | Mar, 2022 | Towards Data Science
Data Analysis
Every Data Analysis in 10 steps!. Adding stucture to your data analysis ! | by Anmol Tomar | CodeX | Mar, 2022 | Medium
Fugue and DuckDB: Fast SQL Code in Python | by Khuyen Tran | Mar, 2022 | Towards Data Science
Data Visualization
Network and Interconnection in Python Maps | by Himalaya Bir Shrestha | Mar, 2022 | Towards Data Science
Three Questions with… Nancy Organ | Nightingale
Data-driven Products
One Stone, Three Birds: Finer-Grained Encryption @ Apache Parquet™
Data Science on Lyft’s Fleet Team | by Kelly Haberl | Mar, 2022 | Lyft Engineering
Why We Switched Our Data Orchestration Service: Spotify Engineering
Data Engineering Jobs
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