View profile

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

Revue
 
 
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 | Integrate.io
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
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.
Powered by Revue
650 California St., San Francisco, CA 94108