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SF Data Weekly - 3 Design Principles, MongoDB -> Redshift, Facebook's Systems@Scale, Kafka for Graph Processing

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September 3 · Issue #129 · View online
SF Data Weekly
Our Pick
3 Design Principles for Engineering Data - Button Blog 3 Design Principles for Engineering Data - Button Blog
Data Pipelines
Lessons Learnt While Building an ETL Pipeline for MongoDB & Amazon Redshift Using Apache Airflow Lessons Learnt While Building an ETL Pipeline for MongoDB & Amazon Redshift Using Apache Airflow
How we built a tool for validating big data workflows How we built a tool for validating big data workflows
A simple event-sourcing example with snapshots using Lambda and DynamoDB
Data Storage
Amazon Redshift — Concurrency Scaling Feature - Innovid Amazon Redshift — Concurrency Scaling Feature - Innovid
10 Ways to Tweak Slow SQL Queries
Data Analysis
Kafka Graph Processing: Visual Stream Analytics with Neo4j Kafka Graph Processing: Visual Stream Analytics with Neo4j
Data Visualization
Data Visualization with Plotly Data Visualization with Plotly
Data-driven Products
Systems @Scale 2019 - Facebook Engineering
Data Engineering Jobs
Data Engineer - Reverb (now part of Etsy)
Data Engineer - Segment
Data Engineer - Clearbit

Data Podcasts to Listen to
The Data Engineering Podcast
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