SF Data Weekly - Empirical Recipes and Some Github Finds





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July 15 · Issue #248 · View online
SF Data Weekly
This week’s pick is a piece that has a recipe for answering any empirical question.
We also have a couple of great Github finds – an open-source MySQL compatible hybrid transaction/analytical processing database, and a tool for easily seeding test databases from production. Stay Healthy!

Our Pick
A recipe to empirically answer any question quickly | by Quentin Gallea, PhD | Jul, 2022 | Towards Data Science
Data Pipelines
Data Engineering Made Simpler: How to Get Your ETLs to Production Faster | by Nick Kobishev | Similarweb Engineering | Jul, 2022 | Medium
How Ecommerce Companies Benefit From a Textual Warehouse | Bill Inmon "Father of the Data Warehouse"
Data Storage
GitHub - stoneatom/stonedb: StoneDB is an open-source, MySQL HTAP and MySQL-native database for oltp, real-time analytics
GitHub - Qovery/Replibyte: Seed your development database with real data ⚡️
Data Analysis
Accelerate machine learning with AWS Data Exchange and Amazon Redshift ML | Amazon Web Services
Data Visualization
A Matter of Time: Linking Job Titles to Data Visualization Tasks | Nightingale
Visualizing Cannabis Sales and Cost | by Brian Perron, PhD | Jul, 2022 | Towards Data Science
Essential Techniques to Style Pandas DataFrames | by Leonie Monigatti | Jun, 2022 | Towards Data Science
Data-driven Products
Resharding Petabytes of data to improve performance for our largest customers | by Shubhit Singh | Jul, 2022 | Mixpanel Engineering
How William Hill migrated NoSQL workloads at scale to Amazon Keyspaces | Amazon Web Services
Data Engineering Jobs
Data Scientist - Atlassian
Data Architect, Remote - Caption Health
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