Our pick this week looks at replacements for the Lipstick Index, a once-famous indicator of spending
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October 9 · Issue #158 · View online |
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Our pick this week looks at replacements for the Lipstick Index, a once-famous indicator of spending during economic downturns, now that COVID has made lipstick obsolete. We also have a couple of different data-based takes on the first Presidential debate, and a slew of AWS data pipeline pieces. Stay healthy!
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The Skincare Index: A Lipstick Index for the Times of Covid | by Elena Marocco | The Startup | Oct, 2020 | Medium
The Lipstick Index was a term coined by the CEO of Estée Lauder, who noticed that lipstick sales spiked during times of economic downturn since it was an affordable luxury for women. This piece uses statistical analysis and data science to find a replacement for the Lipstick Index since you can’t see lipstick behind a mask.
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Normalize data with Amazon Elasticsearch Service ingest pipelines | Amazon Web Services
Amazon Elasticsearch Service is a managed cloud deployment of the Elasticsearch search engine. This piece describes how to use ingest pipelines to normalize incoming data and to create indexes in a predefined fromat.
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Unified serverless streaming ETL architecture with Amazon Kinesis Data Analytics | Amazon Web Services
This piece shows how to do event correlation and enrichment with Amazon Kinesis Data Analytics, and it includes a specific implementation of the generic serverless unified streaming architecture that can be deployed in your own AWS accouunt.
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How to Solve the “You’re Using THAT Table?!” Problem | by Barr Moses | Oct, 2020 | Towards Data Science
The issue of which of the tables in your data warehouse contain vetted, current data, and which have outdated or incorrect data, is as old as data warehouses themselves. This piece describes a Key Assets architecture as way to address this age-old problem.
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Become a BigQuery Power User. 5 tips to help you put this beast to… | by Dewald Abrie | The Startup | Sep, 2020 | Medium
Five solid tips to help you get started with Google’s BigQuery analytical database.
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1st Presidential Debate: By the Numbers | by Theo Goe | Oct, 2020 | Towards Data Science
This piece focuses on textual analysis of the first US Presidential Debate to try to tease some meaning from 90 minutes of fast-paced, chaotic argument.
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Visualization of networks related to the presidential debate. | by Soumyadeep Basu | Oct, 2020 | Medium
Another approach to analyzing the Presidential Debate uses analysis of Twitter data to visualize how different users of that social media platform clustered together in their responses to what the two candidates said during the debate.
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Beautiful correlation plots in R — a new approach | by Stefan Haring | Oct, 2020 | Towards Data Science
The author of the R package correally shows some examples of how the combination of his library, R and Plotly can produce beautiful correlation graphs.
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Keeping your data pipelines healthy with the Great Expectations GitHub Action - The GitHub Blog
Github engineer Hamel Husain explains how to use the open source Great Expectations project along with GitHub Actions to automatically test, document and profile your data pipelines as part of a traditional Continuous Integration workflow.
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Connect Salesforce and Redshift with Xplenty
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Xplenty Data Pipelines Overview | Salesforce Data to Amazon Redshift
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Xplenty is an ETL solution with a visual interface that has a bi-directional connection to Salesforce. This video is a short demonstration of moving data from Salesforce to Amazon Redshift, and it shows how Xplenty can quickly and easily transfer Salesforce data into your Redshift data warehouse.
Intermix - build better data products with analytics into your data warehouse. Complete visibility into your data platform, who is touching your data, and how it’s being used.
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