This week's pick is a Tableau visualization and analysis of stolen bikes in the UK -- if you're livin
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| October 30 · Issue #161 · View online |
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This week’s pick is a Tableau visualization and analysis of stolen bikes in the UK – if you’re living there and want to understand where you might get your bike nicked, or if you’re interested in what you can do with Tableau, this article is for you. We also have a lengthy and detailed description of churn analysis in Python, one of the most in-depth articles that we’ve seen on that important topic, as well as a piece on Uber’s use of Pinot for OLAP on a massive scale. Stay healthy!
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Stolen Bike Visual Analysis using Tableau | by Suhas V S | Towards AI | Oct, 2020 | Medium
The UK Stolen Bike dataset covers 3 years of bike theft. This piece uses Tableau to do visualizations and analysis of the dataset, and the Tableau Public link lets you explore the dataset interactively. |
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Accessing external components using Amazon Redshift Lambda UDFs | Amazon Web Services
This piece describe how to User Defined Functions (UDFs) written in AWS Lambda, which recently went GA, from Redshift. It’s another step in using Redshift SQL statements for pipeline automation.
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Data Pipelines Done Right | Xplenty
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Fixing performance and latency issues on the database | Towards Data Science
A good basic overview of the best strategies and patterns for improving database performance when query performance starts to lag.
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Automating DBA tasks on Amazon Redshift securely using AWS IAM, AWS Lambda, Amazon EventBridge, and stored procedures | Amazon Web Services
As a data warehouse administrator or data engineer, you may need to perform maintenance tasks and activities or perform some level of custom monitoring on a regular basis. You can combine these activities inside a stored procedure or invoke views to get details. This article shows how to do that in Redshift, using a variety of AWS tools.
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Predict Customer Churn in Python
An complete and highly detailed discussion of a seven-stage model of predicting churn, with examples of each stage using sample data and Python code.
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COVID-19 Hospitalizations Are on the Rise — Here’s What You Should Know (Part 1) | by Jorge A. Caballero, MD | Oct, 2020 | Medium Coronavirus Blog | Medium Coronavirus Blog
This is the first article in a series by a co-founder of codersagainstcovid.org. The goal of the series is to add context to widely distributed and quoted hospitalization numbers. This piece covers what number of hospitalized patients really means, Part 2 explains why hospital bed distribution is important, and Part 3 covers the nuances of hospital and ICU occupancy. |
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Create and Share an Interactive Map in 1 Minute | by Joe T. Santhanavanich | Oct, 2020 | Towards Data Science
A simple and easy introduction to creating interactive maps with Google Sheets – great for simple projects.
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Matplotlib Styles for Scientific Plotting | by Rizky Maulana N | Towards Data Science
This is a guide for you to create scientific plotting styles with Matplotlib by customizing rcParams and creating colorblind-friendly palettes.
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Operating Apache Pinot @ Uber Scale | Uber Engineering Blog
Apache Pinot is a distributed Online Analytical Processing (OLAP) system for performing low-latency analytical queries on terabytes-scale data. This piece is a complete overview of how Uber uses Pinot to power near real-time exploration of data related to Uber rides and Uber Eats.
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How should our company structure our data team? | by David Murray | Snaptravel | Oct, 2020 | Medium
Real-life lessons from five data team iterations: centralized, embedded, full-stack, pods and business domains. Here are our job picks for the week:
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