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July 2 · Issue #196 · View online |
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Our pick this week is an excellent analysis of Titanic survivor data. We also have a piece on AWS Lake Formation row- and column-level security, as well as the Redshift SUPER data type, which allows ingestion of unstructured data. Stay healthy!
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Titanic — Predicting Survival rates using Machine Learning | by Punith | CodeX | Jun, 2021 | Medium
A detailed, step-by-step analysis of survival data from the most famous shipwreck in history, the 1912 sinking of the HMS Titanic. Despite the rather grim subject matter, the piece is a great template for similar projects, because it includes exploratory data analysis and data cleanup in addition to the ML component. If you like this piece, the same author’s look at insurance fraud detection using ML is also worth a look.
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Work with semistructured data using Amazon Redshift SUPER | Amazon Web Services
With the new SUPER data type and the PartiQL language, Amazon Redshift expands data warehouse capabilities to natively ingest, store, transform, and analyze semi-structured data, such as weblogs and sensor data.
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Pushing Data from a Data Warehouse to Salesforce | Xplenty
Need to get data out of your data warehouse and into Salesforce? Follow this comprehensive guide. [Sponsored]
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Effective data lakes using AWS Lake Formation, Part 4: Implementing cell-level and row-level security | Amazon Web Services
This is the fourth part of a series that explained how to set up governed tables, add streaming and batch data to them, and use ACID transactions in AWS Lake Formation. This post explains how to set up filter expressions for row-level security, and how to hide or show specific columns for some users.
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GitHub - GoogleCloudPlatform/elcarro-oracle-operator
El Carro is a new project that offers a way to run Oracle databases in Kubernetes as a portable, open source, community driven, no vendor lock-in container orchestration system. El Carro provides a powerful declarative API for comprehensive and consistent configuration and deployment as well as for real-time operations and monitoring.
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An Extensive Cricket’s “Fab Four” Statistical comparison. | by Nishant Singh | Jun, 2021 | Medium
A deep dive into the stats of four top cricket players who emerged in the early 2010s. All the data and a Jupyter notebook can be found on the author’s github.
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How to style your Dataframe with Python | by June Tao Ching | Jun, 2021 | Towards Data Science
Using Python to format a data table similar to conditional data formatting in Excel.
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Grafana 8.0 Rethinks Alerts and Visualizations – The New Stack
Grafana Labs’ Grafana 8.0 release features a number of new features, including the combination of different types of alerts in a single interface and a wider range of visualization options. A good example to draw from for your data visualization projects.
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Awesome data visualization tools for software developers
An interactive tool to find the best data visualization tool for your software development project, based on the framework and licensing.
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Exploring Data @ Netflix. By Gim Mahasintunan on behalf of Data… | by Netflix Technology Blog | Jun, 2021 | Netflix TechBlog
Netflix is open-sourcing Netflix Data Explorer, which allows fast, safe access to data stored in Cassandra and Dynomite/Redis data stores. This piece shares some examples to show how the tool is used at Netflix.
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ArcGIS StoryMaps presents the timeline block
Learn about ArcGIS StoryMaps’ timeline block and some creative ways to use it.
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