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| March 4 · Issue #229 · View online |
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Our pick this week is a visualization of the social map of the BBC Series Peaky Blinders, with the aim of predicting whether a happy ending is possible. We also have an introduction to Google’s new Dataplex cloud mesh data lake manager, and a new Github-like service for data scientists and ML engineers. Stay healthy!
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Is There a Happy Ending for the Peaky Blinders? | Nightingale
Peaky Blinders is an award-winning historical drama series produced by BBC Studios. This piece creates a binary classification system of the show’s characters, and explains how a ML model was trained to predict whether a character will live or die.
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Migrate from SQL Server to Aurora PostgreSQL using SSIS and Babelfish | Amazon Web Services
Babelfish for Aurora PostgreSQL understands T-SQL, Microsoft SQL Server’s proprietary SQL dialect, and supports the same communications protocol. This piece explains how to use Babelfish and Microsoft’s SQL Server Integration Services (SSIS) to migrate data from SQL Server to Aurora.
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Salesforce CRM data to your data warehouse in less than 5 minutes
Integrate.io is the fastest Salesforce data replication tool on the market. Don’t go with a one-size-fits-all data replication tool. Click the image to learn more. [Sponsored]
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Build a data mesh on Google Cloud with Dataplex, now generally available
Dataplex is Google’s “intelligent data fabric,” which allows you to centrally manage, monitor, and govern data across distributed data, and make it securely accessible to a variety of analytics tools.
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DagsHub: a GitHub Supplement for Data Scientists and ML Engineers | by Khuyen Tran | Feb, 2022 | Towards Data Science
Github is great for version control of code, but their file size limits and lack of good change management tools for data makes it less than ideal for data scientists. This piece introduces DagsHub, an alternative that has better data versioning and the ability to connect to data stored on cloud storage systems like S3.
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Hierarchical Clustering: Explain It To Me Like I’m 10 | by Shreya Rao | Feb, 2022 | Towards Data Science
The third entry in a series explaining Machine Learning Algorithms to a 10 year-old covers Hierarchical Clustering. The first and second parts of the series are still available if you missed them. |
How to Perform Sentiment Analysis on Earnings Call of Companies | by Bharath K | Feb, 2022 | Towards Data Science
Using Python and AssemblyAI to analyze an earnings call streamed on YouTube.
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When to use 3D in data visualisation | by Josh Taylor | Towards Data Science
Best practice guidance for the use of three dimensions in data visualization using a number of examples.
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Data transfer in Manhattan using RocksDB
Twitter engineers describe a performance and stability problem they encountered while migrating the storage engine for Manhattan, their internally developed key-value store, to RocksDB, and how they solved it.
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How Panasonic Avionics used Amazon Aurora MySQL to modernize their environment | Amazon Web Services
Panasonic is the leading supplier of in-flight entertainment and communication systems. This piece describes how they moved their backend from an older version of MySQL to Amazon Aurora.
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Become A Better Data Engineer On A Shoestring (More Free Resources) — Pipeline Data Engineering Academy
An annotated list of free resources for those trying to learn data engineering to find a better job, or excel at their current job. Speaking of jobs, here are this week’s picks:
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