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August 28 · Issue #152 · View online |
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Our pick this month shows off the ArcGIS StoryMaps platform as well as delicious Mexican food, by tracing the growth of Mexican restaurants in New York City. Worth a click just to see the platform. A deep dive into Fanatics data visualization architecture, and the use of Pandas with big data are two other highlights. Stay healthy!
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The Mexican Restaurants of New York City
Three historians from SUNY Stony Brook take a look at the growth of Mexican restaurants in New York City, relating it to the growth of the Mexican population there. They also look at the growth of food trucks, with a series of historical maps.
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Debezium and KStreams to Handle Data Aggregation | by Adrian Eka Sanjaya | Aug, 2020 | Medium
A step-by-step example explaining how to use Debezium, a set of connectors for Kafka, to ingest data from a set of different databases using change data capture (CDC). This example uses ZooKeeper to maintain configuration information.
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Automate dataset monitoring in Amazon QuickSight | Amazon Web Services
Using QuickSight, one of Amazon’s data visualization tools, along with AWS Lambda and Athena, to visualize the status of the loads in your QuickSight data pipeline.
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How to do text similarity search and document clustering in BigQuery | by Lak Lakshmanan | Aug, 2020 | Towards Data Science
BigQuery offers the ability to load a TensorFlow SavedModel and carry out predictions. This capability is a great way to add text-based similarity and clustering on top of your data warehouse.
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Speed up data ingestion on Amazon Redshift with BryteFlow | Amazon Web Services
Using Origin Energy as a case study, this piece reviews best practices for data ingestion in Redshift, and shows how BryteFlow can be used to implement those techniques.
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A Sudden Drop in User Engagement. Using product analytics, data… | by Jodi Zhang | Aug, 2020 | Towards Data Science
Analyzing Yammer’s drop in user engagement using product analytics, data visualizations and data science methodologies. Written in SQLite and Pandas, graphs created with Plotly.
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17 Strategies for Dealing with Data, Big Data, and Even Bigger Data | by Jeff Hale | Aug, 2020 | Towards Data Science
While Python and Pandas can be convenient for analyzing smaller datasets, they’re often challenging to use with million, billion or trillion row datasets. This piece examines tools and techniques that allow you to keep using Pandas with big datasets.
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How We Visualized a Data Set That Contains Many Messages | by Felix Buchholz | Nightingale | Aug, 2020 | Medium
Sometimes a dataset can tell many stories. Trying to show them all in a single visualization is great, but can be too much of a good thing. This piece shows a visualization of shareholder votes and discusses the process that led to using the data
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Visualization Of COVID-19 New Cases Over Time In Python | by Jason Bowling | Aug, 2020 | Towards Data Science
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Adventures with metrics in The Times and The Sunday Times newsrooms — Part 1: Problems | by Dan Gilbert | News UK Technology | Aug, 2020 | Medium
The promising beginning of a series examining how a major newspaper grapples with the issues of presenting data visualizations and analysis to a general audience.
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FanViz: Data visualization @ Fanatics | by Fanatics Tech Team | Fanatics Tech Blog | Aug, 2020 | Medium
A deep dive into the homegrown data visualization platform of a site that sells fan merchandise. It explains why Fanatics decided to roll their own, and takes a deep dive into the IDE used to create their dashboards.
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Scale your cloud data warehouse and reduce costs with the new Amazon Redshift RA3 nodes with managed storage | Amazon Web Services
Amazon introduced the RA3 node type to address some of the difficulties in administering a Redshift cluster. This piece explains how customers like Nielsen, Yelp and Duolingo are using RA3 nodes to increase query performance and lower costs.
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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.
Xplenty. - the leading data integration platform to bring all your data sources together. Trusted by companies like Gap, Samsung, Ikea, and Masterclass for their data pipelines.
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