SF Data Weekly - Visualizing Mexican Food in NYC, Fanatics and Pandas for Big Data

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.