SF Data Weekly - Antifragile DWH Patterns, Lookout + Redshift and Kafka

How to get the best of two worlds — the structure and quality of a centralized data warehouse combined with the agility of antifragile practices

A short, simple explanation of Apache Kafka, an open-source platform for handling real-time data feeds.

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Amazon Lookout for Metrics detects outliers in time series data, determines their root causes, and enables quick action. This piece explains custom connectors for Redshift, which are used for multiple tables, complex transformations or for training on historical data.

SQL tips from the simple (DISTINCT counts) to the more complex (CTE and Window Functions).

A ten-step process used by the author for almost every data analysis question he answers.

Querying a Pandas data from using SQL for simpler, yet faster, data access.

A real-world example of using multiple Python packages to create a population count and density spatial map.

Nancy Organ, freelance data visualization designer and developer, answers three questions for Nightingale.

How Uber built and utilized Parquet’s finer-grained encryption feature to support data access restrictions, address retention, and enable encryption at rest.

How the Fleet division of Lyft uses their data science team.

Spotify runs 20,000 batch data pipelines on over 1,000 repositories. This piece explains how they moved to the open-source Flyte platform for all of their workflow orchestration needs.