SF Data Weekly - Data Governance, Catalogs and Great Expectations

Why data governance is having an identity crisis, what it was envisioned as decades ago, and how we can save the reputations of data stewards everywhere.

Data reliability or data integrity is always a difficult challenge. This solution shows how to use the Great Expectations data validation library in a data pipeline with a Redshift endpoint.

We’ll show you how easy it is to create a Salesforce ETL pipeline so you can migrate your Salesforce data to a data warehouse or data lake for analytics and reporting. [Sponsored]

SQLAlchemy is a Object Relational Mapper (ORM) for Python. This piece shows how to use it with Redshift.

The data catalog, along with the warehouse and BI tool, is one of the three pillars of a data ecosystem. This piece explains how to choose one.

Five different probabilistic methods for choosing a sample for a statistical study.

This blog post introduces Google Cloud Storage FUSE to Vertex AI and AI Platform users. This feature enables the training jobs on these platforms to read and write the data on Cloud Storage via a mounted file system.

Using publicly-available data to explore administrative boundaries and population, and evaluation which data sources and techniques work best for this application.

A good step-by-step for those new to data science visualizations.

Part 1 of this series explained the models used at Spotify to recommend content. This piece explains how Spotify evaluates those models.

How the Lyft experimentation team uses A/B testing, time-split and region-split experiments to evaluate product changes.