Data Quality Meetup #3
Why implement regression testing for ETL code changes, how to align data producers and consumers, and what Data teams at Carta, Thumbtack, Shopify & Clari do to solve data quality.
Always keen on exploring the ever-changing world of data
Why implement regression testing for ETL code changes, how to align data producers and consumers, and what Data teams at Carta, Thumbtack, Shopify & Clari do to solve data quality.
Take your ETL workflow to the next level with Datafold and dbt integration that automates data testing and provides column-level data lineage
The more people that are looking at the data, and the more apps that are using the data, the faster data quality issues will be identified and resolved.
On the second Data Quality Meetup, we discussed three types of data testing and when to apply them, new-generation ETL frameworks and ROI of open-source data catalogs.
Over the past 10 years, we've seen a great advancement in technologies and tools for analytics and machine learning: with today’s modern analytics stack, we have fast and scalable data warehouses, dirt-cheap data storage, capable ETL orchestrators, and powerful BI tools.
Unlocking the next level with most popular ETL orchestrator
Put a comma in the right place
How not to frustrate yourself by going open-source for the wrong reasons
Objective criteria and subjective advice
Picking tools for every step in the data flow