Showing results for 
Show  only  | Search instead for 
Did you mean: 

🎥 Anomaly Detection on 5 Pillars of Data Observability with Dynatrace Davis AI

Community Team
Community Team

Data Observability, borrowing ideas from Software Observability, pertains to understanding an organization's full data lifecycle. It involves monitoring and managing the internal state of data systems from ingestion to storage and usage. It is about gaining insight into the data pipeline, understanding how data evolves, and identifying any issues that could compromise its integrity or reliability.

In this Observability Lab hosted by Andi Grabner @andreas_grabner , David Bruendl @DavidBruendl and Wolfgang Beer @wolfgang_beer explain how Dynatrace Davis Anomaly Detection can be applied to the 5 Pillars of Data Observability: Freshness, Distribution, Volume, Schema, and Lineage.

David and Wolfgang show us how Davis Anomaly Detection based on any data in Grail can be applied in Notebooks, Dashboards, Workflows, and in the recently introduced Davis Anomaly Detection App to automate the alerting on Data Observability issues!

Links discussed:
Introduction to Anomaly Detection based on DQL
Data Observability with Dynatrace: 
Davis Anomaly Detection: 
Blog on Data Observability: 
Blog on the Importance of Data Quality: 

Chapter List:
00:00 - Introduction
00:50 - What you will be learning today
01:22 - Recap of Anomaly Detection with DQL session
04:00 - What is Data Observability?
05:03 - Data-driven Enterprises
06:06 - Example of Data Pipelines such as ETL
08:25 - 5 Pillars of Data Observability
12:02 - Live Demo of Data Observability with Dynatrace

- - - 
Subscribe to our YT channel 
Stay up-to-date with Dynatrace! Follow us on FacebookInstagramLinkedIn, TwitterTwitch  

When passion meets people magic and innovation happen.

Featured Posts