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- Data
Data Quality and Observability
This learning path is actively in production. More content will be added to this page as it gets published and becomes available in the library. Planned content includes:
1. Introduction to Data Quality and Observability (video course) 2. Assess Data Quality and Risk (video course) 3. Define Data Quality Requirements (video course) 4. Design and Implement Data Quality Controls (video course) 5. Monitor Data Health with Data Observability (video course) 6. Respond to Data Quality Incidents (video course)
Data quality is how well your organization's data meets the needs of the teams and systems that rely on it, while data observability is the ability to monitor your data's reliability across systems. This learning path covers the full data quality lifecycle, including assessing data quality and risk, defining requirements, building controls, monitoring data health, and responding to incidents. After completing this path, you'll be equipped to assess your organization's data quality and design ways to improve and monitor it.
Content in this path
Data Quality and Observability
Watch the following courses to get started on learning the foundational concepts of data quality and observability.
Try this learning path for free
What You'll Learn
- 1. Core data quality and observability concepts
- 2. How to assess data quality and risk
- 3. How to define data quality requirements
- 4. How to design and implement data quality controls
- 5. How to monitor data health with data observability
- 6. How to respond to data quality incidents
- Learners interested in this path should have basic SQL and/or Python experience and a working familiarity with how data moves through pipelines and systems. Some basic knowledge of metadata and data lineage concepts is helpful, but not required.
- Data quality
- Data observability
- Metadata
- Data lineage
- Data management
- Data governance
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