Telmai Academy
  • Data Quality and Observability Academy
  • Basics of Data Observability
  • Data Quality Indicators
    • Introduction - Indicators of Data Quality
    • Selecting Data Quality Indicators
    • Completeness
    • Uniqueness
    • Freshness
    • Validity
    • Accuracy
    • Consistency
    • Data Lineage
  • Advanced Topic: Implementing DQ indicators
    • Completeness
      • Built-in
      • User-Defined
  • Correctness
    • Categorical (Nominal or Ordinal)
    • Numerical (Discrete or Continuous)
    • Structured
    • Semi-Structured
    • Unstructured
    • Uncommon Types
    • Designated Values
  • Profiling data
    • Basics of profiling
    • Interactive Profiling
  • Monitoring data quality
  • Monitoring definitions
    • SLO
    • SLI
    • Policies
    • Setting up policies and alerting
  • Monitoring Sources
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Data Quality and Observability Academy

Learn from top industry experts and level-up your data quality analysis and monitoring knowledge - for free.

NextBasics of Data Observability

Last updated 2 years ago

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This course is designed for data practitioners like data engineers, product owners, data analysts, data scientists, or even data stewards responsible for data and its reliability.

We noticed that there needs to be structured and easy learning courses designed for today's landscape, including profiling, monitoring, and data observability.

The goal is to give you a guided framework on data quality indicators, metrics, and a process for identifying which metrics your team should use.

By the end of this, you should have a good sense of which metrics to track to improve the quality of your data, how to measure and improve it.

3 Step Approach to Data Quality