
DAD008-Unlocking the Power of Data Layer Schema
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このコンテンツについて
Unfortunately - Andrew's track got corrupted at the end. We did the best we could with the editting, but there was not much we could do. Instead of ending the podcast suddenly, I cut Andrew's track all together. Thankfully, the new Podcasting solution we were using for this episode has been cancelled and we are moving to a another new one to test it out. Thankfully - it was only the last 6 minutes. It was funny seeing that near the end, even Andrew's camera got lost! During the recording, there was no indication that anything was wrong.
Key Takeaways
- Data Layer Schema is essential for accurate data collection.
- A well-defined schema helps developers understand data expectations.
- JSON Schema provides a code-based approach to data validation.
- Schema validation can prevent errors before they reach production.
- Integrating schema validation with tools like ObservePoint enhances data quality.
- Data quality issues can lead to significant financial losses.
- A collaborative approach to defining schemas improves outcomes.
- Using a centralized data layer reduces technical debt.
- Automation in data validation saves time and resources.
- Clear delineation of responsibilities improves team efficiency.
Chapters
- 00:00 Introduction to Day-Layer Schema
- 05:01 Defining Day-Layer Schema
- 09:13 Exploring JSON Schema
- 13:44 Benefits of Schema Validation
- 18:08 Types of JSON Schema Validation
- 25:46 Advanced Schema Features
- 30:15 Collaborative Development of Data Layers
- 34:23 Empowering Developers with Data Quality
- 38:57 Division of Labor in Data Management
- 44:27 Data Quality and Development Team Dynamics
- 46:59 Benefits of Implementing a Daily Schema
- 50:57 Cost of Bad Data and Its Implications
- 57:35 Integrating Tools for Data Validation
- 01:07:45 Outro