Summary:

1. The talk is an instructional session on data modeling using Cloud Firestore, a real-time NoSQL database from Firebase and Google Cloud.

2. NoSQL databases like Cloud Firestore are schema-less and highly scalable, allowing flexibility in data models as opposed to strict SQL databases.

3. Cloud Firestore optimizes performance by allowing developers to denormalize data, which makes read operations more efficient at the cost of duplicating data.

4. Cloud Firestore auto-indexes every field in all documents and collections, which enables fast and scalable queries based on the size of the result set rather than the size of the data set.

5. There are limits to the size of documents and the frequency of updates, with recommendations to consider these when designing data models.

6. Subcollections and their shallow nature allow developers to efficiently structure data hierarchically and fetch it without incurring the cost of unnecessary data retrieval.

7. Billing is based mainly on the number of document interactions, so designing for efficient document structure is key to cost-effective data management.

8. Data should be modeled based on use cases, striking a balance between performance, cost, simplicity, and avoiding sending or storing unnecessary or sensitive data.

9. When deciding whether to use fields or subcollections, consider usage patterns, data size, and performance implications.

10. For data that updates, using Cloud Functions to manage changes in real-time can maintain consistency across denormalized data structures.

Key questions the transcript answers:
- How does Cloud Firestore handle data structuring for a NoSQL database?
- Why are denormalized data and subcollections recommended in Cloud Firestore?
- What should developers be aware of when working with the size of documents in Firestore?
- How does Firestore ensure query performance is fast?
- What are the billing implications of structuring data with Cloud Firestore?
- How can you navigate the trade-offs between larger documents and subcollections?
- What use cases exemplify correct data modeling techniques in Cloud Firestore?
- How can Cloud Functions be utilized to maintain data consistency?

Answers:
- Cloud Firestore is designed as a schema-less NoSQL database that auto-indexes every field for fast queries that are scalable based on the result set size, not the data set.
- Denormalized data is expected in NoSQL databases to optimize for frequent reads, while subcollections provide structure without excessive data retrieval, keeping reads efficient and minimizing costs.
- Documents in Firestore have size limits to prevent issues with large documents, including a total size limit of 1 MB, a maximum of 40,000 index fields, and a recommended 1 QPS (query per second) of sustained document writes.
- Firestore auto-indexes every field to ensure queries remain fast regardless of the underlying data set's size. This feature enables consistent query performance.
- Billing is primarily based on the number of document interactions. Therefore, designing data structures that minimize unnecessary reads and writes can help keep costs down.
- Developers should consider how the app will use the data. For less frequently accessed menu items, subcollections may be preferred, while for user-sensitive information, access levels can be finely controlled.
- Data modeling should reflect actual app behavior focusing on efficient data retrieval, maintaining user privacy, managing costs effectively, and coding defensively.
- Cloud Functions can automate data updates across denormalized structures, ensuring consistency without imposing significant load or security issues on client-side operations.

Here are a few memorable quotes:
- "The NoSQL philosophy is...

Cloud Firestore Data Modeling (Google I/O'19)

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