View source for Embedding (Denormalization)

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'''Category:''' Data Modeling & Partitioning

'''Summary:''' Nesting related data within a single document for fewer round trips.

== Overview ==
Embedding is a data modeling technique where related data is denormalized and nested directly within a single document, rather than split across multiple documents or containers. It is preferred when related entities are frequently read together, have a 1:1 or 1:few relationship, and don't change independently at very different rates, since it reduces the number of round trips (and RU cost) needed to retrieve related data.

== More Info & Links ==
* [https://learn.microsoft.com/en-us/azure/cosmos-db/nosql/modeling-data Microsoft Learn: Data modeling in Azure Cosmos DB]

== DP-420 Style Practice Questions ==
'''Q1.''' When is embedding (denormalizing) related data within a single document generally the best modeling choice?
* A. When related entities are frequently read together and don't change independently at very different rates
* B. When related entities are updated by many different services at very different frequencies
* C. When the embedded data would grow unbounded in size
* D. When strong referential integrity across services is required

'''Q2.''' What is a potential downside of embedding too much related or frequently-changing data into one document?
* A. Increased document size and RU cost for updates to any embedded part
* B. Reduced query flexibility for filtering
* C. Inability to use a partition key
* D. Loss of schema flexibility

== Answer Key ==
''Answers are listed at the very end so you can attempt the questions first.''

* '''Q1 (Embedding (Denormalization)):''' A. When related entities are frequently read together and don't change independently at very different rates — Embedding works well when related data is read together often and doesn't have wildly different update patterns.
* '''Q2 (Embedding (Denormalization)):''' A. Increased document size and RU cost for updates to any embedded part — Embedding large or frequently changing related data can bloat documents and increase the RU cost of updates.

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''DP-420: Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB — term detail page''

[[Category:DP-420 Terminology]]
[[Category:Data Modeling & Partitioning]]