Do you need to model hierarchical data such as JSON, MongoDB, and Google BigQuery?

We have been designing and documenting traditional relational databases using logical and physical data models as entity relation diagrams for a long time and the approach is accepted. Hierarchical structures such as JSON, MongoDB, and Google BigQuery can now be managed using the same approach. Now your developer projects can enjoy the same benefits as database design and fit into a broader governance program

Watch this recording to discover how you can use logical data models to form canonical models for hierarchical data assets:

Standardize information definitions across relational and hierarchical structures
Document and design hierarchical structures using the same language used for other data assets.
Build a common data architecture for data at rest, data in transit, and data in use.
Connect your hierarchical structures into your data governance program
Reduce the time taken to design hierarchical structures by reusing a library of standard definitions.
Generate and reverse engineer code for hierarchical structures in products like JSON, MongoDB, and Google BigQuery.

Chapters:
0:00 Intro

0:07 Slide presentation

6:15 Demo environment

7:15 Demo: Hierarchical structures
– MongoDB, JSON, Google Big Query
– Containment Relationships

10:57 Reverse engineer, Import a JSON file
– Reverse engineer a MongoDB database
– Import a JSON file

22:33 Generate Data Vault objects
– Hubs
– Satellites
– Links

30:25 Q&A
– Enumerated lists created by using Reference Values in JSON schemas

37:06 Outro