A polyglot data model is a common physical data model that can generate schemas for different technologies while remaining technology agnostic. It allows for denormalization and complex data types. The polyglot model acts as a master model that physical target models can be derived from. Changes are made at the polyglot level and then target models are refreshed. The document discusses how to create, promote, and derive target models from a polyglot data model as well as handling changes between the models.

1. Hackolade Tutorial
Part 13 - Leverage a Polyglot data model
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2. What is a polyglot data model?
• A common physical model
• Similar but different from a logical model: see this article
• Truly technology agnostic:
• Physical schemas can be
generated for variety of
technologies
• Automatic mapping to
specific data types of
these technologies
• Allows denormalization, if desired, given access patterns
• Allows complex data types
• Also allows to do conceptual modeling with a graph view
• Reminder: break down complex models into smaller ones, applying
the principles of Domain-Driven Data Modeling
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3. What is a polyglot data model?
• Separate from target “physical” model – but RELATED
• Ensures consistency across physical implementations
• Similar to external definitions – but at MODEL level
• Polyglot data model is the MASTER – from which
target models are derived
• Changes are to be made at the MASTER level
• But “deviations” (add/change/delete) are possible in
the physical target models
• Graph Diagram = friendlier view of the
common business language with entities
and relationships
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4. Create a polyglot data model
• Just another Hackolade model
• Can be created from scratch
• or by converting a physical target model into a polyglot model
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5. Promote to a polyglot model
• Start from a physical target model, and make that the “master”
polyglot model:
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6. Derive a target data model
• You cannot forward engineer a polyglot model: first create a derived
target data model
• Operations take place from the target model
• You don’t “push” from a polyglot, you “pull” (derive) from a target model
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7. Select entities to include in derived model
• Not all or nothing!
• You can select what is derived/activated in the physical target model
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8. Polyglot to relational: Normalization
• When deriving into a relational target data model from a polyglot
data model, Hackolade will suggest normalization
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9. Combining multiple polyglot data models
• Target data models can
contain entities combined
from different polyglot
data models
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10. Adjusting the target model
• Minor deviations: changing properties of entities and attributes
• Adding new entities / views specific to the target model
• Removing entities from the target model
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11. Updating target data models
• Changes in polyglot data
models will be updated in
target data models by
• Refreshing the model link
• Closing and (re)opening the
target data model
• Hackolade will assess the
impact and let you decide
which objects to include in the
refresh
• You can always break the link –
but not reestablish it
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12. Reading material
• See Hackolade online documentation
• The Hackolade Blog
• This excellent new book:
MongoDB Data Modeling & Schema Design
• Many of the principles in the book are related to query
driven modeling based on access patterns!
• Hackolade’s on social media: LinkedIn page, Twitter page
• Download Hackolade studio for free
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13. Questions?
Answers!
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