First in a series of tutorials for Hackolade Studio. A data model is an abstract representation of how elements of data are organized, how they relate to each other, and how how they relate to real-world concepts...

1. Hackolade Tutorial
Part 1 – What is a Data Model
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2. What is a data model?
• An abstract representation of how elements of data are
organized, how they relate to each other, and how how they
relate to real-world concepts.
• An explicit representation of the structure of data.
• The purpose of data modeling is to support the development
of IT systems and to ensure consistency, compatibility, and
quality of data.
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3. What is a data model?
• a data model describes a business.
• a blueprint for applications,
• a map helping evaluate design options
A data model helps to plan ahead, in order to minimize later rework.
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4. ER diagrams help
understand data
models
They are a
graphical representation
of entities,
their characteristics, and
their relationships
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5. Schemas are machine
readable contracts
between systems
• graphical representations
are for humans, not for
systems.
• models are useful after
the modeling
• models can generate
schema’s -- a contract
between 2 systems.
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6. How to start a data model from scratch?
• it is common to start with "functional requirements”:
description of the service that the software must provide.
• data modeling is an iterative process.
• Don't model everything at once.
• start with the concepts of the core purpose of the application,
• dive into the details afterwards
• validate your design with all application stakeholders
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7. Components of a data model
• a data modeler will try to identify: entities, their attributes, and the
relationships between the entities.
• Eg. associate nouns to entities, and verbs to indicate an attribute or a
relationship.
• For example: a customer, with first and last name, owns a vehicle. The vehicle
was registered on a given date and is manufactured by a make.
• Entities: customer, vehicle, and make
• Relationships: customer OWNS vehicle, and make MANUFACTURES vehicle
• Attributes: first name and last name are attributes of customer. Registration date is an
attribute of vehicle.
• In RDBMSs
• a physical table == an entity,
• columns == an attribute of that entity,
• rows == instances of that entity
• FK relationships link tables together.
• With NoSQL databases and denormalization, it's different
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8. Different types of data models
• Traditionally, data modelers
have identified 3 different
levels of data modeling
• A conceptual data model is a
high-level representation of
the vocabulary and the data
without any details about how
the data is stored or
implemented.
• A logical data model provides
more details about the
business rules.
• A physical data model
specifies how the data is
physically stored in a database
or other storage system.
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9. The role of Normalisation in Data Modeling
• Normalization is a technique
• to reduce data redundancy
• to ensure data consistency
• In a normalized database, data is stored in separate tables
• A table == a single entity or relationship,
• A column == a single attribute of the entity,
• A row == a unique instance of that entity.
• FK relationships link tables together.
• Logical Data Models were already normalised
• This complicates their use in NOSQL
• NOSQL leverages denormalization in function of the access pattern of the
application.
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10. The match with Polyglot Persistence
• Polyglot persistence is a data storage architecture
• different types of data are stored in different types of databases, based on
the specific requirements of each type of data.
• Eg. use a relational database for transactional data, a graph database for social network
data, a document database for unstructured data, and a columnar database for analytics
data.
• Advantage
• best database for each type of data
• better performance, scalability, and flexibility
• can simplify data modeling / duplication / redundancy
• Disadvantage
• complexity in the application's data storage layer
• requires careful planning and coordination
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11. The need for Polyglot Data Models
• Because
• NOSQL databases
• API based software development
need for heterogeneous data
models
• allow for denormalisation at the
conceptual/logical
• POLYGLOT DATA MODELS
• allow denormalization, if desired,
given access patterns;
• allow complex data types;
• generate schemas for a variety of
technologies, with automatic
mapping to the specific data types of
the respective target technologies.
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12. The need for Domain-Driven Data Modeling
• DDDM will allow you to
• focus on your core ("domain and sub-domains")
• break down complex problems into smaller ones ("bounded context")
• use data modeling as a communication tool ("ubiquitous language")
• keep together what belongs together ("aggregates")
• reach a shared understanding between business and tech ("collaboration of
domain experts and developers")
• iterate and evolve ("continuous refinement")
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13. Conceptual Logical Physical
Polyglot
Data Model
Target-specific
Data Model
Relational
data modeling
NoSQL/SQL/API/…
data modeling
Domain
Driven
Data
Modeling

14. Reading material
• Our articles on
• Polyglot data modeling
• Domain driven data modeling
• Metadata as code
• See Hackolade online documentation
• The Hackolade Blog
• Hackolade’s on social media: LinkedIn page, Twitter page
• Download Hackolade studio for free
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15. Questions?
Answers!
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