Models

 In Oracle Data Integrator (ODI), a Model serves as a description of a set of datastores. It represents a logical structure of the data stored in a data server. The concept of models is key in understanding how data is represented and interacted with in ODI. Models enable users to define, organize, and manage the metadata for various types of data stores, from relational tables to files and XML elements.

Key Concepts of Models:

1. Definition of a Model:
• A Model is a metadata container for a group of datastores.
• A datastore represents a data structure, such as a table, file, or message, that exists in the database or other data stores.
• The model itself does not contain actual data; it only describes the structure and attributes of the data.
• A model is logical in nature and maps to a physical schema that holds the actual data.
• Models in ODI follow a relational paradigm, which means they organize the data into tables, define relationships between data (e.g., primary and foreign keys), and manage the associated metadata.
2. Mapping to Physical Schema:
• The model is based on a Logical Schema defined in the topology of ODI. This logical schema is the framework within which the model exists and describes the data structures.
• In a given context, the logical schema is mapped to a physical schema.
• A physical schema represents the actual data storage structure.
• The Data Schema of this physical schema contains actual data structures such as:
• Tables (e.g., relational database tables)
• Files (e.g., flat files or XML files)
• JMS messages (Java Message Service messages)
• XML elements (data elements from XML files)
• These are all represented as datastores in the model.
3. Role of Metadata:
• A model in ODI is primarily concerned with metadata management. Metadata refers to the descriptions of data structures rather than the data itself.
• This metadata includes definitions of:
• Tables (columns, data types, keys, etc.)
• Files (file format, structure, etc.)
• XML Elements (elements, attributes, etc.)
• Models provide a way to describe and organize data without holding the data itself.
4. Model Folders and Sub-models:
• Models can be organized into model folders for better management and logical grouping.
• Within a model, you can also create sub-models to further organize the datastores into more granular sections. Sub-models are typically used to represent related datasets or data structures within the same logical model.
• For example, a model could represent all customer data, and sub-models could separate customer demographic data from transaction data.
5. Relational Paradigm:
• Models in ODI follow the relational paradigm, meaning they represent the data structures in terms of tables, attributes, and keys.
• Tables represent the core data structures in a database.
• Attributes represent the columns within those tables.
• Keys define relationships between tables, such as primary keys and foreign keys.

Working with Models in ODI:

Models serve as the foundation for creating ETL mappings in ODI. Here's how models are typically used in the ODI workflow:

1. Creating a Model:
• When creating a model in ODI, you define the logical schema that it will map to and identify the physical schema containing the actual data.
• You also create the datastores within the model to define the structure of the data tables or files being used.
2. Reverse Engineering and Model Creation:
• ODI provides tools for reverse engineering existing databases and systems to automatically generate models based on the data structures found.
• This is detailed in the Connectivity and Knowledge Modules Guide for Oracle Data Integrator. It helps you reverse engineer common technologies like relational databases, flat files, and other sources to generate the models and datastores in ODI.
3. Using Models in Mappings:
• After creating models, you can use them as source or target data stores in your ETL mappings.
• A model defines the structure of data that you can extract (for source data) or load (for target data) during the transformation and loading processes in ODI.
4. Models and Datastores:
• A datastore is a representation of the actual data structure (such as a table, file, or JMS message) within a model. Each datastore is defined with the necessary metadata (column names, data types, keys, etc.).
• The datastore is the actual data source or destination used in ODI mappings. Models only contain the metadata definition for datastores, not the actual data.

Benefits of Using Models in ODI:

1. Organization of Metadata:
• Models provide a structured way to organize and manage metadata for data sources and targets.
• They help users keep track of data structures, which can be critical for managing complex data integration processes.
2. Ease of Reverse Engineering:
• ODI allows for the reverse engineering of existing data structures, which means that users do not need to manually define each table or file. This reduces the setup time and effort required to integrate data from different sources.
3. Consistent Data Representation:
• Since models are based on a relational paradigm, they allow for consistent representation and use of relational concepts like tables, columns, keys, and constraints, which makes it easier to work with the data in mappings.
4. Simplifies Data Management:
• Models allow for easy organization of different datasets and their attributes into logical groupings (models and sub-models), improving data management and workflow organization.
• They support grouping of related data, which is useful when dealing with large, complex data integration tasks.
5. Contextual Data Representation:
• The mapping from a logical schema to a physical schema ensures that data can be represented based on the specific requirements of the context in which it is being used, such as different environments (development, testing, production).

Conclusion:

In Oracle Data Integrator, a Model serves as the blueprint for organizing and managing the metadata of data structures across various sources. It defines datastores and helps map logical schemas to physical schemas, allowing users to manage and integrate data efficiently. Models are essential for data integration workflows, providing a structured way to organize metadata, facilitate reverse engineering, and ensure consistency in data processing.