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When to use or not use Data Vault

Data Vault is a data modeling technique that is often used when there is a need to store and manage large amounts of data in a data warehouse, and where the data is expected to change frequently or have complex relationships. It is well-suited for situations where the data needs to be accessed and processed in real-time or near real-time, and where the data needs to be maintained in a way that allows for easy tracking of changes over time.

When to use it

Data Vault is also well-suited for projects that require the ability to track the history and evolution of data over time. It provides a robust and flexible framework for storing and managing data, and can be easily adapted to changing business needs.

Scenarios to detect that you need it

  • When the data is expected to change frequently: Data Vault is designed to handle data that is expected to change frequently and can be used to track and store data changes over time.
  • When the data has complex relationships: Data Vault is well-suited for storing and managing data with complex relationships, as it uses a flexible, hub-and-link architecture to store data in a way that preserves these relationships.
  • When the data needs to be accessed and processed in real-time or near real-time: Data Vault is often used in combination with real-time or near real-time ETL processes, which can be useful for scenarios where the data needs to be accessed and processed as it is being generated or updated.
  • When the data needs to be maintained in a way that allows for easy tracking of changes over time: Data Vault is designed to store data in a way that allows for easy tracking of changes over time, which can be useful for scenarios where the data needs to be maintained in a way that allows for easy auditing or analysis.

When you do NOT need it

No no situation

On the other hand, Data Vault may not be the best choice for all projects. It can be more complex and resource-intensive to implement compared to some other data modeling techniques, and may not be the most suitable choice for projects with more limited resources or simpler data structures.

Data Vault may not be the best choice in situations where the data is not expected to change frequently, or where the data relationships are relatively simple. Additionally, Data Vault may not be the most appropriate choice for situations where the data does not need to be accessed and processed in real-time, or where the data volume is relatively small.

Scenarios where you’ll find that you DO NOT need it

  • When the data is not expected to change frequently: If the data is not expected to change frequently, it may be more efficient to use a different data modeling technique that is better suited for static data.
  • When the data has relatively simple relationships: If the data has relatively simple relationships, it may be more efficient to use a different data modeling technique that is better suited for storing and managing simpler data structures.
  • When the data does not need to be accessed and processed in real-time or near real-time: If the data does not need to be accessed and processed in real-time or near real-time, it may be more efficient to use a different data modeling technique that is better suited for batch processing.
  • When the data volume is relatively small: If the data volume is relatively small, it may be more efficient to use a different data modeling technique that is better suited for smaller data sets.

Key factors to identify when evaluating Data Vault as an option

There are a few key factors to consider when determining if Data Vault is the most appropriate data modeling technique for a particular project:

  1. Data volume: Data Vault is typically used to store and manage large amounts of data, and may not be the most efficient choice for smaller data sets.
  2. Data complexity: Data Vault is well-suited for storing and managing data with complex relationships, and may not be the most efficient choice for data with relatively simple relationships.
  3. Data change frequency: Data Vault is designed to handle data that is expected to change frequently, and may not be the most efficient choice for data that is not expected to change frequently.
  4. Real-time or near real-time processing requirements: Data Vault is often used in combination with real-time or near real-time ETL processes, and may not be the most appropriate choice for situations where the data does not need to be accessed and processed in real-time or near real-time.
  5. Data maintenance requirements: Data Vault is designed to store data in a way that allows for easy tracking of changes over time, which may be useful for scenarios where the data needs to be maintained in a way that allows for easy auditing or analysis. If these requirements are not present, a different data modeling technique may be more appropriate.

Conclusion

Overall, the specific choice of data modeling technique will depend on the needs and goals of the project, as well as the resources and constraints of the organization. Careful planning and evaluation of the trade-offs and benefits of different approaches may be necessary in order to determine the most appropriate solution.

Best practices when splitting your satellites in Data Vault modelling

In data vault modeling, satellites are used to store historical and contextual data about an entity in a data vault. When splitting satellites, it is important to consider the following best practices:

  1. Use a consistent and logical naming convention: Use a naming convention that is easy to understand and follow. This will help you easily identify and locate the satellites you need.
  2. Keep related data together: Group data that is related or belongs to the same entity in the same satellite. This will make it easier to understand and analyse the data.
  3. Avoid overloading satellites: Avoid adding too much data to a single satellite. If a satellite becomes too large, it can be difficult to manage and maintain.
  4. Use the correct data types: Make sure to use the correct data types for each attribute in the satellite. This will ensure that the data is stored and used efficiently.
  5. Consider data integrity: When splitting satellites, make sure to consider the impact on data integrity. You want to ensure that you do not lose any data or create inconsistencies when splitting the satellites.

By following these best practices, you can ensure that your satellites are organised and maintained in a way that makes it easy to understand and use the data in your data vault.

Criteria to follow when splitting your Satellites

There are a few steps you can follow to split your satellites in data vault modeling:

  1. Identify the reason for splitting: Determine the reason for splitting the satellites. This could be because the satellite has become too large, or because you want to group related data together in a more logical way.
  2. Determine the criteria for splitting: Decide on the criteria for splitting the satellite. This could be based on the type of data being stored, the time period it covers, or any other relevant factors.
  3. Create a new satellite: Create a new satellite for the data that meets the splitting criteria. Make sure to use a consistent and logical naming convention, and to include all relevant attributes in the new satellite.
  4. Migrate the data: Migrate the data from the old satellite to the new satellite. Make sure to carefully check the data to ensure that it has been migrated correctly and that there are no inconsistencies or data loss.
  5. Update any related links: If the satellite being split is linked to other objects in the data vault, make sure to update the links to point to the new satellite.

Avoiding when splitting your Satellites

When splitting satellites in data vault modeling, it is important to avoid the following:

  1. Losing data: Make sure to carefully migrate all data from the old satellite to the new satellite, to ensure that no data is lost during the split.
  2. Creating inconsistencies: Pay attention to the data being migrated, to ensure that it is consistent and that there are no inconsistencies introduced during the split.
  3. Overloading the new satellite: Avoid adding too much data to the new satellite. If a satellite becomes too large, it can be difficult to manage and maintain.
  4. Using an inconsistent naming convention: Make sure to use a consistent and logical naming convention when creating the new satellite. This will help you easily identify and locate the satellite in the future.

By avoiding these pitfalls, you can ensure that the process of splitting your satellites in the data vault is smooth and does not compromise the integrity of your data