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Why we need to Hash our IDs in Data Vault 2.0

Hashing is a technique used to transform a piece of data, such as an identifier, into a fixed-size string of characters, known as a hash. The resulting hash is unique to the input data and has a fixed length, making it useful for various purposes, including data storage and retrieval.

In the context of data vault 2.0, hashing is used to obscure the original values of identifiers in order to protect the privacy of individuals or organizations. By replacing sensitive identifiers with hashed values, it becomes more difficult for unauthorized parties to access or use the original data. This can be especially important when dealing with sensitive or personal information, such as social security numbers or financial data.

Hashing will make an ID totally impossible to get any meaning unless we have the connection between the original ID and the hashed ID

Hashing can also be useful for improving the performance of data management systems by allowing for faster searches and comparisons. When data is stored in a hashed format, it can be more efficiently indexed and retrieved, which can help to reduce the time and resources required to access and process the data.

Overall, hashing is an important technique in data management and can be used to protect the privacy and security of data, as well as improve the performance of data management systems.

Advantages of Hashing IDs in DV2.0

There are several advantages to hashing identifiers in data vault 2.0:

  1. Protects privacy: Hashing can help to protect the privacy of individuals or organisations by obscuring sensitive identifiers such as social security numbers or financial data. This can be especially important when dealing with sensitive or personal information.
  2. Improves security: By replacing sensitive identifiers with hashed values, it becomes more difficult for unauthorized parties to access or use the original data. This can help to improve the overall security of the data vault.
  3. Improves performance: Hashing can help to improve the performance of data management systems by allowing for faster searches and comparisons. When data is stored in a hashed format, it can be more efficiently indexed and retrieved, which can help to reduce the time and resources required to access and process the data.
  4. Reduces storage requirements: Hashing can help to reduce the amount of storage space required to store data, as the resulting hash is typically smaller than the original identifier. This can be especially useful when dealing with large datasets.

Overall, hashing is a useful technique in data management and can provide a number of benefits in the context of data vault 2.0.

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