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Managing Data Vault Performance with Incremental Changes and Deletions

In the world of data warehousing, the Data Vault methodology has emerged as a robust and scalable solution for managing vast amounts of data. However, one common concern among practitioners is how to efficiently handle incremental changes and deletions, particularly when dealing with structures containing billions of rows. This article aims to elucidate the process, focusing on the questions around loading structures, performance considerations, and practical strategies for maintaining efficiency.

Understanding the Basics: Tracking Changes and Deletions

The core principle of Data Vault involves capturing all changes and deletions incrementally. This ensures that the data warehouse remains an accurate historical record of the enterprise’s data. Here’s a simplified illustration of how this can be achieved:

  1. Initial Load: When a new transaction is recorded, it is inserted into the Data Vault as a new record. For instance, if customer A purchases product C at store B on day one, this transaction is recorded with a value of €7.
  2. Handling Updates: If the value of the transaction changes from €7 to €5 on day two, instead of updating the existing record, two new records are created: one to nullify the original transaction (-€7) and another to represent the new transaction (€5).
  3. Dealing with Deletions: If a transaction is deleted, it is handled similarly by inserting a record that nullifies the original transaction.

This method ensures that the Data Vault remains immutable, as records are never directly altered once inserted. Instead, changes are tracked by adding new records, which simplifies loading processes and maintains data integrity.

Loading Structures: The Practical Approach

Loading structures in Data Vault can be challenging, especially when dealing with large datasets. Here are some practical strategies:

Using CDC (Change Data Capture)

If the source system supports CDC, this is the most straightforward method:

  • Insert New Records: Directly insert new records into the target system.
  • Handle Updates and Deletes: For updates and deletes, insert the corresponding counter transactions.

CDC provides a clear and efficient way to track changes and deletions, significantly simplifying the loading process.

Full Load vs. Incremental Load

In scenarios where full loads are used (though rare for very large datasets), the process involves:

  • Identifying New Records: Select records from the staging area that do not exist in the target and insert them with a counter of one.
  • Identifying Deletions: Select records from the target that do not exist in the staging area and insert counter transactions to nullify them.

While full loads can be intensive, they can be managed effectively by optimizing the identification of new and deleted records.

Performance Considerations

Handling billions of rows requires careful planning to avoid performance bottlenecks. Here are some strategies to mitigate performance issues:

Parallel Processing

By running multiple processes in parallel, you can significantly speed up the loading process. For example, separate processes can handle inserts and counter transactions concurrently.

Hash Keys and Indexes

Using hash keys and indexes efficiently can reduce the time needed to check for existing records. Ensure that your hash keys include all relevant business keys and transaction IDs to maintain uniqueness.

High-Water Marks and System Indicators

Some systems, like Oracle, offer features like SCN (System Change Number) or row versions that can help identify modified records. Using these indicators can reduce the amount of data processed by focusing only on recently changed records.

Practical Example: Incremental Loading without CDC

In cases where CDC is not available, you can still achieve efficient incremental loading:

  1. Incremental Updates from Source: If the source system provides daily increments (inserted and updated records), use this data to update the target.
  2. Handling Deletions: For deleted records, you might need an additional table or mechanism to track deletions. If such a table is available, use it to insert counter transactions.
  3. Full Load Approach: If only full loads are available, implement a two-step process to identify and handle new, updated, and deleted records.


Managing incremental changes and deletions in Data Vault structures, especially for large datasets, requires a combination of strategies tailored to the specific capabilities of your source systems. Whether using CDC, full loads, or incremental updates, the goal remains the same: to maintain an accurate and efficient data warehouse. By understanding the principles and applying practical solutions, you can handle the complexities of Data Vault performance effectively.

Remember, the key to success lies in thorough planning, efficient use of system capabilities, and continuous optimization of your data loading processes. By following these guidelines, you can ensure that your Data Vault implementation scales efficiently, even as your data volumes grow.

Meet the Speaker

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Michael Olschimke

Michael has more than 15 years of experience in Information Technology. During the last eight years he has specialized in Business Intelligence topics such as OLAP, Dimensional Modelling, and Data Mining. Challenge him with your questions!

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