What are the most effective data vault design practices?

1 Use a business key

A business key is a unique identifier for each entity or concept in your data vault. It allows you to link different data sources and avoid duplication and inconsistency. A business key should be stable, meaningful, and granular. For example, a customer ID, a product code, or a transaction number can be good business keys. You should avoid using surrogate keys, such as sequential numbers or GUIDs, as they do not reflect the business context and may change over time.

2 Separate hubs, links, and satellites

A data vault consists of three types of tables: hubs, links, and satellites. Hubs store the business keys and metadata of the entities or concepts. Links store the associations or relationships between the entities or concepts. Satellites store the descriptive attributes or details of the entities or concepts. By separating these tables, you can achieve a modular and flexible data vault that can handle changes in the source systems, data quality, and business rules without affecting the existing data or structure.

3 Apply hashing and compression

Hashing and compression are techniques that can improve the performance and security of your data vault. Hashing is a process of transforming a value into a fixed-length code that can be used as a key or a checksum. Compression is a process of reducing the size of a value by removing or replacing redundant or unnecessary information. By applying hashing and compression, you can speed up the data loading and querying processes, reduce the storage space and network bandwidth, and protect the sensitive data from unauthorized access.

4 Use load dates and record sources

Load dates and record sources are metadata that can help you track the history and provenance of your data vault. Load dates are timestamps that indicate when the data was loaded into the data vault. Record sources are identifiers that indicate where the data came from. By using load dates and record sources, you can enable auditability, traceability, and accountability of your data vault. You can also use them to perform incremental loading, delta detection, and change data capture.

5 Implement data quality rules

Data quality rules are criteria that define the validity, accuracy, completeness, consistency, and timeliness of your data vault. They can help you identify and resolve data issues, such as missing values, outliers, duplicates, conflicts, and errors. You can implement data quality rules at different stages of your data vault design, such as during the data profiling, mapping, loading, and validation processes. You can also use tools and frameworks, such as SQL, Python, or Data Vault 2.0, to automate and monitor the data quality rules.

6 Optimize for performance

Performance is a key factor that affects the usability and value of your data vault, depending on various aspects such as data volume, complexity, frequency, latency, and concurrency. To optimize for performance, you should use parallel processing and partitioning to distribute the data loading and querying workloads across multiple servers or nodes. Indexes and views can also be used to speed up data retrieval and filtering operations. Additionally, caching and materialization can store frequently accessed or computed data in memory or disk. Finally, data marts and star schemas can provide simplified and aggregated data for specific business purposes or users.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?