Slowly changing dimensions (SCD) is a common concept in data warehousing that refers to the way historical data is captured and stored in a database. In traditional relational databases, SCDs are commonly implemented using techniques like Type 1, Type 2, or Type 3 dimensions. However, when dealing with graph databases, we need to approach SCDs differently. In this blog post, we will explore how to implement SCDs in graph databases.

What are Slowly Changing Dimensions?

SCDs are used to track changes in data over time, especially in scenarios where historical data needs to be preserved. For example, let’s say we have a customer entity in our database, and we want to capture changes in their address over time. SCDs help ensure that we maintain a complete historical record of the customer’s address changes.

Graph Databases and SCDs

In a graph database, data is represented in the form of nodes and relationships. Each node in the graph represents an entity, and relationships between nodes represent the connections between entities. To implement SCDs in graph databases, we can leverage the inherent flexibility and querying power of graph databases.

Node Versioning

One approach to implementing SCDs in a graph database is to use node versioning. Instead of directly updating a node, we create a new version of the node and link it to the previous version. Each version of the node represents the state of the entity at a specific point in time.

For example, in our customer address scenario, when a customer’s address changes, we create a new version of the customer node with the updated address and establish a relationship between the new version and the previous version. This way, we can track the history of address changes for each customer.

Relationship Versioning

Another approach is to use relationship versioning. In this approach, we create a new relationship between the entities instead of creating a new node version. Each relationship represents a change in the relationship between the entities.

Following the customer address scenario, instead of creating a new version of the customer node, we can create a new relationship between the customer node and a new address node. The relationship can include attributes like a timestamp to indicate when the address change occurred.

Querying Historical Data

One of the key advantages of using a graph database for implementing SCDs is the ability to query historical data easily. With graph database query languages like Cypher (for Neo4j) or Gremlin (for Apache TinkerPop), we can traverse the graph and filter nodes or relationships based on specific criteria like timestamps.

For example, we can query the graph database to retrieve all the address versions for a particular customer or track the changes in the relationship between entities over time.

Conclusion

Implementing SCDs in graph databases requires a slightly different approach compared to traditional relational databases. By leveraging node versioning or relationship versioning techniques, we can effectively track and preserve historical data in a graph database. The flexibility and querying power of graph databases enable us to query and analyze historical data with ease.

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