How do you select the best data warehouse architecture?

2 Data Vault

Another popular data warehouse architecture is the Data Vault, which combines some elements of both Kimball and Inmon. The Data Vault is a hybrid modeling technique that uses three types of tables: hubs, links, and satellites. Hubs store the unique identifiers of business entities, links store the relationships between entities, and satellites store the attributes and history of entities. The Data Vault enables fast loading, high scalability, and easy maintenance of the data warehouse, while preserving the auditability and traceability of the data sources.

4 Cloud vs On-Premise

Another important decision in data warehouse architecture is whether to host your data warehouse in the cloud or on-premise. The cloud offers many advantages, such as lower upfront costs, higher scalability, faster deployment, and easier integration with other cloud services. However, the cloud also poses some challenges, such as security, privacy, compliance, and vendor lock-in. On-premise data warehouses give you more control, customization, and ownership of your data, but require more investment, maintenance, and expertise.

5 Lambda vs Kappa

The Lambda and Kappa architectures are two ways of handling streaming and batch data in your data warehouse. The Lambda architecture uses two layers: a batch layer that processes historical data and a speed layer that processes real-time data. The results from both layers are merged in a serving layer that provides a unified view of the data. The Kappa architecture simplifies the Lambda architecture by using only one layer: a streaming layer that processes both historical and real-time data. The Kappa architecture reduces the complexity and latency of the data pipeline, but requires more robust and reliable streaming systems.

6 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?