How can you use a hub-and-spoke data warehouse architecture pattern to improve your data management?

In my opinion, it can expresses like an arithmetic operation like data warehouse + hub and spoke pattern! The data warehouse is data architecture to store historical unified data storage for analytics cases; while data hub-and-spoke pattern is a way to manage the interaction between consumer and producer components. I understand with the phrase "hub-and-spoke data warehouse"; i connect the data warehouses, archiving data storage, operational data stores, data marts, operational marts or analytical purpose applications by hub-and-spoke platform to support the only analytical cases. My objection about the above concept on the section is more about the standard data warehouse architecture, when the hub-and-spoke approach wouldn't require.

Data Stores like Snowflake offer everything needed to follow a centralized, mesh or hub and spoke approach. Besides separating compute and storage, Snowflake is a federated database - actually a set of databases - and with their extensive Role based security, you easily can support any of the approaches. What ties this together and prevents a mesh or hub from becoming siloed free-for-alls is a Data Catalog and Semantic View Layer. Create a data steering committee and tell all players that they have to be represented and have to participate. Please see my LinkedIn profile posts on Data Democratization and how to build a shared Data Catalog and Semantic View Layer. If done right, you can have your cake and eat it too!

Key roles in the hub & and spoke model are data engineers, analytical engineers, and analysts from the functional team. Data engineers (hub) set up the pipeline to pull the data from sources to the landing area. Then identify the key attributes required to create a single source of truth from various sources, and apply the cleansing & and business rules. Analytical engineers (spoke) work closely with the analysts' team to build the data marts for the functional team (spoke). Data governance should be part of a hub and spoke pattern to ensure the right level policies, and controls are applied and privileges are defined.

The hub-and-spoke data architecture pattern can offer these advantages to improve data management: 1. Data Access Security - Centralized security measures can be implemented more easily in a hub-and-spoke architecture. Access controls, encryption, and other security measures can be managed centrally. 2. Change Management - Implementing changes to the data model or schema is typically more straightforward in a centralized hub. Changes can be applied uniformly, reducing the risk of inconsistencies. 3. Data Analytics - It is easier to perform cross-functional analytics and gain insights from a standardized view of data in the hub, yet data can still be further democratized at the spoke for tailored analyses to meet specific needs.

When you have multiple spokes teams and they reach out hub team for changes, the hub team will become the bottleneck. Hub team is a lean team and prioritization for them will be difficult. The hub team is responsible for managing the platform and providing the data sets. Change management to shared datasets will become a challenge. The Hub team needs to follow the metadata-driven principles to support future change requests with minimal effort. Less collaboration between the hub and spoke shall lead to inconsistent data models and standards.

in my knowledge, focal framework is a candidate to implementing this type of architecture pattern in this situation! Focal framework uses metadata to integration source data to businnes needs by based business requirements and reusable table generator patterns; then you can create a new consumer requirements based table from set up metadata like dimensional or OBT model from metadata parameters! This framework promote: - Integration business area with IT area to get and update metadata. - Speed and minimize maintainment and development work of data pipeline by reusing table generator pattern to get new tables. - Speed of change in data models to improve table generator pattern capacity.