Creating Data Products in a Data Mesh, Data Lake or Lakehouse for use in Analytics (26-27 September 2022, Live Streaming Event)
Creating Data Products in a Data Mesh, Data Lake or Lakehouse for use in Analytics (17-18 October 2022 – Live Streaming Event)
Centralised Data Governance of a Distributed Data Landscape (24-25 October 2022 – Live Streaming Event)
Creating Data Products in a Data Mesh, Data Lake or Lakehouse for use in Analytics (24-25 November 2022 – Amsterdam)
Centralised Data Governance of a Distributed Data Landscape (19-20 October 2022 – Live Streaming Event)
Smart Infrastructure & Smart Applications for the Smart Business – Infrastructure & Application Performance Monitoring
Data Warehouse Automation & Real-time Data – Reducing Time to Value in a Distributed Analytical Environment
Centralised Data Governance of a Distributed Data Landscape (28-29 November 2022 – Live Streaming Event)
Following on from my last blog on data federation, the next data federation pattern I would like to discuss is a Data Warehouse Virtual Data Mart pattern. This is as follows:
This pattern uses data virtualisation to create one or more virtual data marts on top of a BI system thereby providing multiple summarised views of detailed historical data in a data warehouse. Different groups of users can then run ad hoc reports and analyses on these virtual data marts without interfering with each other’s analytical activity.
Pattern Example Use Case
Multiple ‘power user’ business analysts in the risk management department of a bank often need their own analytical environment to conduct specific in-depth analyses in order to create the best scoring and predictive models. This pattern facilitates the creation of multiple virtual data marts without the need to hold data in many different data stores
Reasons For Using It
Reduces the proliferation of data marts and also prevents inadvertent ‘personal’ ETL development by power users who have a tendency to want to extract their own data to create their own data marts. It is often the case that each power user wants a detailed subset of data from a data warehouse that overlaps with the data subsets required by other power users. This pattern avoids inadvertent inconsistent ETL processing on extracts of the same data by each and every power user. It also avoids the duplication of the same data in every data mart, improved power user business analyst productivity, reduces the time to create data marts and reduces the total cost of ownership.