Hi all, some of the products on the market which p...
# gooddata-cloud
Hi all, some of the products on the market which provide 'semantic layer' functionality claim that they are very efficient in building star schema on their level and database model can remain 'normalized'. What are the best practices for GoodData LDM modelling - stick to the star schema ?
for example, screenshot from dbt metrics: is the same approach efficient for GoodData? Keep database normalized, define metrics, dimensions on gooddata side. • some databases support join elimination • databases and GoodData have cache and running the same query many times should be fast • GoodData has preaggregation feature (beta), probable idea is to keep results of complex calculations in separate tables with aggregated data and instead of running queries - get aggregated data having all these layers which help to optimize performance - do you prefer star schemas?
Hi Dmytro, We have a solid knowledge base documented when it comes to LDM - best practises, building, trouble-shooting etc. Please check the bellow documentation section: https://www.gooddata.com/developers/cloud-native/doc/cloud/model-data/ Also, I would strongly recommend you to go through our University courses which are covering the topic: Designing Data Models: https://university.gooddata.com/designing-data-models Understanding the Logical Data Model: https://university.gooddata.com/understanding-logical-data-model You should get all the information needed from above. In case of further questions, feel free to ask.