Data modeling can be complex. Working with multiple data sources and making sense of all the data available today is not an easy task. That's why our Sisense BIC team decided to share with you their knowledge of data modeling best practices and methods here in the community, so that you can leverage your data in the best possible way, making you more effective and driving more successful project outcomes.
If you haven't had the chance yet, make sure to check out our new Data Modeling knowledge base segment. In this segment, Assaf Fraindlich and the team publish a collection of case studies, practical advice, and in-depth articles that can help those of you already working or getting ready to work with ElastiCube data schemes and models. Some of the many articles you can find there:
- Multi Fact Schema - for when you have multiple sources for your measures and multiple sources to slice & dice these measures.
- Derived Fact - to maximize performance by preparing the data in question on a cube level in advance.
- Star Schema - when you have one central source for your measures and multiple sources to slice & dice these measures.
Make sure to follow this segment to stay up to date on new articles that the team will be adding in the future.
Do you have any special requests or questions for our team? Feel free to comment below or create a new post in this forum.
Thanks and happy modelling!
Please sign in to leave a comment.