How to
📄️ Add a Model Server
The installation (On-premise) or set-up (Data Hub Cloud) would have created a Model Server for you and you should not need to create any more. However, in some scenarios such as the use of a sand-box environment, you may require another Model Server to be added.
📄️ Add data to your model
Add a data source
📄️ Bring data into your Model with Pipelines
A Pipeline is a configurable resource that defines a source, steps that transform or augment the data, and a destination. The destination can be a warehouse table or a SQL statement that combines multiple tables
📄️ Configure automatic source loading
Automatic source loading simplifies pipeline configuration by automatically determining the best source loading strategy. Describe the characteristics of your source data, and Data Hub will select the optimal strategy at processing time.
📄️ Connect to a Data Hub warehouse
Connecting directly to a Data Hub model warehouse is possible through SQL Server Management Studio (SSMS) or other third party tools e.g. Power BI.
📄️ Connect to on-premise data
To connect to data sources hosted on your local network or private cloud, you may need to install and configure a Data Gateway. The Data Gateway acts as a bridge between your on-premise data sources and Data Hub, enabling secure data transfer.
📄️ Create a model from a data source
Excel will be used as a data source in the displayed example.
📄️ Modularize your model with module tags
Add a new tag to a resource
📄️ Move your model between environments
Data Hub provides users with the ability to move a model between environments. Any model with its accompanying resources can be migrated as a .zap file for easy import and export.
📄️ Optimize model processing time
Models that process quickly deliver fresh data to your reports sooner, so your team spends less time waiting and more time making decisions. Efficient processing also reduces the chance of scheduled processes overlapping or queuing, and frees up resources so that other models and users aren't slowed down.
📄️ Optimize your Model with Keys and Indexes
This article describes how to optimize a model using keys and indexes. Adding keys and is an advanced optimization technique. Please see the performance guide for when you should add keys and indexes.
📄️ Parameterize your Model to avoid constants
Parameters can be added to two different locations within a Model, depending on the desired scope of the parameter:
📄️ Preview data
Overview
📄️ Refresh Warehouse data or structure
Once a model has been configured, it needs to be processed. This creates the corresponding data warehouse and optionally the semantic layer. You can process the entire Model or specific Model Pipelines.
📄️ Relate your pipelines
Relationships define how the tables within the database relate to each other. Specifically, a relationship joins a primary key column in one table with a foreign key column in another.
📄️ Secure your model with a model role
Model roles provide defined levels of access to the cube for specified category of users. Model roles are managed using the roles list on the model's tab.
📄️ Transform your Data with Steps
A Step is a pipeline setting that configures the transformations to be applied to column(s) in the pipeline. Each step can have a different purpose and correlates to a set of nest views within the warehouse.
📄️ Use a database script
Database scripts allow you to execute commands (via T-SQL) at specific times during warehouse processing. The scripts are specified using the Database Scripts section on the model tab.
📄️ Work with columns
Change source-column selections