Data Calculation Layer

This is the fifth blog in my series The Semantics of the Semantic Layer, where I discuss the seven core capabilities of a semantic layer.In this blog, I will dive deeper into the multidimensional calculation engine that serves as the query and calculation engine for a business-friendly view of data. It is the calculation engine that implements the semantic data model logic that we described in

While this means more work for Tableau, table calculations are generally done over a much smaller set of records than are in the original data source. If table calculation performance is a problem possibly because the result set returned to Tableau is very large consider pushing some aspects of the calculation back to the data source layer.

For data analysts, the semantic layer transforms the data access and analysis process. Rather than relying on IT intervention for every new analysis, analysts can directly access relevant data using familiar business terms. This self-service capability significantly reduces time to insight and enables more agile decision-making.

The new pulldataquotlayerquot function simplifies the syntax for querying ArcGIS layers, and it can be used in public surveys too! Point-in-polygon calculations using a map . The animation below shows a typical scenario for pulldataquotlayerquot. A calculation takes the location set in the map, and triggers a point-in-polygon query to retrieve a

The data type of a calculation result is dependent on the data type of each element of the calculation. If a calculation is performed on two integers, the calculation result is an integer. The pulldataquotlayerquot function only supports requests that return a feature object. To include these parameters in a query, append them to the URL after

Calculated expressions in ArcGIS Field Maps streamline all kinds of data collection workflows from storing location as an attribute, to pulling attributes from related records or other layers in the map. In this blog post, we'll walk through some of the most common use cases and provide sample code to get you up and running with calculated expressions in your own forms.

The data mart layer sits on top of the virtual analytical layer. In the data mart layer we create calculation views that consume the calculation views of the lower layer VAL to generate consumable shapes of data, ready for analytical applications. The data mart is the top layer of the modeling stack in our data warehouse.

You can calculate models for one or more points of view. For example, you could run a Forecasting model against 12 periods of data. When you run a model against a point of view, Enterprise Profitability and Cost Management layers your results on top of your source data without changing it. This makes is easy to undo or redo running a single

The staging layer in traditional data warehouses resided on RDBMS. It was a schema on write approach, which rejected any incompatible changes and needed tables to be dropped and re-created when incompatible schema changes occurred. However, this also meant that a well-defined interface contract was ensured for the data pipelines, which ensured minimal disruption by stopping incompatible

In this article, we'll explore the advantages and disadvantages of performing calculations in the database and application code. We'll consider a few factors that can influence this decision, and we'll discuss which layer database or application is better suited to handle them. 2. Calculation in the Database