The top SQL Server Analysis Services 2014-2016 Enhancements
If you are like most businesses, you have not yet upgraded to SQL 2014. We are just now moving to SQL 2014 as a standard. As a
An overview to the top Microsoft Business Intelligence Roadmap Enhancements announced thus far for Power BI Services and Mobile, Microsoft Excel 2016, SQL Server Reporting Services 2016, and SQL Server Analysis Services 2014-2016.
I will not be the first to say this (I will be added to a long list of
Power Pivot or SSAS Tabular data model optimization is important for Power View reporting usability and functionality.
In this posting, we will discuss the following topics:
X-Axis Guidelines
Advanced Properties
Image Handling
Data Categories
Table Behavior
Field Behavior
X-Axis Guidelines
Data type and distribution
Utilize a number or date that increases uniformly (primarily for Power BI forecasting but we have
Before a Power View report can be built, you have to have a data model built as a data source. There are several configuration options to help optimize your data model specifically for Power View. These configurations and/or optimizations are made within your Power Pivot model or in SQL Server
DAX Best Practices
are vital when utilizing large data models and looking to improve performance. You can get away with poor practices on small models but you will see performance issues as your data grows.
Data Analysis Expressions (DAX)
is the native query and formula language for Microsoft Power Pivot data models and SQL
Data Analysis Expressions (DAX)
is the native query and formula language for Microsoft Power Pivot data models and SQL Server Analysis Services Tabular models. DAX includes some of the familiar Excel functions that you have frequently utilized in Excel formulas, but also includes additional functions designed to manipulate relational data as needed. Much
Organization and user friendly data model development in Power Pivot & Tabular environments take precedence over almost all other concerns.
We will discuss best practices of creating a data dictionary, naming rules, etc. of Power Pivot & Tabular data models in other, related postings.
In this posting, we will discuss some options of organizing the
Normalization vs. Denormalization best practices for Power Pivot / Tabular data modeling is typically not disputed.
First, let's quickly define in human terms what we are referencing when we speak of normalization vs. denormalization. Normalization is reducing data duplication by splitting dimensional data and attributes into their dimension tables. Typically,
We will start off discussing the data model itself.
The data model that is created in Power Pivot or a Tabular solution is the foundation of any Microsoft business intelligence solution.
The data model consists of the data structure (including tables and their relationships, columns in the tables, and all measures
We have seen several dispersed articles on Power Pivot, SSAS Tabular, and Data Analysis Expressions (DAX) best practices.
Over many articles, we will attempt to consolidate many of these into a single, consolidated location as well as adding new material to the list based on our experiences as well as any
