Business Intelligence

Sifiso W. Ndlovu

How to replace hardcoded lookups using SQL Server Master Data Services

September 14, 2017 by

Introduction

A big part of the technical debt in my organization’s data warehouse (DW) and business intelligence (BI) environments relates to hardcoded lookup data. This is data required by the business to make sense of transactional data but was never planned for in the underlying source system and consequently get injected into DW and BI solutions. Inevitably, it is only a matter of time before DW and BI team lose track of the places wherein the hardcoded data reside thus making it difficult to maintain. Furthermore, due to lack of documentation or staff retention, anyone who subsequently takes over these DW/BI solutions can unknowingly create duplicate lookup data. In this article, I explain how we reduced such technical debt in my organization by moving most of the hardcoded lookups into SQL Server Master Data Services (MDS).

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Craig Porteous

How to connect to (and query) Power BI and Azure using PowerShell

September 13, 2017 by

Power BI “as a whole” is a bit of a black box. If you’re like me and used to using SQL Server & its components; SSRS, SSAS etc. you have access to installation directories, Event logs, trace logs, error logs, chocolate logs? You can see full instances & their contents in one go, whether that be databases, reports or cubes. It gives you the control over & responsibility for performance & maintenance.

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Thomas LeBlanc

Using Many-to-Many Relationships in SQL Server Analysis Services (SSAS) 2016

September 7, 2017 by

The Multidimensional Cube option of Analysis Services has handled many-to-many relationships with ease for many versions before 2016. The Tabular had a work around using DAX formulas until the release of SQL Server 2016. There are still some limitations to many-to many in Tabular but of course, there are some “tricks” to overcome the limitations. But, the many-to-many relationship will be in businesses data for many years to come. A solution has to be provided when it comes to Analysis Service databases.

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Thomas LeBlanc

Analysis Services (SSAS) Tabular Models – Attributes and Measures

August 14, 2017 by

In this article, a demonstration of the tabular model will try to related multidimensional cube design to the path forward Microsoft is giving used s with new versions of Analysis Services. Even though the descriptive names are changing, the tabular model still accomplishes the centralized business rules multidimensional cubes gave the business intelligence world. This is enforced by speed obtained from in-memory technology and simplification of the interface for the masses.

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Craig Porteous
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What’s New in Reporting Services (SSRS) 2017

July 26, 2017 by

To further the discussion related to Mohamed’s post on Reporting Services 2016 What’s new in SQL Server 2016 Reporting Services (SSRS), I wanted to cover the new features and functionality we see in Reporting Services 2017 as RC1 was released last week SQL Server 2017 Reporting Services Release Candidate now available, separately from SQL Server 2017 which reached RC1 a few days prior.

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Thomas LeBlanc

Analysis Services (SSAS) Cubes – Dimension Attributes and Hierarchies

July 5, 2017 by

In Data Warehouse language, slicing and dicing is done with Dimension Attributes. Sometime a developer feels the need to provide everything to end users, whereas seasoned Business Intelligence Architects understand to provide only the attributes from the requirements. It is a hard path to follow and the seasoned architect’s experience might be the better option.

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Prashanth Jayaram

How to use Python in SQL Server 2017 to obtain advanced data analytics

June 20, 2017 by

On the 19th of April 2017, Microsoft held an online conference called Microsoft Data Amp to showcase how Microsoft’s latest innovations put data, analytics and artificial intelligence at the heart of business transformation. Microsoft has, over the last few years, made great strides in accelerating the pace of innovation to enable businesses to meet the demands of a dynamic marketplace and harness the incredible power of data—more securely and faster than ever before.

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Minette Steynberg

Reporting in SQL Server – Power BI Report Server

June 13, 2017 by

Power BI is a self-service business intelligence tool from Microsoft which has been steadily gaining momentum in the last couple of months. One of the well-known disadvantages of Power BI is that it is basically cloud only. A lot of companies are not yet at the point where they feel comfortable having their data in the cloud or are premise bound for some other reasons such as data-sensitivity, data-sovereignty or compliance.

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Craig Porteous

Considerations for On-premises data in PowerBI

June 7, 2017 by

Whether you’ve been using PowerBI for a while or you’re just getting started with it, chances are you’ll want to source at least some of your data from on-premises systems. Be that SQL server databases, Oracle, SSAS etc. The same process even applies if you’re sourcing from AWS which I’ll go into in more detail below. All of these data sources will require Microsoft’s On-Premises Data Gateway.

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Thomas LeBlanc

Initial Attributes and Measures in SSAS Multidimensional Cubes

June 2, 2017 by

Connecting to tables in SQL Server Analysis Service (SSAS) databases is easy. Maybe too easy. The learning curve for developing in a Multidimensional Cube is steep, although a Tabular Model has lessened this curve. The power of encapsulating the business logic of aggregations and analytics into an Analysis Service database is too great not to embrace Analysis Services in an enterprise environment.

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Koen Verbeeck

Analysis Services (SSAS) Multidimensional Design Tips – Creating Dimensions

June 1, 2017 by

In the previous article, Analysis Services (SSAS) Multidimensional Design Tips – Data Source View and Cubes, we discussed best practices for SSAS Multidimensional cubes and data source views. In this article, we continue the series with design tips for creating dimensions as the subject. As with the previous article, most tips are suited for SSAS 2008 (most likely 2005 as well) to 2016 and later versions. Analysis Services Tabular is not covered in this article.

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Koen Verbeeck

Analysis Services (SSAS) Multidimensional Design Tips – Data Source View and Cubes

April 28, 2017 by

In this article, we’ll discuss some tips and best practices regarding the design of OLAP cubes in Analysis Services Multidimensional (SSAS). Most tips – if not all – are applicable for SSAS 2008 to 2016 (and later most likely). Since Analysis Services Tabular – the in-memory columnstore OLAP database from Microsoft – is a completely different design experience, it will not be included in this article.

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Thomas LeBlanc

Connecting to Data Source(s) with SQL Server Analysis Services

April 18, 2017 by

For someone new to SQL Server Analysis Services, a decision must be made on whether to use a Multidimensional Cube or Tabular Model. Both types can be installed on the same server, but they must be different instances. That could mean additional license costs. But, with the developer’s edition, both can be installed and reviewed before a production decision is made. Data Source connections are the first step to review with both versions.

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Andrea Martorana Tusa

How to create geographic maps using Power BI – Filled and bubble maps

April 5, 2017 by

The project

This is the first article of a series dedicated to discovering geographic maps in Power BI.

My goal is to illustrate to you the different visuals available in Power BI for making maps. Remember that Power BI is a product undergoing constant evolution; new features are added every month at a fast pace. And over the time Power BI has gained more mapping capabilities compared to the starting point.

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Thomas LeBlanc

SQL Server Data Warehouse design best practice for Analysis Services (SSAS)

April 4, 2017 by

Before jumping into creating a cube or tabular model in Analysis Service, the database used as source data should be well structured using best practices for data modeling. Some might say use Dimensional Modeling or Inmon’s data warehouse concepts while others say go with the future, Data Vault. No matter what conceptual path is taken, the tables can be well structured with the proper data types, sizes and constraints.

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