Wednesday, December 21, 2011

Intelligent Reporting on fluid data sources using Analyzer

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Microsoft Excel is one of the most widely used tools for a variety of business purposes, due to its simplicity, calculation abilities and efficiency as a client tool for accounting purposes. Excel is the tool of choice for not only business users, but for a wide variety of users, right from field engineers collecting data from at the ground level to power-users validating and accounting for this data. Excel worksheets loaded with thousands of records are a huge source of data. But Excel is not meant to be a database management system or a reporting platform either.

Traditional data warehousing methodologies involve developing relational warehouses that are populated from sources using an ETL process – Extract, Transform, and Load. Data from these relational warehouses are processed and stored in analytical databases, which host cube and other data structures. SQL Server 2012 introduces tabular mode Analysis Services and the new Business Intelligence Semantic Model to deal with this scenario. This model requires usage of a new query language known as DAX (Data Analysis eXpressions). As it is a complex process to transform relational data into a dimensional model to facilitate robust analytical capabilities, business users often have to accept tradeoffs and constraints on their ability to perform analytical reporting. These tradeoffs are due to the limitations associated with provisioning data in analytical form. In this Analyzer recipe, we will take a look at what features Analyzer has to deal with this challenge.

The latest version of Analyzer introduces a new feature known as Data Packages to deal with the scenario discussed above. This feature provides the user with the ability to upload their data in Excel / Access file formats. After upload, options are provided to create a model for reporting, and the same model is created as an analytical model for intelligent reporting. Consider you have products-related data from Adventureworks in a flattened Excel file, having 60K records and a lot of fields. After the file is uploaded, all the attributes are listed as shown in the below screenshot. All the attributes are grouped in a single dimension by default. Dimensional modeling requires every unique entry to be associated with a key attribute. This is taken care of by an option to generate a unique key.

The Attributes tab facilitates selection of attributes for the dimension, and the Measures tab allows selection of measures. All numeric fields are identified by default as measures. Time is one of the most important dimensions in any dimensional model. Fields having date / time are identified to be configured as the key for a time dimension. Once the model is configured, it can be saved for later processing or can be processed in place. After processing, Analyzer creates a dimensional model and deploys the solution to the SSAS instance configured to be used with Analyzer. Apart from attribute selection, the entire process of deployment is entirely transparent to the end-users.

One important point that comes to any seasoned SSAS practitioner’s mind is how users would browse attributes. For example, Category -> SubCategory -> Products is a logical and natural hierarchy. Two questions that arise are 1) How would the users know whether a hierarchy exists? 2) How to build this hierarchy? When the model created from the uploaded files is deployed to SSAS, a dedicated database is created for it. If the database is opened using BIDS, it can be easily edited to enrich the model and build a hierarchy. The best part of the entire Data Packages solution is that the model is deployed as a normal SSAS solution and no proprietary encrypted code is injected, as seen in the below screenshot. This allows you to use BIDS with the Data Packages feature, should you wish to enhance an Analyzer-generated cube. Creating such a hierarchy would give a clear visual clue to the user of how to interpret and use the data, but creating the hierarchy would typically require intervention from the IT department as an end-user cannot be expected to accomplish this.

Even if the end-user does not have that support from their IT department handy, it is not a show stopper. Users might not get the visual clue of a hierarchy, but if users are well informed of the data they intend to analyze (which they should be) then Analyzer works on the intersection of fields and provides the same data browsing and reporting experience with/without a hierarchy in place.

The below screenshot displays the hierarchy of products on the rows axis, and it has Country->City on the columns axis. Without even creating a SSAS hierarchy, based on the association of data, Analyzer detects the relationship and displays data in the same way. From a fluid data source like Excel, which has fluid data types and virtually no metadata, Analyzer creates an analytical model transparently. Once the model is available, users can benefit from the intelligent reporting capabilities of Analyzer, without the time and resource-consuming hassle of the IT-supported provisioning process.

Without even a BISM, Analyzer has its own version of a BISM that works transparently for end users and equips them with analytical models on the fly for intelligent reporting capabilities. One of the main differences between the two is that the Analyzer “BISM” works with any version of Excel and does not require SharePoint or SQL Server 2008 R2 or SQL Server 2012. To find out more about this exciting and unique new feature, download an evaluation version of Analyzer.

Sunday, December 18, 2011

Difference between DirectQuery and In-memory mode in tabular mode analysis services

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Tabular models are one of the new enhancements coming in SQL Server 2012 especially with tabular mode analysis services. Querying data from these models is available in two modes: DirectQuery mode and In-memory mode. Those who are well versed with Multidimensional mode analysis services can grasp this very easily. DirectQuery mode is synonymous to ROLAP mode and In-memory mode is synonymous to MOLAP mode. DirectQuery reads data right from the relational data source while the in-memory mode queries data from the memory cache. Both has its own tradeoffs and benefits. More on the same can be read from here.

Interesting part about tabular models and these mode of queries is linked to a different fact. In SQL Server 2012 tabular mode analysis services, analysis services tabular projects can have just one model per solution. I find it a very serious limitation. But the data sources from where data can be sourced is immensely huge, and the list includes sources like Parallel Data Warehouse and ATOM feeds including SSRS reports too. This open up a huge range of interesting possibilities when these modes of querying data is linked with these data sources. For example, Parallel Data Warehouse can be linked to a tabular model, which in turn is used by an SSRS report as a source. Using DirectQuery mode, SSRS can fetch data from Parallel Data Warehouse in real time. This is one such example, but there are a huge range of interesting possibilities that these two modes can open up with interesting combinations of data sources. Check it out yourself.

Tuesday, December 06, 2011

How to install PowerView ? Why Data Alerts and Powerview are not available in native mode SSRS ?

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This blog has been silent for quite some time. I am recovering from an accident, a few turbulence in personal life including laptop crash. I am also busy these days in my new book authoring assignment. I would like to keep the details of the book under the covers, till the time gets mature to announce it. SQL Server MVP Deep Dives Volume 2 got released last month, and I have authored a chapter on this book titled "BI solution development - Design considerations". Coming back to business, this is my blog post after a long break from blogging.

SQL Server 2012 RC 0 got released by Microsoft 2 weeks back, with a deadly bug on Distributed Replay Controller in the setup. Due to this bug I had to skip installation of the component itself. SSRS enhancements is sharepoint integrated mode are shipping two new kids - Data Alerts and PowerView which was formerly known as Project Crescent.

1) I have been receiving queries from many developers on how to install PowerView and how to verify whether PowerView is installed. The answer to this question is, if you have installed SSRS Add-in for Sharepoint products on your Sharepoint farm, consider it granted that PowerView has been installed. This component is the one responsible for installing Data Alerts, PowerView as well as the entire user interface related to SSRS in sharepoint. Keep in view, PowerView and DataAlerts are silverlight based applications and can be operated only in a browser. Simplest way to verify whether powerview is installed, is by deploying a shared dataset on a sharepoint document / reports library. Select the drop-down options for this shared dataset, and you should find an option named "Create PowerView report".

2) Why PowerView and SSRS are not available in native mode SSRS ? Question is very valid, as Reports manager is also a web based application that queries SSRS web service. If these silverlight based enhancements are intended to be made available in native mode, I don't see a technical challenge to it. But these enhancements are packed only with the add-in, which means they are strategically intended to be made available only in sharepoint. So to me it seems more of a business decision than a technical decision. Affording Sharepoint just for a client tool is too heavy, is the reaction that I have been observing from the community. Let's see how community reacts to it in the long term.

I promise my blog readers that I would try to come up to speed on blogging as soon as possible. Keep writing to me.
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