FileStream and FileTable in SQL Server 2012

Data explosion brought a need to store both structured and un-structured data together in the database so that the benefits of the database system (like transactional support, backup and restore support, integrated security support, Full-Text Search support, etc.) can be leveraged. SQL Server 2008 introduced the FileStream data type to store unstructured data, such as documents, presentations, videos, audios and images, on the file system with a pointer to the data in the database.

Storage of un-structured data in FileStream improves the performance by leveraging the NTFS APIs streaming along with additional benefits of the database system.

SQL Server 2012 enhanced this capability even further by introducing FileTable, which lets an application integrate its storage and data management components to allow non-transactional access, and provide integrated SQL Server services - including full-text search and semantic search - over unstructured data and metadata. I am going to talk about these two new features in detail, for more information click here.

Getting Started with the New Column Store Index of SQL Server 2012

Column Store Index is a new feature in SQL Server 2012 that improves performance of data warehouse queries several folds. Unlike regular indexes or heaps, which store data in B-Tree structure (in row-wise fashion), the column store index stores data in columnar fashion and uses compression aggressively to reduce the disk I/O needed to serve the query request along with the newly introduced batch mode processing.

In my last article I talked in detail about the new Column Store Index, how it differs from regular indexes, and why and how it improves the performance of the same query by several folds if column store index is used, where it can be used and what its limitations are.

In this article I am going to take this discussion to another level and show how you can create column store index, how you can use index query hint to include or exclude a column store index, how the performance differs when using column store index vs. row store index and more, click here for more information.

Understanding new Column Store Index of SQL Server 2012

Column Store Index is a new feature in SQL Server 2012 that improves performance of data warehouse queries several folds. Unlike regular indexes or heaps, which store data in B-Tree structure (in row-wise fashion), the column store index stores data in columnar fashion and uses compression aggressively to reduce the disk I/O needed to serve the query request. This article discusses the new Column Store Index in detail, how it differs from regular indexes, why and how it improves the performance of the same query by several folds if column store index is used, where it can be used and what its limitations are, for more information click here.

Understanding BI Semantic Model (BISM) of SQL Server 2012

SQL Server 2012 introduced an unified BI Semantic Model (BISM) which is based on some of the existing as well as some new technologies. This model is intended to serve as one model for all end user experiences for reporting, analytics, scorecards, dashboards, etc. In this tip, I will talk in detail about the new BISM, how it differs from earlier the earlier Unified Dimensional Model (UDM) and how BISM lays down a foundation for future; for more information click here.

Importing Data From Excel Using SSIS - Part 2

In my last tip, "Importing Data From Excel Using SSIS - Part 1" we found out that the SSIS Excel Connection Manager determines the data type of each column of the worksheet on the basis of data for that particular column from the first 8 rows. This is the default behavior and connection manager uses a value in the registry key to consider the number of rows for data type determination. We also saw that the data import fails if the data in others rows (other than first 8 rows) is not compatible or has a longer length than the data in the first 8 rows. I talked about different ways to handle this to make the data import succeed. I also talked about the easiest solution which is to change the registry key value, but this has its own implications and hence the question; what other options are there without creating too much additional overhead? for information click here. 

Importing Data From Excel Using SSIS - Part 1

Recently while working on a project to import data from an Excel worksheet using SSIS, I realized that sometimes the SSIS Package failed even though when there were no changes in the structure/schema of the Excel worksheet. I investigated it and I noticed that the SSIS Package succeeded for some set of files, but for others it failed. I found that the structure/schema of the worksheet from both these sets of Excel files were the same, the data was the only difference. How come just changing the data can make an SSIS Package fail? What actually causes this failure? What can we do to fix it?  Check out this tip to learn more. 

Let's Explore Excel add-in of Master Data Services of SQL Server 2012 - Part 2

SQL Server 2008 R2 introduced Master Data Services (MDS) as Master Data Management (MDM) platform for managing enterprise master data centrally in a consistent, clean and up-to-date manner. There were three ways to manage master data in MDS.  First, was the Master Data Manager which is a web based User Interface. Second, was loading data through the staging process. Third, was to use the MDS WCF services to programmatically manage master data. These were fine options, but there were many users who wanted to manage data in Microsoft Excel in bulk than managing each single record/member at a time in Master Data Manager UI. The SQL Server team heard this requirement and introduced a brand new Excel add-in for SQL Server 2012 for MDS to manage master data. In my last tip I talked about how to get started with MS Excel MDS add-in to manage master data stored in Master Data Services database in Excel. In this tip, I am going to talk more about editing, creating, combining master data, creating entities and how it can be used in conjunction with Data Quality Services for data matching. For more information click here.