I have received lots of interesting comments on the blog about what is in it. Many of you got it absolutely incorrect and many got it close to the right answer but no one got it 100% correct. Well, no issue at all, I am going to give away the price to whoever has the closest answer first in personal email.

Here is the answer to the question about what is in the box? Here it is – the box has my new book. In fact, I should say our new book as I co-authored this book with my very good friend Vinod Kumar. We had real blast writing this book together and had lots of interesting conversation when we were writing this book. This book has one simple goal – “master the basics.”

This book is not only for people who are preparing for interview. This book is for every one who wants to revisit the basics and wants to prepare themselves to the technology. One always needs to have practical knowledge to do their duty efficiently. This book talks about more than basics. There are multiple ways to present learning – either we can create simple book or make it interesting. We have decided the learning should be interactive and have opted for Interview Questions and Answer format.

Here is quick interview which we have done together.

Details of the books are here

The core concept of this book will continue to evolve over time. I am sure many of you will come along with us on this journey and submit your suggestions to us to make this book a key reference for anybody who wants to start with SQL server. Today we want to acknowledge the fact that you will help us keep this book alive forever with the latest updates. We want to thank everyone who participates in this journey with us.

You can get the books from [Amazon] | [Flipkart].

Read Vinod‘s blog post. Do not forget to wish him happy birthday as today is his birthday and also book release day – two reason to wish him congratulations.

Reference: Pinal Dave (http://blog.SQLAuthority.com)

Suppose you want to execute a one-to-many application in which each incoming record needs to be parsed into multiple output records.  For example, a record containing monthly data over a year period needs to be reworked so that each emitted record contains data from a single month.  The following fragment shows two representative data records, where the first field is the year and the following fields are the monthly data, January through December.

Year,Jan,Feb,Mar,Apr,May,Jun,Jul,Aug,Sep,Oct,Nov,Dec
2010,1001,1002,1003,1004,1005,1006,1007,1008,1009,1010,1011,1012
2009,901,902,903,904,905,906,907,908,909,910,911,912

You want each output record to contain only three fields: Year, Month, and Value.

Year,Month,Value
2010,Jan,1001
2010,Feb,1002
2010,Mar,1003
2010,Apr,1004
...

To illustrate how you can reuse the coding within a Datascript Module, think of the incoming record as a sequence of fields each with a specific attribute name and value.

GroupAttribute,Atr1,Atr2,Atr3,Atr4,Atr5,Atr6,Atr7,Atr8,Atr9,Atr10,Atr11,Atr12

And think of each emitted record as comprised of three fields.

GroupAttribute,Atr,Val

Now you can write generic code within the Datascript Module that can handle any incoming record in a one-to-many application.

• Since you want to use the Datascript Module in multiple applications, create this artifact within an expressor library.
  • In this example, the library is named modules_library.0 and the Datascript Module is named OneToMany.
• Within the OneToMany module, include the following scripting.
  • Note that line 4, which defines the fully qualified name of the module, is entered by Studio when you first create the module.  It is a concatenation of the library name and module file name.
  • The Datascript Module includes a single function named oneToMany, which takes as arguments the name of the attribute holding a value that uniquely identifies the incoming record and the incoming record that needs to be divided into multiple output records.
  • Observe how the code uses the generic field names GroupAttribute, Atr, and Val to populate each output record.  It’s not until the collection of output records is returned to the calling code that the coding uses the actual attribute names (Year, Month, Value) of the output record.
    • Consequently, the actual attribute names used by the incoming and outgoing records are only referenced within the calling code, which is specific to an application.
    • All coding within the Datascript Module uses generic names applicable to any incoming data.

Now, let’s turn our attention to the code that uses this Datascript Module.  In this case, the application is quite simple.  It reads a file that contains the yearly summary records, uses a Transform operator to process each record into multiple output records, and then writes each monthly output record to a file.

• Line 2 cross-references the Datascript Module.  This statement is equivalent to simply copying and pasting the scripting from the module into the operator’s coding.
• Line 5 invokes the oneToMany function defined in the module.
• Lines 10 through 20 iterate through the collection of output records returned by the module’s oneToMany function, emitting each record individually.
  • To emit more than one record from the Transform operator, the scripting uses an iterator function, another new feature introduced in expressor 3.3.

Using modules written by other developers is no more involved.  In fact, expressor 3.3 ships with many modules that your code can utilize.  These include a cURL module that you can use to invoke on Web services or FTP servers, a SAX XML module that you can use to parse an XML document, a socket module that you can use to interact with an external application, a SQL module that allows your code to execute a SQL statement against a remote database, as well as some expressor Datascript Modules that include utility functions useful for parsing CSV files and serializing and de-serializing expressor Datascript tables.

To use any of these modules, simply include the necessary require statement at the beginning of your scripting.  The expressor Community Knowledge Base includes entries that describe use of these modules. The expressor Studio desktop ETL tool is freely downloadable from the expressor website.   I encourage you to give it a try!

Reference: Pinal Dave (http://blog.SQLAuthority.com)

Here are few important resources links:

Microsoft SQL Server Migration Assistant (SSMA) Team’s Blog

One very front page of the blog, I noticed very interesting diagram – where it displays four database products. One can click on any of them to go to detail description of the same. Here is interesting Video regarding this tool.

• Planning Your Database Migration
• Analyzing Database Using SSMA
• Converting Schema to SQL Server
• Migrating Data Using SSMA
• Automating Database Migration Using SSMA Console Application

If you have any particular question or need help regarding migration – you can always reach out to me and I will do my best to help you.

Reference: Pinal Dave (http://blog.SQLAuthority.com)

Niraj Bhatt works as an Enterprise Architect for a Fortune 500 company and has an innate passion for building / studying software systems. He is a top rated speaker at various technical forums including Tech·Ed, MCT Summit, Developer Summit, and Virtual Tech Days, among others. Having run a successful startup for four years Niraj enjoys working on – IT innovations that can impact an enterprise bottom line, streamlining IT budgets through IT consolidation, architecture and integration of systems, performance tuning, and review of enterprise applications. He has received Microsoft MVP award for ASP.NET, Connected Systems and most recently on Windows Azure. When he is away from his laptop, you will find him taking deep dives in automobiles, pottery, rafting, photography, cooking and financial statements though not necessarily in that order. He is also a manager/speaker at BDOTNET, Asia’s largest .NET user group.

Here is the guest post by Niraj Bhatt.

I am very excited that Fast Track Data Warehouse 3.0 reference guide has been announced. As a consultant I have always enjoyed working with Fast Track Data Warehouse project as it truly expresses the potential of the SQL Server Engine. Here is few details of the enhancement of the Fast Track Data Warehouse 3.0 reference architecture.

The SQL Server Fast Track Data Warehouse initiative provides a basic methodology and concrete examples for the deployment of balanced hardware and database configuration for a data warehousing workload. Balance is measured across the key components of a SQL Server installation; storage, server, application settings, and configuration settings for each component are evaluated.

Read white paper on fast track data warehousing.

Reference: Pinal Dave (http://blog.SQLAuthority.com)

This whitepaper talks about what are best practices for Data Warehousing. This whitepaper discusses ETL, Analysis, Reporting as well relational database. The main focus of this whitepaper is on mainly ‘architecture’ and ‘performance’.

Download Best Practices for Data Warehousing with SQL Server 2008 R2

Reference: Pinal Dave (http://blog.SQLAuthority.com)

For many online applications, there is a need to not only access small amount of information from disk storage, but large amounts in the forms of sets.  SQL Server allows access to these sets of data in a sequential manner that optimizes computer.  This proprietary technology makes SQL Server the go-to system to fit these computing needs.

SQL Server has also solved the problem of changing from “data marts” to data warehouses.  As companies grow, their digital storage needs change and expand as well.  Previously, transferring a system over to an SQL Server meant having to destroy old data during the transfer.  This was the source of a lot of data loss and server problems.  However, the new versions of SQL Server have built-in systems that help organizations transfer to the larger data storage system.

The costs of data storage and data warehousing are becoming a huge concerns for small businesses and corporations alike.  It is a known rule of thumb that the larger the main server, the more expensive it is.  SQL Server also addresses this problem by allowing data warehousing to be run on multiple processors – in the end rendering it much cheaper than having one main data warehousing server.  SQL Servers uses the “hub-and-spoke” system, which allows data warehousing to be broken up by multiple systems without losing any data.

A huge question is how SQL Server can store so much data.  As with any storage system, some data is compressed before it is stored.  Other system will undergo lengthy processes in order to retrieve this compressed data due to updates and possible loss of information, but SQL Server forgoes this updating process, which allows for quick and easy access as well as compression.

The issue of compression of too much data is also solved with parallel processing.  In this system, multiple processors are “enlisted” in order to help store the data.  SQL Server has the technology to divide the data appropriately, and then easily access this data on command.  Rather than slowing one server down, the data can be divided up to be easily handled.

One of the beauties of computing is that the computer can calculate probabilities much faster than a human – indeed, this was one of the main functions of computer development.  SQL Server uses this capacity to store data, using “Probability of Access” technology.  SQL Server can determine which data is “hot” and likely to be accessed the most, which is moderate, and which is cold – that is, there is a low probability of access.  With these designations, SQL Server can store the Hot data in “high performance” storage, so it can be quickly recovered, and cold data is stored elsewhere, freeing up the processors to work more efficiently.

SQL Server has not only optimized storage of “static” data, or individual points of information, but has created a technology that will also store “streaming” data.  Streaming data is a way to record multiple “events” over a period of time – like measurements of temperature thousands of times per minute for instance.  This kind of data storage would completely bog down another server, but SQL Server has developed the technology to efficiently store this data without slowing down processors.  And, of course, the data will be easily accessible because of the data warehousing systems already discussed.

Streaming data is difficult for most servers because of storage purposes.  This streaming data overwhelms the servers because they cannot store and retrieve this much data efficiently, and it bogs down the system.  Other companies have attempted to create programs that allow for access of streaming data, but it often comes at the cost of processor speed.  The usual process is to store streaming data as historical data then retrieving it as static data, which slows down all the processors.  SQL Server has created a system that allows for analysis of this data by showing it in the interactive sector.

In summary, SQL Server is at the very forefront of future data warehousing – DW 2.0.  SQL Server is able to store large amounts of data at a low cost and with maximum efficiency.  Microsoft has taken into account the need for multiple processors and an easy way to transfer old data into this new system.  The technology behind SQL Server is sure to make data access much more efficient.

Reference: Pinal Dave (http://blog.SQLAuthority.com)

• Fast Track Data Warehouse has a primary emphasis on eliminating potential performance bottlenecks.
• It supports maximum of 48 TB data at this moment.
• Currently HP, Dell, Bull, IBM and EMC2 provides necessary hardware for Fast Track Data Warehouse.
• All the Software and Hardware comes in a single package.
• The Base OS is Windows Server 2008 and Base Database is SQL Server 2008 R2.
• FTRA stands for Fast Track Reference Architecture.
• SMP stands for Symmetric Multiprocessing.
• Fast Track is pre-configured and it just works out of the box.
• PDW stands for Parallel Data Warehouse.
• Fast Track Data Warehouse is based on Symmetrical Multi Processing (SMP) and PDW is based on Massively Parallel Processing (MPP).
• Each CPU Core delivers 200 MB per second data.
• This hardware architecture is available at $13,000 per Terabyte.
• It uses RAID 1 Mirror sets
• Recovery model of FTDW is recommended to Simple Recovery mode.
• Turn off AutoGrow for user defined database.
• Leave AutoGrow On for TempDB and set at 4 MB.
• Update the database statistics regularly.

I really had great time doing training for this subject. If you have worked with Fast Track Data Warehouse, what was your experience?

Reference: Pinal Dave (http://blog.sqlauthority.com)

I recently attended a wonderful training session organized by Microsoft on Fast Track Data Warehouse Reference Architectures. If you are regular reader of my blog, you will be well aware of the fact that I am more of the Relational guy than a Business Intelligence professional. I was initially a bit skeptic about this training. However, once I start learning about it, to my surprise, I thought that I am the perfect guy to learn this. In fact, I realized that few of the tricks which this course is suggesting have already been implemented in my earlier consulting assignments.

Fast Track Data Warehouse is a very unique effort by Microsoft, where few Reference Architectures which offer scalability and reliability to huge database are proposed. Fast Track Datasheet provides some excellent points about this reference architecture. I am listing few of the same here.

• Scale from 4 up to 48 Terabytes using compression capabilities in SQL Server 2008 Enterprise
• Choose from industry-standard hardware from Dell, HP, Bull, IBM, EMC and other leading vendors
• Pre-configured servers, storage and networking, specifically balanced and optimized for warehousing
• Implement an enterprise-class solution for less than one-third the price of a comparable Oracle system
• Fast Track Data Warehouse has a primary emphasis on eliminating potential performance bottlenecks.
• Optimized for sequential IO rather than random IO, each Fast Track Data Warehouse is designed to provide up to 200 MB/s per CPU core

The Fast Track approach derives its strength from the advanced data warehouse enhancements included in SQL Server 2008 Enterprise such as compression, parallel partitioning and star join query optimization

Well, I think it is great effort by Microsoft and I must express deep gratitude to Microsoft for giving me opportunity to learn this unique initiative. I clearly see that in near future, lots of Data Warehousing solutions will be switching to this solution. You can read more about this reference architecture over here. The image used in blog post is taken from MS official site of Fast Track.

Reference: Pinal Dave (http://blog.SQLAuthority.com)

Master Data Services helps enterprises standardize the data people rely on to make critical business decisions. With Master Data Services, IT organizations can centrally manage critical data assets company wide and across diverse systems, enable more people to securely manage master data directly, and ensure the integrity of information over time. (Source: Microsoft)

Today I will be talking about the same subject at Microsoft TechEd India. If you want to learn about how to standardize your data and apply the business rules to validate data you must attend my session. MDS is very interesting concept, I will cover super short but very interesting 10 quick slides about this subject. I will make sure in very first 20 mins, you will understand following topics

• Introduction to Master Data Management
  • What is Master Data and Challenges
  • MDM Challenges and Advantage
• Microsoft Master Data Services
  • Benefits and Key Features
  • Uses of MDS
  • Capabilities
• Key Features of MDS

This slides decks will be followed by around 30 mins demo which will have story of entity, hierarchies, versions, security, consolidation and collection. I will be tell this story keeping business rules in center. We take one business rule which will be simple validation rule and will make it much more complex and yet very useful to product.

I will also demonstrate few real life scenario where I will be talking about MDS and its usage.

Do not miss this session. At the end of session there will be book awarded to best participant.

My session details:

Session: Master Data Services in Microsoft SQL Server 2008 R2
Date: April 12, 2010  Time: 2:30pm-3:30pm
SQL Server Master Data Services will ship with SQL Server 2008 R2 and will improve Microsoft’s platform appeal. This session provides an in depth demonstration of MDS features and highlights important usage scenarios. Master Data Services enables consistent decision making by allowing you to create, manage and propagate changes from single master view of your business entities. Also with MDS – Master Data-hub which is the vital component helps ensure reporting consistency across systems and deliver faster more accurate results across the enterprise. We will talk about establishing the basis for a centralized approach to defining, deploying, and managing master data in the enterprise.

Reference: Pinal Dave (http://blog.SQLAuthority.com)

SQL Server 2008 R2 offers an impressive array of capabilities for developers that build upon key innovations introduced in SQL Server 2008. The SQL Server 2008 R2 Update for Developers Training Kit is ideal for developers who want to understand how to take advantage of the key improvements introduced in SQL Server 2008 and SQL Server 2008 R2 in their applications, as well as for developers who are new to SQL Server. The training kit is brought to you by Microsoft Developer and Platform Evangelism.

The training kit is designed for the following technical roles:

• Developers who build applications for the Microsoft platform.
• Microsoft evangelists, technical specialists and consultants.

Download SQL Server 2008 R2 Update for Developers Training Kit (March 2010 Update)

Abstract courtesy Microsoft

Reference: Pinal Dave (http://blog.SQLAuthority.com)

Data Mining Algorithms (Analysis Services – Data Mining)

The data mining algorithm is the mechanism that creates a data mining model. To create a model, an algorithm first analyzes a set of data and looks for specific patterns and trends. The algorithm uses the results of this analysis to define the parameters of the mining model. These parameters are then applied across the entire data set to extract actionable patterns and detailed statistics.

Read more here.

Reference: Pinal Dave (http://blog.SQLAuthority.com)

Join us for a 4 series webcast as follows:

• Part 1: Infrastructure and Resource Management for Business Intelligence – Jan 7
  
• Part 2: BI on your desktop – End to end BI solution from MS – Jan 28
  
• Part 3: IT Managers and Mission Critical Data – What, Why, When and How to manage – Feb 4
  
• Part 4: Understanding security and compliance for Enterprise – Feb 11

Reference: Pinal Dave (http://blog.SQLAuthority.com)

SQL Server Integration Services (SSIS) brings a revolutionary concept of enterprise-class ETL to the masses. The engine is robust enough to handle hundreds of millions of rows with ease, but is simple enough to let both developers and DBAs engineer an ETL process. In this whitepaper, you will see the benefits of migrating your SQL Server 2000 Data Transformation Services (DTS) packages to Integration Services by using two proven methods. You will also see how you can run and manage your current DTS packages inside of the SQL Server 2005 and 2008 management tools.

Ready white paper Migrating DTS Packages to Integration Services

Abstract &  courtesy : Microsoft

Reference: Pinal Dave (http://blog.SQLAuthority.com)

Posted in Business Intelligence, Data Warehousing, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL White Papers, SQLAuthority News, T SQL, Technology Tagged: DTS ]]> http://blog.sqlauthority.com/2009/12/20/sqlauthority-news-migrating-dts-packages-to-integration-services/feed/ 6 pinaldave http://blog.sqlauthority.com/2009/12/10/sql-server-future-of-business-intelligence/ http://blog.sqlauthority.com/2009/12/10/sql-server-future-of-business-intelligence/#comments Thu, 10 Dec 2009 01:30:06 +0000 pinaldave http://blog.sqlauthority.com/?p=7498 ]]> Business Intelligence (BI) is slated to play bigger roles in all kinds of businesses in the coming years. This is not surprising as data analysis and smarter decision making has made the use of BI inevitable in all sizes of businesses across all sectors, including Real estate, IT, mobile devices, governmental agencies, scientific and engineering communities and R&D labs, banking and insurance, to name a few. BI can effectively deal with industry-specific constraints, operations and objectives thereby helping organizations to better understand their customers, optimize their operations, minimize risk, manage revenue, and ultimately improve their results. Moreover, the changing economic environment, which is marked by shrinking budgets, is making way for advancement of successful BI initiatives.

Business Intelligence Forecasting – Predicting Your Way To Success

Today, technology has created one solid platform for the world market, eradicating geographical boundaries. The escalating number of consumers with wide-ranging demands and expectations is making it extremely difficult to conduct business. So meeting customer’s demands has become a top-most priority for all organizations to gain competitive advantage. However, with the implementation of Business Intelligence solutions, companies can stay ahead in the race and keep pace with the market trends and more importantly, meet customers’ expectations. BI Forecasting will help them prepare business strategies while keeping in mind the future events by analyzing the available past data.

Predictive Analytics is the branch of data mining that is used to analyze current and historical data to make predictions about future events. It can help companies optimize their existing processes, well understand customer behavior, spot unexpected opportunities, and anticipate problems before they occur. More and more organizations are realizing the benefits of using data to align their current actions with their future objectives. By incorporating predictive analytics into their daily business operations, they can have better control over the decisions they make every day, which in turn will help them maximize their ROI.

Business Intelligence – Paving Way For Information Democracy

The number of BI users has been constantly growing past the traditional IT “power user”, from dozens, to hundreds and in some cases now thousands of users. Soon this number will be in the millions. Earlier, BI was restricted to only statisticians and corporate analysts and only these selected few could access BI. But today, BI is spreading to nearly every strata of organization, as companies attempt to provide critical data to business users who need it to perform their jobs.

Increasingly, companies are realizing the importance of users having access to timely and relevant insight, which will help in managing performance and aligning them with the mission of the organization. BI is bringing information democracy by providing everyone the insights they require and delivering information to the right people at the right time across the enterprise. More and more organizations are expected to empower their employees with BI for productivity and operational gains.

Software as a Service Business Intelligence –Broadening Usage of BI

A rising number of BI vendors are providing BI software as a service (SaaS) or on-demand business intelligence service. SaaS has been increasingly gaining popularity among small and mid-sized companies. It offers companies a practical option to deploy applications that can provide significant business value. With BI SaaS, companies are not required to make major upfront investments in a hardware server and licenses for the BI software, operating system, web servers and the like. In this case, the software provider hosts them over the Internet for a fee, which can be monthly, quarterly or yearly basis.

The high implementation cost of an end-to-end BI solution was an important factor which discouraged small and mid-sized businesses to adopt BI. With on-demand BI, all sizes of companies can avail the benefits of BI and enhance their business growth. Apart from being cost-effective, it provides several other benefits such as shorter implementation cycles and no maintenance of IT support staff.

Real-time Business Intelligence – Instant Information For Success

Over the last few years BI has been gradually growing in importance and in future, organizations will depend only on real-time information related to their business for smarter decision making. Real-time business intelligence can be defined as the process of delivering information on business operations devoid of any latency. Real-time BI disseminates information about a business in a range from milliseconds to a few seconds after the business event. While traditional business intelligence gives users only historical information, real time business intelligence provides a comparison of present business events with historical events, which helps in identifying a range of issues thereby allowing them to resolve it on time. Moreover, the primary aim of real-time BI is to enable corrective actions to be initiated and business rules to be attuned to optimize business processes.

With consumers demanding faster and improved services from businesses it has become imperative for companies to pay even better attention to consumer satisfaction. They now demand near real-time analysis for intelligent decisions for business success. The rising demand for near-real-time data analysis is making way for a new framework for business intelligence. Business intelligence 2.0 is the recently-coined term to describe the acquisition, provision and analysis of real-time data, which was earlier lacking in BI. BI 2.0 is a part of the constantly developing business intelligence industry and indicates the next step for BI.

BI has truly empowered every businessperson who can now easily use BI to make better decisions and that too on their own, without relying on IT or power analysts to prepare and interpret results for them. In a couple of years BI applications will become as commonplace as spreadsheet applications within all organizations that are midsize or larger. Organizations making effective use of BI technologies will rise and stay far ahead from their competitors. It is expected that BI will soon replace the gut-feel management with real data-based decision-making. Over the coming years, business intelligence will undergo transformation that will have a broad and lasting impact. It will revolutionize the way that we think about business and the way business decisions are made. It’s only when thoughtful analysis supersedes gut feeling and conventional perception, we will enter the next level of business intelligence that will empower businesses with the capacity to reason, prepare, forecast, resolve issues and innovate.

Reference: Pinal Dave (http://blog.sqlauthority.com),DNS

By leveraging on the power of Business Intelligence (BI) organizations can understand and analyze large volumes of rapidly changing data for effective decision-making. BI helps decision makers to harness the advantage of change to create competitive advantages, achieve corporate objectives, and make better decisions, faster.

Business Intelligence to put it simply, is all about improving decision making within an organization. By presenting the latest information to the right people at the right time the quality of decisions as well as their timeliness can be improved. These days, organizations are embedding BI into business process to create a better workflow, apart from gaining other benefits of BI. In fact, BI can make all areas of business strategic and make them value-rich.

Proper BI deployment – Aligning all business metrics

Business Intelligence adds value to all departments or business units within an organization. In general, revenue-generating areas of business such as sales and product manufacturing are often considered critical to a company’s success. While other areas like HR and operations are a requisite to do business but are not considered crucial for revenue generation and for improving business performance. So naturally, when it comes to adding BI value to a business, only revenue-generating departments and areas strike the mind first. However, this is not the case and with appropriate metrics and data this thought process needs to be changed.

Traditionally, Business Intelligence focused primarily on siloed information. Functional areas like Finance, Sales, Marketing and HR created metrics. To provide value to an organization from top to bottom, metrics must align from top to bottom across business units and do away with silos. Undoubtedly, there is still value in maintaining HR, Finance and Sales-specific metrics; however, if the metrics of all areas of an organization are aligned to corporate metrics and objectives, it can prove to be a potent tool to facilitate both the top and bottom lines of an organization. From line leaders to middle managers to executives, they all must have metrics and dashboards that are congruent and support one another.

Nowadays, businesses are putting in a lot of effort to come up with smart, intelligent decisions that will help them run a strategic operation and gain competitive advantage. One of the key aspects of proper Business Intelligence deployment is the alignment of metrics from top to bottom across functional areas, which can go a long way in ensuring business success.

Scenario: Business Intelligence in real world

Let us take a real world scenario and try to understand how proper alignment of business metrics makes all areas of a business more strategic and value-rich.

In our example, let’s take a car manufacturing company Speed Motor Manufacturing. The top priority corporate objective of the company is to increase its profit margin by 5%. To attain this target, it is important to consider each area of business to help impact the bottom line, including the HR department. In general, HR department is considered important by all organizations; however, it is not considered strategic or a contributor to the bottom line.

Now, let’s assume that the operation’s business unit of Speed Motor Manufacturing has ascertained that the best way they can contribute to the overall margin objective is to make 20% more use of a new type of machine which can speed up assembling car components, which will in turn improve the speed of manufacturing process and more cars will be produced per day. However, this complex machine cannot be operated by all individuals. It requires specialized training and more experienced users. Now, in order to meet the objective, the new car manufacturing machine requires an increased usage by 20% for the year.

Here, HR department can play a major role. The HR department ties its goals and metrics to help operations meet their goals, thus directly contributing to the corporate objective of increasing the profit margin by 5%. To meet the objective, HR department must retain the skills required for operating these complex machines, train individuals to operate the new machines and even hire new skills that can operate the new machines. HR’s metrics map specifically to the operations team’s metrics, which map directly to the overall corporate metrics.

In our example, we saw how metrics are aligned top to bottom and across the organization.

Having said that, in order to successfully measure these metrics and create dashboards to check advancement and achievement, the data and the data structure must be available and appropriate. If the data structure is present and the objectives are properly aligned, all aspects of an organization can be strategic and contribute to enhance the business performance for assured success. The HR business unit measures the training it conducted, which increased the number of qualified operators of the new machineries. The operations leader measures the number of hours the new machine was operated compared to the previous year. The finance team measures the profitability of the operations in the current year compared to last year and quantifies operations increase in margin. Thus, we see how all the departments work together towards a common objective, set up metrics to monitor and measure their success and link them accordingly.

In a nutshell, proper deployment of Business Intelligence makes way for perfect objective alignment and improved vision throughout the organization for business success and competitive advantage.

Reference : Pinal Dave (http://blog.SQLAuthority.com), DNS

The performance and stability of any application solution—whether line of business, transactional, or business intelligence (BI)—hinges on the integration between solution design and hardware platform. Choosing the appropriate solution architecture—especially for BI solutions—requires balancing the application’s intended purpose and expected use with the hardware platform’s components. Poor planning, bad design, and misconfigured or improperly sized hardware often lead to ongoing, unnecessary spending and, even worse, unsuccessful projects.

The ultimate goal of the Fast Track reference configurations is to take the guesswork out of hardware and architectural decisions for the database layer of Microsoft SQL Server-based BI solutions. To help you get started with the Fast Track reference architectures, understand their approach and value, and use them to implement the best possible solution, this paper includes:

• An overview of the new Fast Track reference architectures
• A review of BI fundamentals and applicable hardware considerations
• The tested Fast Track reference architecture components and options
• Prescriptive guidance for designing and optimizing a solution
• Resources available to help you choose or create a new hardware configuration

The Fast Track Data Warehouse reference configurations focus on the central database component of a BI solution, commonly called the data warehouse or data mart. These Fast Track reference configurations target solutions on a single server estimated at up to 32TB of data.

Read An Introduction to Fast Track Data Warehouse Architectures

Abstract courtesy : Microsoft

Reference: Pinal Dave (http://blog.SQLAuthority.com)

Posted in Business Intelligence, Data Warehousing, Pinal Dave, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL White Papers, SQLAuthority News, T SQL, Technology ]]> http://blog.sqlauthority.com/2009/12/07/sql-server-white-paper-an-introduction-to-fast-track-data-warehouse-architectures-by-erik-veerman/feed/ 0 pinaldave http://blog.sqlauthority.com/2009/12/04/sql-server-2008-star-join-query-optimization/ http://blog.sqlauthority.com/2009/12/04/sql-server-2008-star-join-query-optimization/#comments Fri, 04 Dec 2009 01:30:01 +0000 pinaldave http://blog.sqlauthority.com/?p=7470 ]]> Business Intelligence (BI) plays a significant role in businesses nowadays. Moreover, the databases that deal with the queries related to BI are presently facing an increase in workload. At present, when queries are sent to very large databases, millions of rows are returned. Also the users have to go through extended query response times when joining multiple tables are involved with such queries. ‘Star Join Query Optimization’ is a new feature of SQL Server 2008 Enterprise Edition. This mechanism uses bitmap filtering for improving the performance of some types of queries by the effective retrieval of rows from fact tables.

Improved Query Response Times
In general, data warehouses employ dimensionally modeled star or snowflake schemas. These schemas have one or more than one fact tables that contain transactional data and many dimension tables, which holds information such as product data, customer information, and times and dates – all these define the fact table data. Usually, foreign keys are employed for maintaining relationships between the rows in fact tables and also between the rows in the dimension tables. Databases that contain star schemas are recognized by SQL Server 2008 Enterprise. It uses the new Star Join Query logic for processing queries against such star schemas more efficiently. Typically, on an average, data warehouse queries run faster to approximately 20 percent.

Automatically Implemented
Star Join Query Optimization is automatically implemented by the SQL Server. It does not require a special database or application configuration. The query processor will usually optimize queries with medium selectivity (this refers to the queries that retrieve approximately 10% to 75% of rows from a fact table). Such queries are usually handled using hash joins to join the dimension and fact tables by employing the foreign keys to identify the matching rows. A hash table is built for each dimension table referenced in the query in the case of hash joins; the optimization process uses these hash tables for deriving bitmap filters. The key values from each dimension table are identified by bitmap filters; these key values qualify for inclusion in the query. When the fact table is scanned, the bitmap filters are applied to it. These bitmap filters eliminate those rows of the fact table which are not qualified for inclusion in the result set. The most selective bitmap filter is applied first as it is found to eliminate the highest number of rows. Since the eliminated rows do not need further processing, the subsequent filters need not be applied to them – this way the process becomes more efficient.

Query Selectivity
The performance is enhanced in the case of medium selectivity queries while using bitmap filtering because the rows are filtered before any joins are implemented. Hence, there is a decrease in the number of rows that are processed by each join. Bitmap filtering is not applied when queries are highly selective (i.e., those queries that return less than 10% of the rows in a fact table). In such case, a nested loop join is found to be generally more efficient. Similarly, when the queries are not very selective at all (queries which return more than 75% of the rows in a fact table), bitmap filtering is not applied as there are very few rows to be filtered, and hence, there is no requirement of enhancement in performance in this case.

Integer Data Types
Star join optimization is found to give the highest efficiency when the data type of the columns used in the joins is integer. This feature enables the bitmap filter to be applied as part of the initial table or index scan rather than being used at a later stage in the query plan. Most of the queries are benefited from star join optimization since foreign key relationships are commonly implemented using integer-based alternate key columns.

Reference : Pinal Dave (http://blog.SQLAuthority.com)

Posted in Best Practices, Business Intelligence, Data Warehousing, Pinal Dave, SQL, SQL Authority, SQL Joins, SQL Optimization, SQL Performance, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology ]]> http://blog.sqlauthority.com/2009/12/04/sql-server-2008-star-join-query-optimization/feed/ 2 pinaldave http://blog.sqlauthority.com/2009/11/28/sqlauthority-news-sql-server-r2-resources-downloads-documentations/ http://blog.sqlauthority.com/2009/11/28/sqlauthority-news-sql-server-r2-resources-downloads-documentations/#comments Sat, 28 Nov 2009 01:30:10 +0000 pinaldave http://blog.sqlauthority.com/?p=7433 ]]> Microsoft SQL Server 2008 R2 November Community Technology Preview
Building on SQL Server 2008, R2 provides an even more scalable data platform with comprehensive tools for managing your databases and applications, improving the quality of your data, and empowering your users to build rich analyses and reports using tools they are already familiar with.

Microsoft SQL Server 2008 R2 November Community Technology Preview Feature Pack
The Microsoft SQL Server 2008 R2 Feature Pack is a collection of stand-alone packages which provide additional value for SQL Server 2008 R2.

SQL Server 2008 R2 Books Online Community Technology Preview November 2009
Download the a community technology preview (CTP) release version of the documentation and tutorials for Microsoft SQL Server 2008 R2.

Microsoft SQL Server 2008 R2 November Community Technology Preview – Express Edition
Microsoft SQL Server 2008 R2 Express is a powerful and reliable data management system that delivers a rich set of features, data protection, and performance for embedded application clients, light Web applications, and local data stores.

SQL Server 2008 R2 November CTP Reporting Services Add-in for Microsoft SharePoint Technologies 2010
The Microsoft® SQL Server 2008 R2 November CTP Reporting Services Add-in for Microsoft SharePoint Technologies allows you to integrate your reporting environment with SharePoint to experience the benefits of using the collaborative environment provided by SharePoint.

Microsoft SQL Server 2008 R2 November CTP StreamInsight
Microsoft® SQL Server® StreamInsight is Microsoft’s new Complex Event Processing technology to help businesses derive better insights by correlating event streams from multiple sources with near-zero latency.

Microsoft SQL Server 2008 R2 November CTP Report Builder 3.0
Microsoft SQL Server 2008 R2 Report Builder 3.0 provides an intuitive report authoring environment for business and power users. It supports the full capabilities of SQL Server 2008 R2 Reporting Services. The download provides a stand-alone installer for Report Builder 3.0.

Microsoft SQL Server 2008 R2 – PowerPivot for Microsoft® Excel 2010 – November Community Technology Preview
Microsoft® PowerPivot for Microsoft Excel 2010 provides ground-breaking technology, such as fast manipulation on large data sets (often in millions of rows), streamlined integration of data, and the ability to effortlessly share your analysis through Microsoft SharePoint 2010.

What’s New in SQL Server 2008 R2 November Community Technology Preview (CTP)
This document describes what’s new in the November CTP of Microsoft SQL Server 2008 R2. Analysis Services, Database Engine, Reporting Services, and Service Broker all have new or enhanced features in this release. Master Data Services is a new component in this release.

Reference: Pinal Dave (http://blog.sqlauthority.com)

Business intelligence (BI) is a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data from various data sources, thus providing enterprise users with reliable and timely information and analysis for improved decision making. To put it simply, BI is an umbrella term that refers to an assortment of software applications for analyzing an organization’s raw data for intelligent decision making for business success. BI as a discipline includes a number of related activities, including decision support, data mining, online analytical processing (OLAP), querying and reporting, statistical analysis and forecasting.

Business Intelligence for Success

Business intelligence equips enterprises to gain business advantage from data. Once an organization is powered with BI it can anticipate enhanced turnaround time on data collection, come up with fresh ideas for novel business initiatives, foresee accurate picture of customer needs and demands, and perform more targeted marketing campaigns. In addition, it will gain enhanced knowledge that will help it advance its brand into the top slot in terms of market share, cut down its client churn, reduce its overheads and also diminish delays in supply chain, among other advantages. Decisions purely based on the gut feeling cannot assure success; but in BI’s fact-based decision-making framework, confident decisions can be made for assured business success. Further, BI makes an organization agile thereby giving it a competitive edge in today’s evolving market condition.

Business Intelligence Scenario

Every day, a dairy company collects from 2,000 stores information which is uploaded to servers at its headquarters at the end of the day. This information is used by the chain’s main office to instantly analyze key operational measures such as the number of dairy products sold, profits, trends, and so forth.

Next day, by early morning the company’s managers receive performance data. Next, they verify current revenue, time required to perform each job, and other performance measures. With BI, franchisees with multiple locations can have consolidated views, as can the company’s regional managers.

This scenario clearly explains how implementation of Business intelligence can be very fruitful for an organization. BI can catalyze a business’s success in terms of

Revenues

• Distinguish the products and services that drive revenues.
• Rank customers and locations based on profitability.

Customer relationship management

• Categorize low-value customers and work toward improving their value.
• Find customer relationship issues early and take timely action to resolve them.

Sales and marketing

• Aim at high-value customers to minimize marketing risk.
• Rank the success of product promotions based on product and market segment.
• Find what is in the sales pipeline

Business Intelligence Terms

Ad Hoc Query – Ability to create an immediate ‘request for information’ using an analytical reporting tool to answer a specific business-related query.

Aggregation – Predefined summaries based on comprehensive data in the data warehouse.

Atomic data – Topmost level of detail in the data warehouse.

Attribute – Field represented by a column in reports, tables and charts.

Cube – A powerful analysis tool that is used for viewing and analyzing data in a multi-dimensional format, from any angle, in any combination, through a ‘slice and dice’ and drilldown approach.

Data – Factual information for analysis.

Data Mart – A logical subset of related information, typically around one or a few business processes, or a particular subject area.

Data Warehouse – A repository of well-organized corporate data for Business Analysis and Reporting. It is also a collection of data marts.

Database – A collection of data arranged for convenient and quick search and retrieval.

OLAP – Online Analytical Processing is a category of database software that provides an interface to help users quickly and interactively scrutinize the results in a variety of dimensions of the data.

Balanced Scorecard – A Performance management tool that recapitulates an organization’s performance from several standpoints on a single page.

Dashboard – A reporting tool that presents key indicators on a single screen, which includes measurements, metrics, and scorecards.

Data Cube – A multidimensional structure (typically over three dimensions) that forms the basis for analysis applications, allowing various types of calculations and aggregations.

Data Mapping – Process of spotting a source data element for all data elements in the target data warehouse environment.

Data Quality – It is related to facets such as accessibility, comprehensiveness, exactness, steadiness, significance and timeliness of data.

Data staging – A system where operations like data extraction, transformation and loading operations are executed.

Dimension Table – It contains attributes that describe fact records in the fact table. For instance, pupil enrollment is a central fact table with several dimensions linked to it such as Faculty, Term, and Degree.

Drill Down – A feature that allows users to click on an item to navigate through hierarchy and move to a level of better detail.

EIS – Enterprise Information System – A category of applications and technologies for presenting and analyzing corporate and external data for various management purposes.

ETL – Extract, Transform and Load -It is a three-step process at the center of data warehousing. In the first step, operational data is extracted from multiple sources. In the next step, it is transformed into the required type. In the final step, data is loaded into the data warehouse.

Forecasting – It is the formulation of trends, predictive models, and scenarios to prepare for the future for improved decision making.

Gap analysis – Study of whether the available business data supports business requirements, thus answering questions related to data accessibility, amount of data, missing data, and legacy systems. It examines information and decides on the resources and efforts required to satisfy requirements.

Granularity – Level of detail or summarization of data in the data warehouse. The more the detail, the higher the level of granularity.

Institutional performance management – The process of basing an organization’s actions and decisions on actual measured results of performance. It incorporates performance measures, benchmarks, and goals to attain best results.

Measures – A quantifiable, specific piece of information.

Metrics – Measures of performance that observe progress and evaluate trends within an organization.

Normalization – Standard database design technique for relational databases.

Slice and Dice – Multidimensional tools allow users to view data from any angle. The ability to select various angles to view data from is called slice and dice capability.

Snapshot – View of data at a particular moment.

Reference: Pinal Dave (http://blog.SQLAuthority.com), DNS

SQL Server Accelerator for Business Intelligence (BI) is no more supported by Microsoft. Microsoft does not provide any support for this solution accelerator and has no plans to release future versions. Microsoft SQL Server 2005 and later versions include most of the functionality of the accelerator and are supported according to the Microsoft Product Lifecycle.

For the same reason, this tool is not included in the course as well new developer should not focus learning the same.

Reference : Pinal Dave (http://blog.sqlauthority.com)

Who all are involved in Data Mining?

Data mining is an activity, which can be programmed, that involves the analysis of data and finally revealing the hidden patterns. Architects, Developers and Analysts are involved in the data mining process.

Data mining is usually carried out by an Analyst. However, it is not necessary that every time, he/she will be able to identify all the hidden patterns of a particular data set, irrespective of its size. Finally these identified patterns are converted into useful information for business purpose. A Developer combines data mining with application solutions, and an Architect understands the needs of the developer and the analyst and meets them accordingly.

Microsoft and Data Mining

Microsoft provides a wide range of data mining options, which includes collaborative solutions and ad hoc analysis (in MS Office Excel). A free plug-in is available in MS Office Excel 2007, which helps the analyst to analyze the data patterns. In addition to this plug-in, the Business Intelligence Development Studio (BIDS) that is free with the SQL Server can also be used for data mining purpose.

It should be noted that data mining is not done on the basis of any known data patterns or any other additional information. The results obtained of data mining are generated from the data presented and not from any other resources. Microsoft data mining applies mathematical techniques on the available data set to obtain models. In addition to BDIS, the .NET framework and Data Mining extensions (DMX) language is also provided by Microsoft for custom solutions. At times, data mining is also known as machine learning.

Results of Data Mining – Data Mining Models

Microsoft data mining results in data mining models, which are statistical information – either predictive or descriptive. A Microsoft Mining Model consists of the following three components: metadata, which is information about the data; patterns, which are mathematical formulas or rules; and bindings, where the data is defined. The statistical results may not be understandable in relation to a business perspective. Hence, these results or models must be translated to useful business information. One who engages in data mining is responsible for creating a link or relation between the resultant data model and the respective business problem.

Role of Data Miner (or Analyst)

A data miner should undergo adequate training with regard to all the tools and technologies used in mining and should not limit himself/herself to only those tools that are required for that particular organization/business. In fact, it is the responsibility of the organization to provide training to the data mining professional on a broader perspective. Data mining is never complete without the analyst. The application of the results of data mining to a specific business significantly depends on how far the analyst has understood the industry-specific objectives.

Applications of Data Mining

Data mining is used in various applications such as forecasting business and customer trends, detecting fraud (especially in the banking sector), generating customized advertisements, grouping customers on the basis of their purchasing trends, and risk analysis.

Benefits of Microsoft Data Mining

Microsoft data mining is extensible. It can be licensed through SQL Server 2008 (or SQL Server 2005) and it is compatible with other technologies, thereby allowing access to data in different formats. Microsoft data mining can also be used for business intelligence solutions and it is scalable unlike other data mining products.

Reference : Pinal Dave (http://blog.sqlauthority.com)

SQL Server 2008 R2 introduces new management tools to help improve IT efficiency and productivity. Investments in application and multi-server management will help organizations proactively manage database environments efficiently at scale through centralized visibility into resource utilization. Such investments can help streamline consolidation and upgrade initiatives across the application lifecycle—all with tools that make it fast and easy.

This paper introduces the new extensions in SQL Server Management Studio and the Control Point Explorer, and it walks through the simple process of setting up a SQL Server managed server group, including SQL Server Control Point installation, enrolling an instance into central management, extracting Data-tier Applications from existing deployments, and deploying Data-tier Applications to the new managed server group.

Read whitepaper Application and Multi-Server Management

Abstract courtesy : Microsoft

Reference : Pinal Dave (http://blog.sqlauthority.com)

Posted in Business Intelligence, Data Warehousing, SQL, SQL Add-On, SQL Authority, SQL Documentation, SQL Download, SQL Query, SQL Server, SQL Tips and Tricks, SQL White Papers, SQLAuthority News, T SQL, Technology ]]> http://blog.sqlauthority.com/2009/08/30/sqlauthority-news-application-and-multi-server-management/feed/ 0 pinaldave http://blog.sqlauthority.com/2009/05/08/sqlauthority-news-future-of-business-intelligence-and-databases-article-by-nupur-dave/ http://blog.sqlauthority.com/2009/05/08/sqlauthority-news-future-of-business-intelligence-and-databases-article-by-nupur-dave/#comments Fri, 08 May 2009 01:30:57 +0000 pinaldave http://blog.sqlauthority.com/?p=5013 ]]> This article is submitted by Nupur Dave

Future of Business Intelligence and Databases

The term business intelligence (BI) was coined by Howard Dresner in the early 1990s. He defined Business Intelligence as “a set of concepts and methodologies to improve decision making in business through use of facts and fact-based systems.” In a time when data warehousing was considered leading-edge he created the vision that led to the development of business intelligence, as it is known today.  The once visionary BI is now commonplace and in near future a momentous transformation is about to take place.

BI is all set to bring information democracy where end users across an organization will be able to view information related to their segment. Further, with the rising consumer expectations there will be demand for capability requirements of BI as well.

Information Democracy with Business Intelligence

BI plays a crucial role in providing cost-effective, comprehensive insights into business processes that help organizations make informed, strategic decisions that assure enhanced business results. Not long ago, BI was the domain of statisticians and corporate analysts. Only the selected few could access BI. Today, BI is spreading to nearly every strata of organization, as companies attempt to provide critical data to business users who need it to perform their jobs. It is quite evident that BI is instrumental in bringing information democracy and it provides everyone the insights they require – delivering information to the right people at the right time across the enterprise. More and more organizations are expected to empower their employees with BI for productivity and operational gains.

Business Intelligence and Performance Management for competitive advantage

Today, when economic downturn is adversely affecting the world market, making insightful decisions about major changes in their business and markets has become crucial for organizations. They need to reconsider their strategies and operating plans. Only companies that can anticipate emerging trends and opportunities, refocus business strategy and vision, and align resources to maximize performance can effectively deal with the adversities of economic downturn and gain competitive advantage. BI and Performance Management brings business sensibility with enhanced visibility and insights, by delivering accurate and latest information to the right people for faster, improved, strategic and tactical decision making. Companies have increasingly starting realizing the importance of timely performance information to improve results, which will further make way for the rising demand for BI and Corporate Performance Management Solutions.

Business Intelligence via Software As A Service (SaaS)

It is being anticipated that in the coming years a number of organizations will have an industry-specific analytic application delivered via software as a service (SaaS) as a standard component of their BI portfolio. Information aggregators will bank on SaaS to deliver domain-specific analytic applications, which are made from industry data they assemble, and bring the balance of power in the BI platform market in their favor. Organizations will share their data with only those aggregators that can assure security as well as confidentiality. Thus, countless information aggregators offering SaaS analytic applications might appear; however, a virtual monopoly is expected to remain within each vertical niche owing to the high barricade that blocks the entry for others.

Business Intelligence and Social Software

Collaborative decision making will soon become the norm, which amalgamates social software with BI Platform capabilities. With social software users can tag assumptions made in the decision-making process to the BI framework, which significantly enhances the business value of BI as it ties all the good. Take for instance, in order to decide how much to invest in marketing a new product, users can tag their assumptions about the future sales of that product to a key performance indicator (KPI) that measures product sales. Whenever the KPI surpasses a threshold the BI platform would send alerts to the user to inform the decision makers about assumptions that no longer hold true.

With the advent of social software it has become easier to utilize the upsurge of interest in informal collaboration. As an alternative to the advancing of the usual top-down decision-making initiative, social software will be employed to collaborate and make decisions.

Business Intelligence Across All Sectors

BI is on its way to become an integral component of every type and size of organization across all sectors, including Real estate, IT, mobile devices, governmental agencies, scientific and engineering communities and R&D labs, banking and insurance, to name a few. BI can effectively deal with industry-specific constraints, operations and objectives thereby helping organizations better understand their customers, optimize their operations, minimize risk, manage revenue, and ultimately improve their results. More importantly, it is expected that in a couple of years BI will soon replace the gut-feel management with real data-based decision-making.

Business Intelligence For Real-Time Data

Today, consumers are demanding faster and improved services from businesses. As it has become imperative for companies to pay attention to consumer satisfaction in order to stay competitive they are depending even more on BI. They now demand near real-time analysis for intelligent decisions for business success. The rising demand for near-real-time data analysis is making way for a new framework for business intelligence. BI 2.0 is the recently-coined term to describe the acquisition, provision and analysis of real- time data, which was earlier lacking in BI. BI 2.0 is a part of the constantly developing business intelligence industry and indicates the next step for BI.

In near future, companies will switch from the traditional model of making business adjustments on a quarterly basis to business intelligence and performance management tools to make real-time shifts in strategy to respond to changes in the marketplace.

Over the coming years, business intelligence will undergo transformation that will have a broad and lasting impact. It will revolutionize the way that we think about business and the way business decisions are made. It’s only when thoughtful analysis supersedes gut feeling and conventional perception, we will enter the next level of business intelligence that will empower businesses with the capacity to reason, prepare, forecast, resolve issues and innovate.

Reference : Nupur Dave (http://blog.sqlauthority.com)

Meeting started with introduction and welcome to all the members from SQL Server MVP – Pinal Daveand followed by technical session of “SQL Server 2008 – Backup and Compression” by Pinal Dave. This session was very special because not every database user is aware of the special feature of SQL Server 2008 and how it can improve the life of SQL Administrator.

Download the PDF of the SQL Server Presentation

[Please leave a comment in this thread if you are interested in SQL script which I had used in this demo presentation.]

I am very much encouraged as a User Group Leader because one of the member of UG Tejas Shah presented his very first technical session today. Tejas Shah delivered excellent session of “Introduction to Service Broker”. It is very new subject and UG members liked it.

The most awaited session was presented by SQL Server MVP – Pinal Dave where he discussed “How to become MVP?”. Pinal explained the advantages and responsibility of becoming MVP. There were many questions regarding this subject. I think UG members and SQL enthusiastic were really into learning how to become MVP. I did my best to answer all the questions which I had received. Community Service is the short answer of this subject.

Pinal Dave shared his experience of attending MVP Open Day 2008, Goa. There were nearly 15 brand new photographs of the Open Day. To know more about MVP Open Day 2008 please visit following article.

SQLAuthority News – Author Visit – South Asia MVP Open Day 2008 – Goa – Group Photo

UG president Jacob who is New York participated in this meeting using GoToMeeting and discussed his experience of attending PASS conference. This was very special as this was first time when we had live presentation using Audio Video community tools.

Well last but not least, we had our ice creams. Ice Cream was from Havmore and flavor was ‘lonavali’. Let me know if you have any suggestions. Again, if you are interested in participating in UG meetings, please let me know. It is free and good networking with other tech developers.

Pinal Dave UG Introduction

Pinal Dave Technical Session

Tejas Shah on Service Broker

UG Group Photos

Reference : Pinal Dave (http://blog.SQLAuthority.com)