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Microsoft SQL Server Business Intelligence Development Beginner’s Guide Sample Chapter 5, Master Data Management, guides readers on how to manage. Read Microsoft SQL Server Business Intelligence Development Beginner’s Guide by Reza Rad with a free trial. Read millions of eBooks and audiobooks on. This book will give you a competitive advantage by helping you to quickly learn how to design and build BI system with Microsoft BI tools. This. This book starts with designing a data warehouse with dimensional modeling, and then looks at creating data models based on SSAS multidimensional and Tabular. Get Microsoft SQL Server Business Intelligence Development: Beginner’s Guide now with the O’Reilly learning platform. O’Reilly members experience live.❿
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Microsoft sql server 2014 business intelligence development beginners guide pdf free
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<p>Simon has worked across a numb. James is truly a unique guest as he credits much of his Grace also shares her take on why a liberal arts education is valuable in technology industries, plus how data can help marketers create personalized and impactful customer experiences.</p>
<p>Key Takeaways: Include your customers in the design process. Changing the design process to be consultative, collaborative, and more of a conversation with the customer ensures that the end result meets their needs. Such a minor mindset shift can lead to exceptional results. Critical thinking matters. In such a rapidly changing field as marketing, being able to intuitively bridge the gap between knowledge and expertise has become an even more valuable skill.</p>
<p>With some simple tips, you can cultiva. What can you do with it? Astrato is a data analytics and business intelligence tool built on the cloud and for the cloud. Alexander discusses the features and capabilities of Astrato for Jonathan Sharr is the kind of story that keeps us going! Since then he went They discuss the role of data analytics in customer personalization, driving business, and creating a competitive advantage.</p>
<p>Al and Benn Stancil discuss Mode, data, leadership and analytics. To kick off this new podcast, we wanted to switch the roles… today, Cindi is our guest! Tune in for a discussion between Cindi and Ian Faison, Executive Producer of The Data Chief, about culture, leadership, innovation, data for good, and what you can expect from future episodes of The Data Chief.</p>
<p>Key Takeaways: How being an aspiring writer — but a lousy typist — got Cindi into technology. The biggest changes Cindi has witnessed over the last 20 years. Where the new role of CDO Chief Data Officer fits into the hierarchy among the CIO and CAO, the barriers someone in this position can expect to face, and the ways we can expect this role to evolve in the months and years to come.</p>
<p>When it comes to actual decision-making, architects 43 per cent were. For example, you might want to hand over database management to a service provider to free up capacity and focus on key business initiatives. Managed databases are increasingly popular — in our l. To compile the Leap Readiness Index for the financial sector, we have included 44 top retail banks, insurance services, and leading payment companies based on their revenue by the end of The ranking is based on six main factors: 1 financ.</p>
<p>The Caribbean has just launched its first online database aimed at tracking human rights violations and providing data to assist advocacy work. Using dynamic third party and proprietary data sources, 25 segments have been built to give highly informative insights based on the actual behaviour and socio-economic status of a region, area unit or meshblock. Customers can be meaningfully tagged. First, a safe bet… database as a service DBaaS will continue to grow.</p>
<p>This useful online tool for researchers in British Columbia is unlike any digital resource found elsewhere. Two big searchable online databases provide free instant access to detailed information on 70, West Coast vessels and 58, mariners and r. Every year, on the first Friday in July, Database Administrator Appreciation Day takes place, which is about saying thank you to the people who are responsible for organising data. They are the ones who set up and run databases so they can manage and.</p>
<p>Review must be at least 10 words. Time for action — loading customer information from a flat file into a database table with a Data Flow Task. Time for action — looping through CSV files in a directory and loading them into a database table. Time for action — creating a data mining solution with the Microsoft Decision Tree algorithm.</p>
<p>Time for action — finding the best mining model with Lift Chart and Profit Chart. Time for action — changing the background color of data rows based on expressions. Time for action — creating your first dashboard with PerformancePoint Dashboard Designer. Time for action — visualizing time-based information with a scatter chart. Time for action — designing reports and working with the local processing mode. Time for action — changing a report configuration with a ReportViewer Object through code behind.</p>
<p>All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented.</p>
<p>However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals.</p>
<p>However, Packt Publishing cannot guarantee the accuracy of this information. Reza Rad has more than 10 years of experience in databases and software applications. Most of his work experience is in data warehousing and business intelligence. He has a Bachelor’s degree in Computer Engineering. He has worked with large enterprises around the world and delivered high-quality data warehousing and BI solutions for them.</p>
<p>He has worked with industries in different sectors, such as Health, Finance, Logistics, Sales, Order Management, Manufacturing, Telecommunication, and so on. Reza has written books on SQL Server and databases. His blog contains the latest information on his presentations and publications.</p>
<p>Reza is a Mentor and a Microsoft Certified Trainer. He has been in the professional training business for many years.</p>
<p>He conducts extensive handed-level training for many enterprises around the world via both remote and in-person training. He has worked for more than 10 years with Oracle Corporation and has held various positions, including that of a Practice Manager. He had been co-running the North Business Intelligence and Warehouse Consulting practice, delivering business intelligence solutions to Fortune clients.</p>
<p>During this time, he steadily added business skills and business training to his technical background. In , John decided to leave Oracle and become a founding member in a small business named iSeerix. This allowed him to focus on strategic partnerships with clients to design and build Business Intelligence and data warehouse solutions. John’s strengths include the ability to communicate the benefits of introducing a Business Intelligence solution to a client’s architecture.</p>
<p>He has gradually become a trusted advisor to his clients. His philosophy is based on responsibility and mutual respect. He relies on the unique abilities of individuals to ensure success in different areas and strives to foster a team environment of creativity and achievement.</p>
<p>Through the years, he has worked in numerous industries with differing technologies. This broad experience base allows him to bring a unique perspective and understanding when designing and developing a data warehouse. The strong business background, coupled with technical expertise, and his certification in Project Management makes him a valued asset to any data warehouse project. Goh Yong Hwee is a database specialist, systems engineer, developer, and trainer based in Singapore.</p>
<p>Throughout his training, he has consistently maintained a Metrics that Matter score exceeding 8 out of He has also been instrumental in customizing and reviewing his training center’s training for its clients. Use Template Explorer to build and manage files of boilerplate text that you use to speed the development of queries and scripts. Template Explorer. Use the deprecated Solution Explorer to build projects used to manage administration items such as scripts and queries.</p>
<p>Solution Explorer. Use the visual design tools included in Management Studio to build queries, tables, and diagram databases. Visual Database Tools. Use the Management Studio language editors to interactively build and debug queries and scripts. Reza Rad has more than 10 years of experience in databases and soft ware applications.</p>
<p>Most of his work experience is in data warehousing and business intelligence. He has a Bachelor’s degree in Computer Engineering. He has worked with large enterprises around the world and delivered highquality data warehousing and BI solutions for them. Let’s elaborate on this example. As you can see from the preceding list, the geographical information in the records is redundant.</p>
<p>This redundancy makes it difficult to apply changes. For example, in the structure, if Remuera , for any reason, is no longer part of the Auckland city, then the change should be applied on every record that has Remuera as part of its suburb. The following screenshot shows the tables of geographical information:. So, a normalized approach is to retrieve the geographical information from the customer table and put it into another table.</p>
<p>Then, only a key to that table would be pointed from the customer table. In this way, every time the value Remuera changes, only one record in the geographical region changes and the key number remains unchanged. So, you can see that normalization is highly efficient in transactional systems. This normalization approach is not that effective on analytical databases. If you consider a sales database with many tables related to each other and normalized at least up to the third normalized form 3NF , then analytical queries on such databases may require more than 10 join conditions, which slows down the query response.</p>
<p>In other words, from the point of view of reporting, it would be better to denormalize data and flatten it in order to make it easier to query data as much as possible. This means the first design in the preceding table might be better for reporting.</p>
<p>However, the query and reporting requirements are not that simple, and the business domains in the database are not as small as two or three tables. So real-world problems can be solved with a special design method for the data warehouse called dimensional modeling.</p>
<p>There are two well-known methods for designing the data warehouse: the Kimball and Inmon methodologies. The Inmon and Kimball methods are named after the owners of these methodologies.</p>
<p>Both of these methods are in use nowadays. The main difference between these methods is that Inmon is top-down and Kimball is bottom-up. In this chapter, we will explain the Kimball method. Both of these books are must-read books for BI and DW professionals and are reference books that are recommended to be on the bookshelf of all BI teams.</p>
<p>This chapter is referenced from The Data Warehouse Toolkit , so for a detailed discussion, read the referenced book. To gain an understanding of data warehouse design and dimensional modeling, it’s better to learn about the components and terminologies of a DW.</p>
<p>A DW consists of Fact tables and dimensions. The relationship between a Fact table and dimensions are based on the foreign key and primary key the primary key of the dimension table is addressed in the fact table as the foreign key. Facts are numeric and additive values in the business process. For example, in the sales business, a fact can be a sales amount, discount amount, or quantity of items sold.</p>
<p>All of these measures or facts are numeric values and they are additive. Additive means that you can add values of some records together and it provides a meaning. For example, adding the sales amount for all records is the grand total of sales. Dimension tables are tables that contain descriptive information. Descriptive information, for example, can be a customer’s name, job title, company, and even geographical information of where the customer lives.</p>
<p>Each dimension table contains a list of columns, and the columns of the dimension table are called attributes. Each attribute contains some descriptive information, and attributes that are related to each other will be placed in a dimension. For example, the customer dimension would contain the attributes listed earlier. Each dimension has a primary key, which is called the surrogate key. The surrogate key is usually an auto increment integer value.</p>
<p>The primary key of the source system will be stored in the dimension table as the business key. The Fact table is a table that contains a list of related facts and measures with foreign keys pointing to surrogate keys of the dimension tables. Fact tables usually store a large number of records, and most of the data warehouse space is filled by them around 80 percent. Grain is one of the most important terminologies used to design a data warehouse.</p>
<p>Grain defines a level of detail that stores the Fact table. For example, you could build a data warehouse for sales in which Grain is the most detailed level of transactions in the retail shop, that is, one record per each transaction in the specific date and time for the customer and sales person. Understanding Grain is important because it defines which dimensions are required. There are two different schemas for creating a relationship between fact and dimensions: the snow flake and star schema.</p>
<p>In the start schema, a Fact table will be at the center as a hub, and dimensions will be connected to the fact through a single-level relationship.</p>
<p>There won’t be ideally a dimension that relates to the fact through another dimension. The following diagram shows the different schemas:.</p>
<p>The snow flake schema, as you can see in the preceding diagram, contains relationships of some dimensions through intermediate dimensions to the Fact table.</p>
<p>If you look more carefully at the snow flake schema, you may find it more similar to the normalized form, and the truth is that a fully snow flaked design of the fact and dimensions will be in the 3NF.</p>
<p>The snow flake schema requires more joins to respond to an analytical query, so it would respond slower. Hence, the star schema is the preferred design for the data warehouse.</p>
<p>It is obvious that you cannot build a complete star schema and sometimes you will be required to do a level of snow flaking. However, the best practice is to always avoid snow flaking as much as possible. After a quick definition of the most common terminologies in dimensional modeling, it’s now time to start designing a small data warehouse.</p>
<p>One of the best ways of learning a concept and method is to see how it will be applied to a sample question. Assume that you want to build a data warehouse for the sales part of a business that contains a chain of supermarkets; each supermarket sells a list of products to customers, and the transactional data is stored in an operational system.</p>
<p>Our mission is to build a data warehouse that is able to analyze the sales information. Before thinking about the design of the data warehouse, the very first question is what is the goal of designing a data warehouse? What kind of analytical reports would be required as the result of the BI system?</p>
<p>The answer to these questions is the first and also the most important step. This step not only clarifies the scope of the work but also provides you with the clue about the Grain. Defining the goal can also be called requirement analysis.</p>
<p>Your job as a data warehouse designer is to analyze required reports, KPIs, and dashboards. After requirement analysis, the dimensional modeling phases will start. Based on Kimball’s best practices, dimensional modeling can be done in the following four steps:. In our example, there is only one business process, that is, sales. Grain, as we’ve described earlier, is the level of detail that will be stored in the Fact table.</p>
<p>Based on the requirement, Grain is to have one record per sales transaction and date, per customer, per product, and per store. Once Grain is defined, it is easy to identify dimensions. Based on the Grain, the dimensions would be date, store, customer, and product. It is useful to name dimensions with a Dim prefix to identify them easily in the list of tables. The next step is to identify the Fact table, which would be a single Fact table named FactSales. This table will store the defined Grain.</p>
<p>After identifying the Fact and dimension tables, it’s time to go more in detail about each table and think about the attributes of the dimensions, and measures of the Fact table.</p>
<p>Next, we will get into the details of the Fact table and then into each dimension. There is only one Grain for this business process, and this means that one Fact table would be required.</p>
<p>To connect to each dimension, there would be a foreign key in the Fact table that points to the primary key of the dimension table. The table would also contain measures or facts. For the sales business process, facts that can be measured numeric and additive are SalesAmount, DiscountAmount, and QuantitySold. The Fact table would only contain relationships to other dimensions and measures. The following diagram shows some columns of the FactSales :.</p>
<p>As you can see, the preceding diagram shows a star schema. We will go through the dimensions in the next step to explore them more in detail. Fact tables usually don’t have too many columns because the number of measures and related tables won’t be that much. However, Fact tables will contain many records. The Fact table in our example will store one record per transaction. As the Fact table will contain millions of records, you should think about the design of this table carefully.</p>
<p>The String data types are not recommended in the Fact table because they won’t add any numeric or additive value to the table. The relationship between a Fact table and dimensions could also be based on the surrogate key of the dimension.</p>
<p>The best practice is to set a data type of surrogate keys as the integer; this will be cost-effective in terms of the required disk space in the Fact table because the integer data type takes only 4 bytes while the string data type is much more. Using an integer as a surrogate key also speeds up the join between a fact and a dimension because join and criteria will be based on the integer that operators works with, which is much faster than a string.</p>
<p>If you are thinking about adding comments in this made by a sales person to the sales transaction as another column of the Fact table, first think about the analysis that you want to do based on comments.</p>
<p>No one does analysis based on a free text field; if you wish to do an analysis on a free text, you can categorize the text values through the ETL process and build another dimension for that.</p>❿
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