Microsoft SQL Server Business Intelligence Development: Beginner’s Guide [Book] – What you’ll love about SQL Server 2017
SQL Server Express – Wikipedia.SQL Server Business Intelligence Development Studio (BIDS)
The following diagram shows a sample OLAP cube:. In the preceding diagram, the illustrated cube has three dimensions: Product , Customer , and Time. Each cell in the cube shows a junction of these three dimensions. Aggregated data can be fetched easily as well within the cube structure.
For example, the orange set of cells shows how much Mark paid on June 1 for all products. As you can see, the cube structure makes it easier and faster to access the required information.
Multidimensional modeling is based on the OLAP cube and is fitted with measures and dimensions, as you can see in the preceding diagram. The tabular model is based on a new In-memory engine for tables. The In-memory engine loads all data rows from tables into the memory and responds to queries directly from the memory. This is very fast in terms of the response time. The frontend of a BI system is data visualization. In other words, data visualization is a part of the BI system that users can see.
There are different methods for visualizing information, such as strategic and tactical dashboards, Key Performance Indicators KPIs , and detailed or consolidated reports. As you probably know, there are many reporting and visualizing tools on the market. Microsoft has provided a set of visualization tools to cover dashboards, KPIs, scorecards, and reports required in a BI application.
Excel is also a great slicing and dicing tool especially for power users. There are also components in Excel such as Power View, which are designed to build performance dashboards. Sometimes, you will need to embed reports and dashboards in your custom written application. Chapter 12 , Integrating Reports in Application , of this book explains that in detail.
Every organization has a part of its business that is common between different systems. That part of the data in the business can be managed and maintained as master data.
For example, an organization may receive customer information from an online web application form or from a retail store’s spreadsheets, or based on a web service provided by other vendors. Master Data Management MDM is the process of maintaining the single version of truth for master data entities through multiple systems. Even if one or more systems are able to change the master data, they can write back their changes into MDS through the staging architecture.
The quality of data is different in each operational system, especially when we deal with legacy systems or systems that have a high dependence on user inputs. As the BI system is based on data, the better the quality of data, the better the output of the BI solution. Because of this fact, working on data quality is one of the components of the BI systems. As an example, Auckland might be written as “Auck land” in some Excel files or be typed as “Aukland” by the user in the input form.
As a solution to improve the quality of data, Microsoft provided users with DQS. DQS works based on Knowledge Base domains, which means a Knowledge Base can be created for different domains, and the Knowledge Base will be maintained and improved by a data steward as time passes. There are also matching policies that can be used to apply standardization on the data. A data warehouse is a database built for analysis and reporting. In other words, a data warehouse is a database in which the only data entry point is through ETL, and its primary purpose is to cover reporting and data analysis requirements.
This definition clarifies that a data warehouse is not like other transactional databases that operational systems write data into. When there is no operational system that works directly with a data warehouse, and when the main purpose of this database is for reporting, then the design of the data warehouse will be different from that of transactional databases. If you recall from the database normalization concepts, the main purpose of normalization is to reduce the redundancy and dependency.
The following table shows customers’ data with their geographical information:. Let’s elaborate on this example. As you can see from the preceding list, the geographical information in the records is redundant. 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. 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.
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.
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. 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.
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. 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. 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. 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. 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. The primary key of the source system will be stored in the dimension table as the business key. Download Summary:. Total Size: 0. Back Next. Microsoft recommends you install a download manager. Microsoft Download Manager. Manage all your internet downloads with this easy-to-use manager.
It features a simple interface with many customizable options:. Download multiple files at one time Download large files quickly and reliably Suspend active downloads and resume downloads that have failed. Administrators can use the Add-in to create new model objects and load data without ever launching any administrative tools, helping to speed deployment. With the Master Data Services Add-in for Excel, all master data remains centrally managed in MDS, while the ability to read or update the data is distributed to those who need it.
The Master Data Services MDS Add-in for Microsoft Excel is a data management tool that delivers a multitude of master data management capabilities with ease and efficiency. Leverage existing Excel functionality to share your managed lists with others, knowing these lists can be secured and monitored with all of the features provided by Master Data Services.
Filename: X86 and x64 Package StreamInsight. To install the component, run the platform-specific installer for x86 or x64 respectively.
For more information see the Readme and the installation topic in the Help file. SSMA automates all aspects of migration including migration assessment analysis, schema and SQL statement conversion, data migration as well as migration testing.
Upgrade Advisor identifies feature and configuration changes that might affect your upgrade, and it provides links to documentation that describes each identified issue and how to resolve it.
Read the Installing Data Provider section of the product documentation, which is available on-line or as a download. This package is only available in a bit version. NET is a Microsoft. NET Framework object model that enables software developers to create client-side applications that browse metadata and query data stored in Microsoft SQL Server Analysis Services. NET Framework object model that enables software developers to create client-side applications to manage and administer Analysis Services objects.
Total Size: 0. Back Next. Microsoft recommends you install a download manager. Microsoft Download Manager.
Manage all your internet downloads with this easy-to-use manager. It features a simple interface with many customizable options:. Download multiple files at one time Download large files quickly and reliably Suspend active downloads and resume downloads that have failed. Connect to on-premises data—without moving the data to the cloud—and view your information in one place with Power BI.
Learn more about Power BI. Learn more about on-premises connectivity. Learn how to pin a report item to Power BI dashboard. Read the report.
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Developer Build, test and demonstrate applications in a non-production environment with this full-featured edition of SQL Server View all page feedback. In this article. The premium offering, SQL Server Enterprise edition delivers comprehensive high-end datacenter capabilities with blazing-fast performance, unlimited virtualization, and end-to-end business intelligence – enabling high service levels for mission-critical workloads and end-user access to data insights.
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SQL Server Database Engine includes the Database Engine, the core service for storing, processing, and securing data, replication, full-text search, tools for managing relational and XML data, in database analytics integration, and PolyBase integration for access to Hadoop and other heterogeneous data sources, and the Data Quality Services DQS server. Analysis Services includes the tools for creating and managing online analytical processing OLAP and data mining applications.
Reporting Services includes server and client components for creating, managing, and deploying tabular, matrix, graphical, and free-form reports. Reporting Services is also an extensible platform that you can use to develop report applications. Integration Services is a set of graphical tools and programmable objects for moving, copying, and transforming data. MDS can be configured to manage any domain products, customers, accounts and includes hierarchies, granular security, transactions, data versioning, and business rules, as well as an Add-in for Excel that can be used to manage data.
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