Introduction to the Business intelligence platform of bi-learning (the problem of traditional relational database)

As early as the full business intelligence platform in SQL Server 2005, the NoSQL stuff was still at the conceptual stage, and it was not until 2009 that it became known that it was a leap forward. Of course, this is going to pull away, let's go straight to the focus of this article on the traditional relational database in the face of some problems.

• Limitations of relational databases
• SQL Server 2005–present complete Business intelligence platform

• Limitations of relational databases:

With the rapid development of information technology, data processing is not only more and more demanding in quantity, but also more and more high in quality, the database has undergone fundamental changes. This change has brought great challenge to database technology, the object of database management is no longer confined to simple data types such as text data, but it needs to describe and preserve a large number of multimedia unstructured complex data, as well as the relationship between data.
In addition, with the proliferation of popular site visits, the storage mechanism of the database itself, the use of a large number of concurrent users, the efficiency of storage space, as well as the integrity and security of data and other aspects of higher requirements. These are not the traditional relational database, the use of two-dimensional table simple structure can be satisfied.

Relational database is based on the representation of data as a simple two-dimensional model, which is represented as rows and columns of records for storage processing. Obviously, due to the constraints of the prevailing conditions, it is a kind of technology suitable for the simple data storage processing, which has the insurmountable limitation.
The inherent limitations of the relational database management system are shown in the following three areas:

1. Limitations on the data model
2. Performance Limitations
3. Limitations on scaling scalability

1. Limitations on the data model:

The two-dimensional table data model used in relational databases does not effectively handle multidimensional data that typically exists in most transactional applications. The inevitable result is that, in a complex way, the number of interaction tables increases dramatically and does not provide a good model for simulating real-world data relationships.
Because of the data model used by the relational database, the storage space can be increased and wasted, and the response performance of the system will be decreased continuously. Moreover, in real data, there are many types of relational databases that cannot be handled well.

2. Performance Limitations:

A relational database management system designed for static applications, such as report generation, does not have an optimization process for efficient transaction processing. The result is often some relational database products, in the GUI and the Web transaction process, did not achieve the desired effect. Unless more hardware investment is added, this does not fundamentally solve the problem.
With the two-D table data model of the relational database, the typical multidimensional data in most transactional applications can be processed, but the result is often the creation and use of a large number of data tables, which still makes it difficult to build a data model that simulates the real world. And when the data need to make the report output, but also the scattered set of a large number of two-dimensional data table, and then use the index technology to connect the table, can find all the required data, which will inevitably affect the application system response speed.

3. Limitations on Scaling scalability:

The ability of relational database technology to effectively support applications and data complexity is limited. The normalized design method based on the relational database has been unable to design and optimize the performance of the complex transaction database system. In addition, the high development and maintenance costs are also difficult for enterprises to bear.
In addition, the retrieval strategies of relational databases, such as composite indexes and concurrent locking techniques, can be used to create complexity and limitations.

• SQL Server 2005–present complete Business intelligence platform

1. SQL Server Analysis Services (SSAS)
2. SQL Server Integration Services (SSIS)
3. SQL Server Reporting Services (SSRS)

1.SQL Server Analysis Services (SSAS)

Microsoft SQL Server Analysis Services (SSAS) provides online analytical processing (OLAP) and data mining capabilities for business intelligence applications. Analysis Services allows you to design, create, and manage multidimensional structures that contain data aggregated from other data sources, such as relational databases, for OLAP support. For data mining applications, analysis Services allows you to design, create, and visualize data mining models that are constructed by using a variety of industry-standard data mining algorithms and based on other data sources.

Analysis Services (SSAS) embodies the structure

• The server components of analysis Services are implemented as Microsoft Windows Services. SQL Server Analysis Services supports multiple instances of the same computer, and each Analysis Services instance is implemented as a separate instance of Windows service.
• The client communicates with analysis Services using common standard XML for (XMLA), which is a SOAP-based protocol for issuing commands and receiving responses that are exposed as a Web service. In addition, the client object model is provided through XMLA (including managed providers (Adomd.net) and native OLE DB providers).

• Query commands can be emitted in the following ways:
  SQL;
  Multidimensional Expressions (MDX), an industry-standard query language for analysis, or data Mining Extensions (DMX), an industry-standard query language for data mining.
  You can also use the Analysis Services Scripting language (ASSL) to manage Analysis Services database objects. For more information, see Analysis Services Scripting Language (ASSL)

• Analysis Services also supports the local cube engine, which allows applications in disconnected clients to browse the stored multidimensional data locally.

2.SQL Server Integration Services (SSIS)

SQL Server Integration Services (SSIS) is a platform for building high-performance data integration solutions, including extract, transform, and load (ETL) packages for data warehouses.
Graphical tools and wizards for building and debugging packages, tasks for performing workflow functions such as FTP operations, executing SQL statements or sending e-mail, data sources and destinations for extracting and loading data, transformations for cleaning, aggregating, merging, and replicating data; for managing integration Service's Management Services Integration services, and application programming interfaces (APIs) for programming with the Integration Services object model.

Integration Services (SSIS) embodies the structure

Microsoft SQL Server Integration Services (SSIS)
Consists of four key components:
1. Integration Services
2. Integration Services Object Model
3. Integration Services Runtime
4. Runtime executable files and data flow tasks that encapsulate the data flow engine and data flow components.

3.SQL Server Reporting Services (SSRS)

SQL Server Reporting Services provides a web-enabled enterprise-level reporting feature that enables you to create reports that get content from multiple data sources, publish reports in different formats, and centrally manage security and subscriptions.

Reporting Services (SSRS) embodies the structure

SQL Server Reporting Services is a set of processing components, tools, and programming interfaces that enable development in a managed environment and use rich-format reports. The toolset includes deployment tools, configuration and management tools, and report viewing tools. Programming interfaces include simple Object Access Protocol (SOAP), URL endpoints, and Windows Management Instrumentation (WMI), which can be easily integrated with new or existing applications and portals.

Local cubes and local mining model support

Local cubes and local mining models allow the workstation to be parsed when the client workstation is disconnected from the network. Local cube engine (Msmdlocal.dll) supports local cubes and local mining models for clients. The local cube engine is an in-process COM server. The client application invokes the OLE DB for OLAP provider, which loads the local cube engine to create and query the local cube, as shown in.

Introduction to the Business intelligence platform of bi-learning (the problem of traditional relational database)