Start studying comp applications. Learn vocabulary, terms, and more with flashcards, games, and other study tools. Heres a short list of our Favourite Data Modelling Tools and here is an excellent discussion about Modelling Tools on LinkedIn, which makes PowerDesigner the most. Erd Diagram Software Open Source' title='Erd Diagram Software Open Source' />Understanding BI Components and Data. Introduction. In first article of this series, we discussed the fundamentals of Business Intelligence BI with various data warehouse design approaches. Erd Diagram Software Open Source' title='Erd Diagram Software Open Source' />In this article, well know more about BIs various components, architecture, data modeling, and its importance. Also, well know in detail about Extract E, Transform T, and Load L. BI is a platform or system that builds by combining multiple components together. These various components can form different technologies and tools. Before starting to develop any BI system, its important to know the various components in detail and solid architecture design for successful implementation. Microsoft Visio v z. VIZeeoh formerly Microsoft Office Visio is a diagramming and vector graphics application and is part of the Microsoft. Im looking for a tool that generates db diagrams. TerasLMA es una Plataforma de Formacin aeronutica que lleva ms de tres aos funcionando en Espaa, y tenemos destacada expericia en la preparacin de. Empower visual communication across your enterprise. Our powerful diagramming and project collaboration tools help teams work together more effectively. Components of Business Intelligence. An architect should be very careful before starting to design and develop any BI solution. Its equally important for an organization not only for decision making but also, at the same time, it demands a huge investment that may go badly if the right components and approaches are not considered or good design is not in place. A BI system is all about data first, identify the right data and sources second, store and analyze the data to provide desired output third, visualize the data as information using various ways such as reporting, online query, alerts, and so forth. To support all three stages, there are various options available in the industry. All major players who influence the industry trends have great exposure in BI technology and related tools. Some of them are IBM, Oracle, SAP, Microsoft, and many more. Also, there are a couple of open source suites available to design and develop BI solutions. To design a BI system, the selection of technology and tools is very important. You may choose either a complete suite from one technology like IBM, Oracle, or Microsoft, or you can go for hybrid tools where the data gathering tool can be part of a different technology suite from data analysis and data visualization. Sometime, the consideration of technology and tools is completely situational. For instance, if you are building an end to end BI system from scratch, you need to think about technical feasibility, the available skill set, and, most importantly, the licensing cost with support. If you are building a BI solution on top of a legacy system, you may think about hybrid design and use the best available licensed or open source tool compatible with the legacy system. In next section, well discuss how these different BI components participate and interact with each other when building the system. Architecture Design for a BI Solution. Architecture design plays a key role for any system implementation, but its more critical if you are working on BI solutions. As we talked, there are multiple components that interact with each other if there is any gap, we may not get the required information and meet the objective. If you see the following logical architecture of a BI solution referred from the first article, it has three major layers a Data Integration Layer, a Data Analysis Layer, and a Information Delivery Layer. These are detailed below. Data Integration Layer It collects data from various data sources using an ETL tool and stores the data in centralized data storage Database called Data Warehouse DW or Data Mart DM. In Architecture, we dont include the designing of DW or DM databases in detail, but consider design principles and patterns that are specialized parts of the DW systems, such as Source systems as staginglanding area, Data warehouse as backroom using normalized ERD, Data Mart as frontroom using dimension modeling, and an Analytical Cube with any one of the MRH OLAP methodologies. Ill discuss more about MRH OLAP in the next article. Data Analysis Layer Once data is integrated and becomes a single source of truth, other analytical tools OLAP Cube can consume this data and build aggregations to provide decision support information. Information Delivery or Data Distribution Layer This is the important layer in a BI system it gives a real insight of information and what exactly stakeholders want to see. It helps business users in decision making and more activities, such as exploring the information, slicing and dicing the data, sending alerts, and in predictive analysis. Any data visualization tool reporting tools, MS Excel can consume data from OLAP Cube or integrated data sources DW or DM, and offers an opportunity to understand the data and show the power of BI. Data Modeling. Data modeling is an integral part of BI system designing. There are three basic types of data models available Conceptual Data Model, Logical Data Model, and Physical Data Model. Conceptual Data Model This model represents the highest level of relationships between different entities. The audience of this data model is business owners, BA, and Architects. Logical Data Model It represents entities, attributes, and relationships involved in a business function and helps as the basis for the creation of the physical data model. Physical Data Model This represents an application and database specific implementation of a logical data model and defines database objects. Here is the comparative analysis Objects. Conceptual. Logical. Physical. Entity Name. XX Entity Relationship. XX Attributes Identification X Primary Key Relationship XXForeign Key Relationship XXDatabase Table Name  XTable Column Name  XTable Column Data Type  XIn all of the models above, data are designed for a different level of audiences. Availability of these models is important for any DW or DM design and development. Apart from these, database design of the DW and DM should be well defined. It completely depends on methodology that is being used for development, Inmon or Kimball, or any other. But fundamentally, Inmon supports the 3rd normal form, which can be represented by an Entity Relationship diagram and Kimball supports dimensional modeling, which is equally known as a star or snow flake design. Star or snow flake design is nothing but the representation of fact and dimension tables in a specific pattern. Ill discuss all these terminologies in detail in the next article, along with OLAP methodologies. Once a well defined database design is in place, the next step would be extracting and loading the data from different or required data sources. X Type Jaguar Manual Download more. ETL Extract, Transform, and Load. ETL is emerging terminology in the data world. It is used not only in BI implementation, but also at the same time is often used in all types of data migration or other data related activities, such as improving data quality, data profiling, and the like. ETL is a combination of three activities Data Extraction, Transformation including cleansing, and Loading. Data Extraction In this, data can be consumed from various sources and loaded on a staging area for further processing. It is important to know that do we need to pull complete available data from the source or partial data. In the ETL world, this type of processing is known as Full pull and Delta pull. If it is Full pull, every time we need to pull all available data from the source. This is time consuming and may present other limitations, but if it is delta pull, we need to design ETL in such a way that whenever it pulls data from a source, it pulls only modified and newly entered data from the source. This is highly recommended, but depends on data availability at the source.