 Good morning, my dear students and Dr. Sachin R. Gengze, Professor and Head, Department of Electronics Engineering at Vulture Institute of Technology, Sonapur. Today, we are going to talk about Data Warehouse. The learning outcome of this session includes, at the end of this video, you will be able to compare database and Data Warehouse and can explain different features of Data Warehouse. We already seen what is a data and a database. The data is nothing but an elementary description of thing, event, activities and transactions that are recorded, classified and stored, but may not be in an organized way. Data can consist of numbers, it can consist of strings, alphabets, alphanumerical, images, audios, videos. So everything that is being stored as a description of the thing, event or activity is called as the data. Now, the database is, if we store the data in an organized way, then it is called as the database. A database is a collection of data that is organized so that it can be easily accessed, managed and updated, then it becomes a database. Another way of looking at the database is it's a collection of schemas, tables, queries, reports, views and the other objects. Now once we understand, and I think that we have spent last couple of lectures in understanding what is a data and database, now once we understand what is a data and database, let us go ahead and understand some new concept like a data warehouse and a data mart. As we know, the databases are optimized for extremely fast processing of the queries or ad hoc user request for specific data and these kind of data processing we are calling as an online transaction processing or OLTP, which consists of maybe updating certain data as and when happens, whenever there are certain transactions inside an organization, those transactions must be reflected, captured and rather reflected or made into the data in available in the database and all those kind of software systems are called as an OLTP or online transaction processing system. The databases are basically designed to support these OLTP. Now these databases need to strike a balance between transaction processing efficiency and the query efficiency, so there can be transactions where the data need to be updated or there can be select type of the queries where certain information need to be extracted from the database. Given this function, database cannot be optimized for a certain application like the data mining or complex online analytical processing system or the decision support and hence these limitations of the database which are being used for the OLTP but cannot satisfy or rather cannot support the systems like an OLAP or the decision support or the data mining, it leads to a different type of database, which is called as the data warehouse and data mark. So the database limitations which are used for the OLTP leads to another kind of system, they are called as the data warehouse and the data mark. The data warehouse and data mark are optimized, these are optimized, these are designed in a such a way that they support OLAP that is nothing but online analytical processing or data mining application or decision support application or even the business intelligence or BI applications. Database, what is the difference between database, data warehouse and data mark? Database stores data generated by the business applications, sensors and online transaction processing system and all this data when is being stored in a systematic way, organized way we are calling it as a database. Now what is a data warehouse? Data warehouse integrate data from multiple databases and data sources and organize them for a complex analysis or knowledge discovery and to support the decision making. So data from the various databases in an organization which are coming from the different sources are stored into a single system we can and that is being called as the data warehouse and what for this data warehouse is being used? See the database usually is used for the online transaction, the data warehouses are being used for analysis and knowledge discovery and to support the decision making. What is the data mark? Data mark is a small scale data warehouse that support only a single function or a department. We know that in a typical organization you have different department and different functional areas. Now the data warehouse store the data related to all this functional area or department. So you can see that data warehouse is a quite large entity. A part of that database or a particular area data can be stored or functional area data can be stored separately and that is called as the data mark. So you can see that this is a diagram of the data mark and data warehouse you can see that there are number of sources where from the data can come like there can be internal data external data or a personal data. This data is stored into being a data warehouse there is certain metadata the metadata is nothing but the data about the data and that the metadata can be generated. Now the sum of the part data of warehouse can go into the data mark. It depend upon the organizational structure. In sum of the organization there can be only data warehouse. In sum of the organization all those organization who cannot afford to have a big data warehouse they can have certain data mark of only important functional areas or there can be data warehouse and data mark together also it depend. So this is a generic diagram which shows that there can be data warehouse and data mark also. So all that data which is required for analysis processing is being stored into a data warehouse. Now there are what is the use of this data warehouse of the data mark. So that you can see over here the different applications like the OLAP that is an online analytical processing or EIS. Now the EIS stands for Executive Information System, the DSS stands for Decision Support System or the application like data mining they require large amount of data all that data and that data is available in this data warehouse. Now what is the output of this application? The output of this application can either be data visualization where the manager can see the different graphs or charts or tables then it can also give an information to the manager in order to take certain decision and then there can be certain knowledge and the management of the knowledge. And what are the application areas where this data warehouse can be used? It can be used for SCM, SCM stands for Supply Chain Management, CRM, CRM is Customer Relationship Management, E-commerce or taking certain statistical decision for all these kind of application the data warehouse which store a large amount of the data is being used by the application programs like the OLAP or EIS or DSS or the data mining. A data warehouse which is abbreviated as a DW or DWH also known as an Enterprise Data Warehouse because it stores the data related to the entire enterprise or the organization is the system used for reporting and data analysis and is considered as a core component of business intelligence environment required by the organization. These DWs are the central repositories, a central system of integrated data from one or more different sources. They store current and historical data there is not only the current data but also the historical data which is required for the analysis purpose is being stored into the data warehouse and are used for creating analytical report by the knowledge worker. In an organization we see that there are data workers and the knowledge worker and these analysis part or the report generation part is majorly being handled by the knowledge worker. So this data warehouse is used to this knowledge worker. In a data warehouse data is only read only and the data is being loaded into the data warehouse into a cyclic process called as the extract, transform and load. Unlike database where the data is continuously changing the data in data warehouse is non-volatile it means that it is not changing and we have seen that the data is in data warehouse is being used for the applications like an OLAP. Now example of the report that can be created out of the data warehouse could range from annual and quarterly comparison and trends to detailed daily sales analysis. If I compare database and data warehouse we know that the databases are designed for OLTP, data warehouse are designed for OLAP and data is extracted from the database and is loaded into the data warehouse. The data in database is read as well as the write data, the data in data warehouse read only data, the data in the data warehouse is updated using some periodical cycle. Database are optimized for storing the data while the data warehouse are optimized to respond to the analytical question. So please pause your video and answer the question what are what can be different application for the database and what can be different application for the data warehouse. So we can answer this question with certain example. I think now you have an answer of what are the possible application of database and what are the possible application of the data warehouse. Lastly we talk about what are the application areas of the data warehouse where this data warehouse can be used. It can be used for marketing and sales application. It can also be used for the sales performance application. It can be used by a typical organization for pricing and contract for forecasting. Maybe it's a forecasting of a cell or forecasting of some other aspect related to the business. For forecasting that also the data warehouse data can be used. Financial application the data warehouse is also proving to be an important asset and lastly we have seen that for customer relationship management also the data in a data warehouse can be used. So with that we come to the end of this session. I have used three references for this particular session information technology for management by Turban Volvino. The second book is information system by Ralph Stair and George Reynolds and lastly I have used a book called the information technology for management by Turban and Woods. So with that we come to the end of the session on the data warehouse. Thank you very much for joining me. Thanks.