Msbi by quontra us
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The document discusses key concepts related to databases, including types such as relational databases and the roles of ERP and OLTP systems. It explains data warehousing as a technique for integrating data from various sources to support decision-making and highlights the importance of OLAP for data analysis. Additionally, the document outlines the structural elements of data warehousing, including dimensions, facts, and data marts.
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A single, complete and
consistent store of data
obtained from a variety of
different sources made
available to end users in a
what they can understand
and use in a business
context.
[Barry Devlin]](https://image.slidesharecdn.com/msbibyquontraus-141028065800-conversion-gate02/85/Msbi-by-quontra-us-18-320.jpg)
Msbi by quontra us
- 1. Presented By Quontra SSoolluuttiioonnss Email : info@quontrasolutions.com Contact : 404-900-9988 Website : www.quontrasolutions.com
- 4. DataBase (DB) – A place where the collection of records will be maintained in a structured format so that It can be easily retrieved when ever required is known as a database. One of the most popularly used database model is the relational model. It was developed by Edgar Codd in 1969. Example : How do you think the Organizations store their employee and customer information? they store it in a database. where do you think the website maintains the login information about their users? they store it in a database.
- 5. ERP– ERP, which is an abbreviation for Enterprise Resource Planning, is principally an integration of business management practices and modern technology. ERP is a business tool that management uses to operate the business day-in and day-out. OLTP– OLTP, which is an abbreviation for Online Transaction processing, handle real time transactions which inherently have some special requirements. If your running a Bank, for instance, you need to ensure that as people withdrawing money from ATM’S they are properly and efficiently updating the database also those transactions are properly effecting to their Accounts.
- 6. 6 Data, Data everywhere yet ... • I can’t find the data I need – data is scattered over the network • I can’t get the data I need • need an expert to get the data • I can’t understand the data I found • available • I can’t use d tahtae pdoaotraly Id ofocuumnednted • results are unexpected • data needs to be transformed from one form to other
- 7. 7 What are the users saying... • Data should be integrated across the enterprise • Summary data has a real value to the organization • Historical data holds the key to understanding data over time • What-if capabilities are required
- 8. In What way I can Answer the above question with 8 my OLTP system... Is Data Warehousing is the Solution ?? YES Can I Improve my business using Data warehousing ?? YES.. How ??
- 9. 9 Data warehouse helps any Business in Many Which are our lowest/highest margin customers ? Which are our lowest/highest margin customers ? Who are my customers and what products are they buying? Who are my customers and what products are they buying? Which customers are most likely to go to the competition ? Which customers are most likely to go to the competition ? What impact will new products/services have on revenue and margins? What impact will new products/services have on revenue and margins? What is the most effective distribution channel? What is the most effective distribution channel? What product prom- -otions have the biggest impact on revenue? What product prom- -otions have the biggest impact on revenue? Ways Let’s say A producer wants to know….
- 10. DWH – (Data Warehousing) It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources. Raugh kimball – In simplest terms Data Warehouse can be defined as collection of Data marts. -Data marts : Subjective collection of Data. Bill Inmon – A data warehouse is a “subject-oriented, integrated, time variant and nonvolatile” collection of data in support of management’s decision-making process.”
- 11. OLAP – (Online Analytical Processing) The ability to analyze metrics in different dimensions such as time, geography, gender, product, etc. For example, sales for the company is up. What region is most responsible for this increase? Which store in this region is most responsible for the increase? What particular product category or categories contributed the most to the increase? Answering these types of questions in order means that you are performing an OLAP analysis. OLAP servers provides better performance for accessing multidimensional data. The most important mechanism in OLAP which allows it to achieve such performance is the use of aggregations. Aggregations are built from the fact table by changing the granularity on specific dimensions and aggregating up data along these dimensions. OLAP systems gives analytical capabilities that are not in SQL or are more difficult to obtain.
- 12. 1. OLTP (on-line transaction processing) 2. Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc. 1. OLAP (on-line analytical processing) 2. Data analysis and decision making 3. The tables are in the Normalized form. 3. The tables are in the De-Normalized form. 5. For Designing OLTP we used data modeling. 5. For Designing OLAP we used Dimension modeling. OLAP is classified into two i.e., MOLAP & ROLAP 4. We Called the Storage objects as Tables. i.e., All the masters and the Transactions are stored in the tables. 4. We Called the Storage objects as Dimension and Facts. i.e., All the masters Are dimension and the Transactions are Facts.
- 13. Product Prod_Id Prod_Nam e Base_Rate Cat_Id Category Cat_Id Cat_Name Cat_Desc Group_Id Group Group_Id Group_Name Group_Desc Product_Dim Prod_Id Prod_Name Base_Rate Cat_Name Cat_Desc Group_Name Group_Desc Topics Later We will Cover 1. Types of Dimensions 3. Hierarchies 2. Slowly changing Dimensions Normalized Tables De-Normalized Tables
- 14. SalesOrderDetails SalesOrder_Fact Cust_Id Cust_Id SalesPerson Prod_Id Prod_Id Order_Date Order_Date Delivery_Date Booked_Date Unit_Price Delivery_Date Qty Unit_Price Total_Amount Qty Tax Tax Created_By Qty*Unit_Price+Tax=Total Amount Reference keys of Dimensions Numeric fields called as Fact or measure Usually calculate all the calculations before storing into OLAP
- 15. Prod_Di m Prod_Id ……… Cust_Di m Cust_Id ……… Org_Dim Org_Id SalesOrder_F ……… act Cust_Id Prod_Id Order_Date Delivery_Date Org_Id Unit_Price Qty Total_Amount Tax Time_Di m Date Year Month ……… STAR Schema
- 16. Product_Di m Prod_Id Prod_Name Base_Rate Cat_Name Cat_Desc Group_Na me Group_Des c SalesOrder_Fact Cust_Id Prod_Id Order_Date Delivery_Date Unit_Price Qty Total_Amount Tax
- 17. 1. Dimensions will have only relation with the Fact. (Normalized model) 1. Dimension will have a relation other than Fact. (De- Normalized model) 2. One to many or One to One relation will Occur. 2. Used for many to many relation. 3. Performance is fast but required huge storage space. 3. Performance is Low but required Less storage space.
- 18. 18 A single, complete and consistent store of data obtained from a variety of different sources made available to end users in a what they can understand and use in a business context. [Barry Devlin]
- 19. 19 Data Warehousing -- It is a process • Technique for assembling and managing data from various sources for the purpose of answering business questions. Thus making decisions that were not previous possible • A decision support database maintained separately from the organization’s operational database
- 20. 20 Also Data Mining works with Warehouse Data Data Warehousing provides the Enterprise with a memory Data Mining provides the Enterprise with intelligence
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- 28. Data Storage Data-Migration Middleware (Populations-Tools) Operational Data Sources Repository Data Analysis Reporting, OLAP, Data Mining
- 29. Additional Benefit What happened? Number of Users Why did it happen? What will happen? What happened why and how?
- 30. Stage DB Optional O L A P ROLAP OLTP MOLAP SSIS CUBE Integration Services Analysis Services Reporting Services SSAS SSRS SSIS Data Marts
- 31. OLTP – Online Transaction Processing OLAP – Online Analytical Processing MOLAP – Multidimensional OLAP ROLAP – Relational OLAP HOLAP – Hybrid OALP Dimensions – De-normalized master tables Attributes – Columns of Dimensions Hierarchies – sequential order of attributes Facts (Measure group) – Transactions tables in DWH Fact (Measures) Cubes – Multidimensional storage of Data KPI’s – Key performance indicator Dashboards – combination of reports,kpis,charts Data Marts – Subjective Collection of Data SCD’s – Slowly changing Dimensions Perspectives – Child Cube