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building a data warehouse ppt

0000064394 00000 n H��WK�� ����c�����&o�%;���X������f��䚑�&�������j��l��Y��|�^�l{�E�w���]�i'��:�B��8M�7J����O���]�����M��:!� ���1���ȍ�N���)~C�S����}��������ʮB�Q��kcF�E�ti�wƍ�?�N���� u&�zm��N�u��=vW9���� c�I� �F%{�. 0000009665 00000 n What’s the difference between the Kimball and Inmon methodologies? 0000051085 00000 n xref 0000068053 00000 n 0000063658 00000 n Outline your existing operation. See our Privacy Policy and User Agreement for details. Particularly, three basic principles that helped us a lot when building our data warehouse architecture were: Build decoupled systems, i.e., when it comes to data warehousing don’t try to put all … 0000069921 00000 n 0000006573 00000 n We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. 0000065406 00000 n endstream endobj 1621 0 obj<> endobj 1623 0 obj<> endobj 1624 0 obj<>/Font<>/XObject<>/ProcSet[/PDF/Text/ImageC/ImageI]/ExtGState<>/Pattern<>>> endobj 1625 0 obj[/Indexed 1647 0 R 15 1696 0 R] endobj 1626 0 obj[/Indexed 1647 0 R 15 1697 0 R] endobj 1627 0 obj[/Indexed 1647 0 R 255 1698 0 R] endobj 1628 0 obj[/Indexed 1647 0 R 255 1699 0 R] endobj 1629 0 obj[/Indexed 1647 0 R 255 1700 0 R] endobj 1630 0 obj[/Indexed 1647 0 R 255 1701 0 R] endobj 1631 0 obj[/Indexed 1647 0 R 255 1702 0 R] endobj 1632 0 obj[/Indexed 1647 0 R 255 1703 0 R] endobj 1633 0 obj[/Indexed 1647 0 R 15 1704 0 R] endobj 1634 0 obj[/Indexed 1647 0 R 15 1705 0 R] endobj 1635 0 obj[/Indexed 1647 0 R 15 1706 0 R] endobj 1636 0 obj[/Indexed 1647 0 R 255 1707 0 R] endobj 1637 0 obj[/Indexed 1647 0 R 255 1708 0 R] endobj 1638 0 obj[/Indexed 1647 0 R 255 1709 0 R] endobj 1639 0 obj[/Indexed 1647 0 R 255 1710 0 R] endobj 1640 0 obj<> endobj 1641 0 obj<> endobj 1642 0 obj<> endobj 1643 0 obj<> endobj 1644 0 obj<> endobj 1645 0 obj<> endobj 1646 0 obj<> endobj 1647 0 obj[/ICCBased 1683 0 R] endobj 1648 0 obj[/Indexed 1647 0 R 255 1682 0 R] endobj 1649 0 obj[/Indexed 1647 0 R 255 1684 0 R] endobj 1650 0 obj[/Indexed 1647 0 R 15 1685 0 R] endobj 1651 0 obj[/Indexed 1647 0 R 15 1686 0 R] endobj 1652 0 obj[/Indexed 1647 0 R 15 1695 0 R] endobj 1653 0 obj[/Indexed 1647 0 R 15 1694 0 R] endobj 1654 0 obj[/Indexed 1647 0 R 255 1693 0 R] endobj 1655 0 obj[/Indexed 1647 0 R 255 1692 0 R] endobj 1656 0 obj[/Indexed 1647 0 R 255 1691 0 R] endobj 1657 0 obj[/Indexed 1647 0 R 255 1690 0 R] endobj 1658 0 obj[/Indexed 1647 0 R 255 1689 0 R] endobj 1659 0 obj[/Indexed 1647 0 R 15 1688 0 R] endobj 1660 0 obj[/Indexed 1647 0 R 15 1687 0 R] endobj 1661 0 obj<>stream 0000065574 00000 n Microsoft Metadata is data about data which defines the data warehouse. Fluff, but point is I bring real work experience to the session, http://www.ispot.tv/ad/7f64/directv-hang-gliding. see Ralph Kimball Books) and a better understanding of the architectures (see Building an Effective Data Warehouse … 0000006523 00000 n 0000006804 00000 n Started because a business user wanted to create a report using data from multiple systems and a quick an dirty ETL was created. Does the new Tabular Model in SQL Server 2012 change things? Data Warehouse Design, Build, and Implementation 1. 0000066392 00000 n In fact we have gone so far to say that the first and foremost critical success factor in the building of a data warehouse is to NOT build the data warehouse using the Big Bang approach. `g�#&�����������$�B����_:��|V�0E�kP}�y�J�[��P�2��$��B(S���_6?邬o|�>����-������?�I5柶R��ݮ�ɑd9c�z��[����$�����AT`Q�s��B��c�҆��ad��/{� m�wL,b� Ϝi/yui۱�6�( c��T5�тq���D Z��t��;�ֳD;䱥)�j��M1l 3ԧ֜�Ŀ�$a�*��'^y�;#(k�l���7�T��-܃���m� ��uZ�8@� ָSa� Question: How many people know what SSAS cubes are? James Serra, Big Data Evangelist 0000009615 00000 n this Report, that report… etc) and build a data Warehouse just to satisfy the reporting … This reference architecture implements an extract, load, and transform (ELT) pipeline that moves data from an on-premises SQL Server database into SQL Data Warehouse. In the Data Warehouse Architecture, meta-data plays an important role as it specifies the source, usage, values, and features of data warehouse data. When people say the use the Kimball model, most times they really mean they are using the Kimball Methodology and/or are using dimensional modeling. Clipping is a handy way to collect important slides you want to go back to later. Internal Data: In each organization, the client keeps their "private" spreadsheets, reports, customer profiles, and sometimes eve… Once you decide to build a data warehouse, the next step is deciding between a normalized versus dimensional approach for the storage of data in the data warehouse. A data warehouse is a central repository of information that can be analyzed to make more informed decisions. 0000067911 00000 n 0000071362 00000 n 0000069005 00000 n 2. 0000070939 00000 n If you continue browsing the site, you agree to the use of cookies on this website. Data Warehouse Information Center is a knowledge hub that provides educational resources related to data warehousing. 0000069174 00000 n In the world of computing, data warehouse is defined as a system that is used for data analysis and reporting. In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence. 0000064226 00000 n You’ll leave with information that will amaze your boss and lead to a big raise… – or at least lead you down the correct path to adding business value to your organization! Begin creating a strategic plan for your warehouse by looking at … They are then used to create analytical reports that can either be annual or quarterl… Data Warehousing: • Data Warehousing is a process of building the data warehouse and leveraging information gleaned from analysis of the data with the intent of discovering competitive enablers that can be employed throughout the enterprise. 0000067627 00000 n 1620 126 0000006423 00000 n 0000009169 00000 n The presentation … Based on the data requirements in the data warehouse, we choose segments of the data from the various operational modes. See our User Agreement and Privacy Policy. 0000065742 00000 n 0000068845 00000 n 0000009515 00000 n 0000054147 00000 n 0000065306 00000 n Building a database to support the decision making activities of a department or business unit DATA WAREHOUSING Data warehouse – a 0000063958 00000 n It is dedicated to enlightening data professionals and enthusiasts about the data warehousing … Does the new Tabular model in SQL Server 2012 change things? A data warehouse that is efficient, scalable and trusted. 0000038171 00000 n 0000051552 00000 n 0000006623 00000 n 0000064566 00000 n 0000006325 00000 n 0000005927 00000 n The dimensional approach, made popular by in Ralph Kimball ( website ), states that the data warehouse … 0000063474 00000 n 0000070473 00000 n Ralph didn’t invent the original basic concepts of facts and dimensions, however, he established an extensive portfolio of dimensional techniques and vocabulary, including conformed dimensions, slowly changing dimensions, junk dimensions, mini-dimensions, bridge tables, periodic and accumulating snapshot fact tables, and the list goes on. What if I have a ton of data? What is the best methodology to use when creating a data warehouse? Building data warehouses has become easier over the years due to improvements in the tools, improvements in the processes (i.e.

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