dwh_tutorial

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DWH Data Transformation - Involves converting the data from legacy format to warehouse format. Data Loading - Involves sorting, summarizing, consolidating, checking integrity, and building indices and partitions. Refreshing - Involves updating from data sources to warehouse. Note: Data cleaning and data transformation are important steps in improving the quality of data and data mining results. 7
3. TERMINOLOGIES DWH In this chapter, we will discuss some of the most commonly used terms in data warehousing. Metadata Metadata is simply defined as data about data. The data that are used to represent other data is known as metadata. For example, the index of a book serves as a metadata for the contents in the book. In other words, we can say that metadata is the summarized data that leads us to the detailed data. In terms of data warehouse, we can define metadata as following: Metadata is a roadmap to data warehouse. Metadata in data warehouse defines the warehouse objects. Metadata acts as a directory. This directory helps the decision support system to locate the contents of a data warehouse. Metadata Repository Metadata repository is an integral part of a data warehouse system. It contains the following metadata: Business metadata - It contains the data ownership information, business definition, and changing policies. Operational metadata - It includes currency of data and data lineage. Currency of data refers to the data being active, archived, or purged. Lineage of data means history of data migrated and transformation applied on it. Data for mapping from operational environment to data warehouse - It metadata includes source databases and their contents, data extraction, data partition, cleaning, transformation rules, data refresh and purging rules. The algorithms for summarization - It includes dimension algorithms, data on granularity, aggregation, summarizing, etc. 8
Page 2 and 3: About the Tutorial A data warehouse
Page 4 and 5: IT Strategy .......................
Page 6 and 7: Categories of Metadata ............
Page 8 and 9: 1. OVERVIEW DWH The term "Data Ware
Page 10 and 11: DWH Data Warehouse Applications As
Page 12 and 13: 2. CONCEPTS DWH What is Data Wareho
Page 16 and 17: DWH Data Cube A data cube helps us
Page 18 and 19: DWH Data Mart Data marts contain a
Page 20 and 21: 4. DELIVERY PROCESS DWH A data ware
Page 22 and 23: DWH and medium-term, then we can de
Page 24 and 25: DWH The architecture is designed to
Page 26 and 27: DWH Controlling the Process Control
Page 28 and 29: 6. ARCHITECTURE DWH In this chapter
Page 30 and 31: DWH Enterprise Warehouse Virtual W
Page 32 and 33: DWH The transformations affects
Page 34 and 35: DWH Detailed Information Detailed i
Page 36 and 37: 7. OLAP DWH Online Analytical Proce
Page 38 and 39: DWH Roll-up is performed by cl
Page 40 and 41: DWH Here Slice is performed for t
Page 42 and 43: DWH OLAP vs OLTP Data Warehouse (OL
Page 44 and 45: 8. RELATIONAL OLAP DWH Relational O
Page 46 and 47: 9. MULTIDIMENSIONAL OLAP DWH Multid
Page 48 and 49: 10. SCHEMAS DWH Schema is a logical
Page 50 and 51: DWH The sales fact table is sam
Page 52 and 53: DWH define dimension location as (l
Page 54 and 55: DWH Horizontal Partitioning There a
Page 56 and 57: DWH Round Robin Partitions In the r
Page 58 and 59: DWH Note: While using vertical part
Page 60 and 61: DWH Role of Metadata Metadata has a
Page 62 and 63: DWH Challenges for Metadata Managem
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DWH As the merchant is not interest
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DWH The summaries are data marted i
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14. SYSTEM MANAGERS DWH System mana
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DWH Events Events are the actions t
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15. PROCESS MANAGERS DWH Process ma
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DWH Functions of Warehouse Manager
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16. SECURITY DWH The objective of a
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DWH But if each department accesses
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DWH These restrictions need to be c
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17. BACKUP DWH A data warehouse is
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DWH Connecting the tape drive as
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DWH Will it work in cluster a
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DWH It is also essential that th
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DWH Points to Note It is important
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DWH Test Schedule First of all, the
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DWH Testing the Application All th
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21. INTERVIEW QUESTIONS DWH Dear re
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DWH Q: Define a warehouse manager.
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