DATA WAREHOUSE EGCO321 DATABASE SYSTEMS KANAT POOLSAWASD DEPARTMENT OF COMPUTER ENGINEERING MAHIDOL UNIVERSITY
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1 DATA WAREHOUSE EGCO321 DATABASE SYSTEMS KANAT POOLSAWASD DEPARTMENT OF COMPUTER ENGINEERING MAHIDOL UNIVERSITY
2 CHARACTERISTICS Data warehouse is a central repository for summarized and integrated data from operational databases and external data sources. The processing requirement of decision support applications have led to four distinguishing characteristics for data warehouses, as described in the following: Subject-Oriented Integrated Time-Variant Nonvolatile
3 COMPARISON OF OPERATIONAL DATABASES AND DATA WAREHOUSES
4 ARCHITECTURES FOR DATA WAREHOUSE Enterprise Data Model is a conceptual data model of the data warehouse defining the structure of the data warehouse and the metadata to access and transform operational databases and external data sources.
5 TWO-TIER DATA WAREHOUSE ARCHITECTURE
6 THREE-TIER DATA WAREHOUSE ARCHITECTURE
7 BOTTOM-UP DATA WAREHOUSE ARCHITECTURE
8 DATA MART Data mart is a subset or view of a data warehouse, typically at a department or functional level, that contains all data required for decision support tasks of that department.
9 APPLICATION OF DATA WAREHOUSES
10 MULTIDIMENSIONAL DATA The multidimensional data model supports data representation and operations specifically tailored for decision support processing in data warehouses.
11 RELATIONAL REPRESENTATION OF SALES DATA
12 MULTIDIMENSIONAL REPRESENTATION OF SALES DATA
13 A THREE-DIMENSIONAL DATA CUBE
14 MULTIDIMENSIONAL REPRESENTATION OF SALES DATA WITH ROW TOTALS
15 DATA CUBE Data cube is a multidimensional format in which cells contain numeric data called measures organised by subjects called dimensions. A data cube is sometimes known as a hypercube because conceptually it can have an unlimited number of dimensions.
16 MULTIDIMENSIONAL TERMINOLOGY Data cube or hypercube generalises the twodimensional and three-dimensional representations show in the previous page. A data cube consist of cells containing measures and dimensions to label or group numeric data. Each dimension contains values known as members. Dimension can have hierarchies composed of levels. Cells in a data cube contain measures such as the sales values.
17 TIME-SERIES DATA Time is one of the most common dimensions in a data warehouse and is useful for capturing trends, making forecasts, and so forth. The following list shows typical properties for a time series. Data Type Start Date Calendar Periodicity Conversion
18 DATA CUBE OPERATIONS (1) Slice Because a data cube can contain a large number of dimensions, uses often need to focus on a subset of the dimensions to gain insights. Dice Because individual dimensions can contain a large number of members, uses need to focus on a subset of members to gain insights.
19 DATA CUBE OPERATIONS (2)
20 DATA CUBE OPERATIONS (3) Drill-Down Uses often want to navigate among the levels of hierarchical dimensions. The drill-down operator allows uses to navigate from a more general level to a more specific level. Roll-Up Roll-up (also called drill-up) is the opposite of drill-down. Roll-up involves moving from a specific level to a more general level of a hierarchical dimension. Pivot The pivot operator supports rearrangement of the dimensions in a data cube.
21 EXAMPLE SLICE AND DICE OPERATION
22 DRILL-DOWN OPERATION FOR THE STATE OF UTAH
23 SUMMARY OF THE DATA CUBE OPERATIONS
24 RELATIONAL DATA MODELLING FOR MULTIDIMENSIONAL DATA (1) When using a relational database for a data warehouse, a new data modelling technique is needed to represent multidimensional data. A star schema is a data modelling representation of multidimensional data cubes. In a relational database, a star schema diagram looks like a star with one large central table, called the fact table, at the centre of the star that is linked to multiple dimensional tables in a radial manner.
25 RELATIONAL DATA MODELLING FOR MULTIDIMENSIONAL DATA (2) The fact table stores numeric data (facts), such as sales results, while the dimension tables store descriptive data corresponding to individual dimensions of data cube such as product, location, and time. There is a 1-M relationship from each dimension table to fact table.
26 ERD STAR SCHEMA EXAMPLE
27 CONSTELLATION SCHEMA Constellation schema is a data modelling representation for multidimensional databases. In a relational database, a constellation schema contains multiple fact tables in the centre related to dimension tables. Typically, the fact tables share some dimension tables.
28 ERD CONSTELLATION SCHEMA EXAMPLE
29 SNOWFLAKE SCHEMA Snowflake schema is a data modelling representation for multidimensional databases. In a relational database, a snowflake schema has multiple levels of dimension tables related to one or more fact tables. You should consider the snowflake schema instead of the star schema for small dimension tables that are not in 3NF.
30 ERD SNOWFLAKE SCHEMA EXAMPLE
31 STORAGE AND OPTIMISATION TECHNOLOGIES Several storage technologies have been developed to provide multidimensional data capabilities. The storage technologies support On Line Analytic Processing (OLAP), a generic name applied to decision support capabilities for data cubes.
32 ONLINE ANALYTICAL PROCESSING (OLAP) Advanced data analysis environment Supports decision making, business modelling, and operations research activities Characteristics of OLAP Use multidimensional data analysis techniques Provide advanced database support Provide easy-to-use end-user interfaces Support client/server architecture
33 OLAP CLIENT/SERVER ARCHITECTURE
34 OLAP SERVER ARRANGEMENT
35 OLAP SERVER WITH MULTIDIMENSIONAL DATA STORE ARRANGEMENT
36 OLAP SERVER WITH LOCAL MINI DATA MARTS
37 ROLAP ROLAP is a relational DBMS extensions to support multidimensional data. ROLAP engines support a variety of storage and optimisation techniques for summary data retrieval.
38 TYPICAL ROLAP CLIENT/SERVER ARCHITECTURE
39 M OLAP MOLAP is a storage engine that directly stores and manipulates data cubes. MOLAP engines generally offer the best query performance but place limits on the size of data cubes.
40 M OLAP CLIENT/SERVER ARCHITECTURE
41 HYBRID OLAP (HOLAP) HOLAP is a storage engine for data warehouse that combines ROLAP and MOLAP storage engines. HOLAP involves both relational and multidimensional data storage as well as combing data from both relational and multidimensional sources for data cube operators.
42 MAINTAINING A DATA WAREHOUSE Although data warehouse largely contain replicated data, maintaining a data warehouse is much more difficult than simply copying from data sources.
43 WORKFLOW FOR DW MAINTENANCE
44 ETL TOOLS To support complexity of data warehouse maintenance, software product known as Extraction, Transformation, and Loading (ETL) tools have been developed. ETL tools are software tools for extraction, transformation, and loading of change data from data sources to a data warehouse. ETL tools eliminate the need to write custom coding for many data warehouse maintenance tasks.
45 OVERVIEW OF THE DATA WAREHOUSE REFRESH PROCESS
46 DATA MINING Seeks to discover unknown data characteristics Automatically searches data for anomalies and relationships Data mining tools Analyse data Uncover problems or opportunities Form computer models based on findings Predict business behaviour with models Require minimal end-user intervention
47 EXTRACTION OF KNOWLEDGE FROM DATA
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