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Questions and Answers
What is the main characteristic of a data warehouse, according to W.H. Inmon?
Which technology is NOT mentioned as an example of a Data Warehouse in the provided text?
What does OLAP stand for in the context of Data Mining?
In the context of data warehousing, what does 'time-variant' mean?
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Which term describes the principle of organizing data in a data warehouse around major subjects like customer and product?
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What is the purpose of materializing a data cube in OLAP technology?
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Which type of OLAP server architecture uses a relational database management system (DBMS) to store and manage warehouse data, and middleware to support missing pieces?
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What is the key characteristic of Multidimensional OLAP (MOLAP) architecture?
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Which type of OLAP server architecture combines the benefits of both relational and multidimensional approaches?
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What is the key purpose of materialized views in a data warehouse?
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What is the main purpose of the data mart in the recommended multi-tier data warehouse development approach?
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What is the key difference between a data mart and a data warehouse?
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Which of the following is NOT a common operation performed in a data warehouse?
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What is the primary purpose of materialization in the context of data cubes?
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Which of the following operations is NOT typically performed during the data transformation step in a data warehouse?
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What is the primary advantage of using a data warehouse for data mining tasks?
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Which of the following is NOT a typical component of a data warehouse?
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In the context of OLAP, what is the purpose of a data cube?
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Study Notes
Data Warehousing and OLAP Technology for Data Mining
- A data warehouse is a decision support database that is maintained separately from the organization's operational database.
- It supports information processing by providing a solid platform of consolidated, historical data for analysis.
Characteristics of a Data Warehouse
- Subject-oriented: organized around major subjects, such as customer, product, sales.
- Integrated: combining data from multiple sources.
- Time-variant: data is time-stamped.
- Nonvolatile: data is not updated in real-time.
Examples of Data Warehouses
- Amazon Redshift
- Google BigQuery
- Snowflake
- Microsoft Azure Synapse Analytics
- IBM Netezza
- Oracle Exadata
- Teradata
- SAP BW/4HANA
Data Warehouse Development
- A recommended approach involves:
- Multi-Tier Data Warehouse
- Distributed Data Marts
- Data Mart
- Model refinement
- Enterprise Data Warehouse
- Model refinement
- Define a high-level corporate data model
OLAP Server Architectures
- Relational OLAP (ROLAP):
- Use relational or extended-relational DBMS to store and manage warehouse data.
- OLAP middle ware to support missing pieces.
- Multidimensional OLAP (MOLAP):
- Array-based multidimensional storage engine (sparse matrix techniques).
- Fast indexing to pre-computed summarized data.
- Hybrid OLAP (HOLAP):
- User flexibility, e.g., low level: relational, high-level: array.
- Specialized SQL servers:
- Specialized support for SQL queries over star/snowflake schemas.
Data Warehouse Components
- Operational meta-data:
- Data lineage (history of migrated data and transformation path).
- Currency of data (active, archived, or purged).
- Monitoring information (warehouse usage statistics, error reports, audit trails).
- Business data:
- Business terms and definitions.
- Ownership of data.
- Charging policies.
Data Warehouse Back-End Tools and Utilities
- Data extraction:
- Convert data from legacy or host format to warehouse format.
- Data cleaning:
- Detect errors in the data and rectify them when possible.
- Data transformation:
- Get data from multiple, heterogeneous, and external sources.
- Sort, summarize, consolidate, compute views, check integrity, and build indices and partitions.
- Refresh:
- Propagate the updates from the data sources to the warehouse.
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Description
Test your knowledge on concepts related to data warehouse development, including multi-tier data warehouse, distributed data marts, and data mart models. This quiz covers topics such as virtual warehouses, materialized views, and high-level corporate data modeling.
