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Questions and Answers
What is a common misconception about data warehousing design?
What is the primary function of a data warehouse?
What is an example of politically naive behavior in data warehousing?
Which of the following should be avoided in the management of a data warehouse?
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Which category does not belong to the tools and techniques used in business analytics?
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What is a major benefit of real-time data warehousing?
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What are dashboards primarily used for in the context of business intelligence?
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What should organizations be cautious of when implementing a data warehouse?
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Which component is not part of business performance management?
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Which common mistake involves data loading practices?
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What type of analytics does the second generation of analytics primarily focus on?
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What does metadata in a data warehouse refer to?
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How do end users interact with a data warehouse?
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In the context of data warehousing, what should organizations focus on instead of periodic reporting?
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What is the primary function of a data warehouse?
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Which of the following best describes performance management processes?
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Which of the following best describes a dependent data mart?
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What is essential for defining success in a data warehouse project?
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What is a primary limitation of traditional analytics for end-users?
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Which characteristic indicates that a data warehouse contains historical data?
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What is the primary function of an Operational Data Store (ODS)?
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What does the term 'nonvolatile' mean in the context of data warehouses?
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Which area is NOT typically supported by an Enterprise Data Warehouse (EDW)?
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What are the categories of metadata mentioned for a data warehouse?
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What type of architecture do data warehouses utilize to facilitate user access?
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Which characteristic of data warehouses addresses issues with data from different sources?
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Which component is NOT part of the data warehousing process mentioned in the content?
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What does 'metadata' refer to in the context of data warehousing?
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What type of data mart is designed for a specific business unit not derived from a data warehouse?
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Which option accurately describes a key feature of a data warehouse when comparing it to traditional databases?
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Data in a data warehouse can be sourced from which of the following?
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Which of the following is NOT a characteristic of a data warehouse?
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What are the three major processes of data integration?
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Which of the following best describes the Extract, Transform, Load (ETL) process?
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What is a potential challenge associated with data transformation tools?
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In the ETL process, what does the 'Load' step involve?
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Why is the ETL process considered critical in data warehousing?
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Which technique is categorized under data integration processes?
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What does data federation enable in the context of data integration?
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What primarily governs the decision to purchase or build data transformation tools?
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Study Notes
Data Warehousing
- Data Warehousing (DW) is a pool of historical and current data.
- It supports decision making:
- It provides a data repository available to managers.
- Data is structured for analytical processing.
- Data Warehouse characteristics:
- Subject-oriented: Data is organized by subjects like sales or customers.
- Integrated: Data from different sources is normalized.
- Time variant: Data includes historical data measured over time.
- Nonvolatile: Data cannot be changed directly in DW, changes are tracked.
- Web-based: Typically used as a web-based application.
- Real-Time: Newer DWs provide real-time data access and analysis.
Data Warehouse Components
- Data Marts:
- A subset of a DW focusing on a single subject area.
- Dependent Data Marts: Created directly from a data warehouse.
- Independent Data Marts: Small DW for a specific business unit or department.
- Operational Data Stores (ODS):
- A recent customer information file (CIF).
- Used for short-term decisions in mission-critical applications.
- Enterprise Data Warehouses (EDW):
- Large-scale DW used across the enterprise.
- Used for various Decision Support Systems (DSS) such as:
- Customer Relationship Management (CRM)
- Supply Chain Management (SCM)
- Business Performance Management (BPM)
- Business Activity Monitoring (BAM)
- Product Lifecycle Management (PLM)
- Revenue Management
- Knowledge Management Systems (KMS)
- Metadata:
- Describes the contents of a data warehouse.
- Explains data acquisition and usage.
- Includes syntactic metadata, structure metadata and semantic metadata.
Data Warehousing Process
- Components of the data warehousing process:
- Data Sources: Data comes from operational legacy systems, external providers, OLTP or ERP systems, and web data.
- Data Extraction and Transformation: Extracting and transforming data from source to DW format for analysis.
Successful Data Warehouse Implementation
- Things to Avoid:
- Starting with the wrong sponsorship chain.
- Setting unrealistic expectations.
- Engaging in politically naive behavior.
- Loading data just because it's available.
- Believing that data warehousing database design is the same as transactional database design.
- Choosing a technology-oriented data warehouse manager.
- Focusing on traditional internal data, ignoring external data.
- Delivering data with confusing definitions.
- Believing promises of performance, capacity, and scalability.
- Believing that problems are over when the DW is up and running.
- Focusing on ad hoc data mining and periodic reporting instead of alerts.
Real-Time Data Warehousing
- Traditionally, DWs work with historical data for strategic and tactical decisions.
- Real-Time Data Warehousing (RDW) loads and provides data as they become available.
- Decision support has become operational.
- Real-time data warehouse (RDW), also known as active data warehouse (ADW).
### Data Integration
- Data integration comprises three major processes:
- Data access: accessing and extracting data from sources.
- Data federation: integrating views across multiple data stores.
- Change capture: identifying, capturing, and delivering changes made to data sources.
Data Integration Techniques
- Enterprise Application Integration (EAI):
- Service-Oriented Architecture (SOA):
- Enterprise Information Integration (EII):
- Extraction, Transformation, and Load (ETL):
ETL Process
- ETL is crucial for data warehousing.
- ETL process consumes 70 percent of the time.
- ETL consists of three steps:
- Extraction: Reading data from databases.
- Transformation: Converting extracted data to the appropriate format for the DW.
- Load: Putting the transformed data into the data warehouse.
- ETL tools are expensive and have a steep learning curve.
- Difficulty in evaluating the effectiveness of the IT organization until they learn the tools.
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Description
Dive into the core concepts of Data Warehousing including its characteristics and components. This quiz covers essential aspects like data marts, operational data stores, and the importance of structured data for analytical processing. Test your understanding of how Data Warehousing supports decision-making with historical and current data.