Podcast
Questions and Answers
What is a major challenge of using data lakes?
What is a major challenge of using data lakes?
- Requires predefined schemas
- Limited scalability
- High implementation costs
- Data quality and governance issues (correct)
Which of the following is a feature of cloud data warehousing?
Which of the following is a feature of cloud data warehousing?
- Requirement for on-premises infrastructure
- Fixed hardware requirements
- Limited data storage capabilities
- Dynamic scaling based on workload (correct)
What is an advantage of a data lakehouse compared to traditional data warehouses?
What is an advantage of a data lakehouse compared to traditional data warehouses?
- It allows for ACID transactions (correct)
- It eliminates data governance needs
- It stores only structured data
- It requires specialized hardware
Which of these is NOT a characteristic of traditional data warehousing architecture?
Which of these is NOT a characteristic of traditional data warehousing architecture?
What benefit does a data lake provide over traditional data warehouses?
What benefit does a data lake provide over traditional data warehouses?
What is a primary advantage of using data marts?
What is a primary advantage of using data marts?
Which of the following represents a challenge associated with data lakes?
Which of the following represents a challenge associated with data lakes?
What is a key feature of cloud data warehousing?
What is a key feature of cloud data warehousing?
Which aspect distinguishes federated data warehouses from traditional data warehouses?
Which aspect distinguishes federated data warehouses from traditional data warehouses?
What is a significant benefit of data lakehouses?
What is a significant benefit of data lakehouses?
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Study Notes
Data Warehousing
- Serves as a central repository for reporting and data analysis.
- Integrates data from multiple sources.
- Stores current and historical data.
- Used for creating analytical reports for business intelligence activities.
- Primary goal is to support decision making.
Data Warehouse
- A large, centralized repository that integrates data from various sources.
- Designed for complex queries, analytics, and reporting processes.
- Contains vast amounts of historical data, facilitating trend analysis, forecasting, and business intelligence.
- Key characteristics include:
- Subject-oriented: Organizes data around key business subjects, making it easier to access relevant information.
- Integrated: Combines data from different sources into a coherent format, ensuring consistency.
- Time-variant: Stores historical data to provide a time-based perspective for analysis.
- Non-volatile: Once data enters the warehouse, it is not frequently modified or deleted, preserving data integrity.
Data Marts
- A smaller, more focused subset of a data warehouse.
- Caters to the specific needs of a particular department or business unit.
- Designed for more specialized analytical processing.
- Types:
- Dependent Data Marts: Sourced directly from a centralized data warehouse.
- Independent Data Marts: Created without relying on a central data warehouse, gathering data from operational systems or external sources.
- Advantages:
- Quicker access to relevant data for a specific department.
- Easier and faster implementation compared to full-scale data warehouses.
- Disadvantages:
- Potential for data inconsistency if not integrated with a central warehouse.
- Limited scope of data, making cross-departmental analysis more challenging.
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