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About this document
- Pages
- 6 pages
- Price
- $6 one-time, yours to keep
- Membership
- Included in every plan
- Format
- Subject
- Business, Computer Science
- Module
- Week 4
This document contains multiple-choice questions on Big Data and databases. It covers key concepts such as types of Big Data, database management, and data warehousing. It is useful for exam preparation for Week 4.
What's inside
- Big Data concepts
- Types of Big Data
- Database management
- Data warehousing
- Data integration and governance
Who it is for: Students preparing for a Week 4 exam on Big Data and databases. (Undergraduate)
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Summary
Big data and databases are compared. Students get practice with true/false questions on the topics.
Big data definitions
Big data is defined. Students are asked to identify what is not a type of big data. NoSQL databases are compared with traditional relational databases.
Database basics
Key concepts in databases are explained. A/B experiments are described. The difference between data redundancy and data integrity is asked about.
Normalization and data warehouses
Normalization is explained. The difference between organizational databases and data warehouses is described. The ETL process is mentioned.
Data warehouse management
Data warehouse governance is discussed. The role of the IT department and business users is described. Tacit knowledge is defined.
Key terms
- Big data
- Large amounts of structured data in organizational databases.
- NoSQL databases
- Databases that can manage more different types of data than traditional relational databases.
- Normalization
- A process that typically improves processing performance.
- Data warehouse
- A repository of historical data organized by subject to support decision makers.
- ETL process
- Now typically called data integration.
- Tacit knowledge
- Knowledge gained from experience, such as insights on how to do well in a course.
Questions this document helps with
- What is a NoSQL database?
- How does normalization affect processing performance?
- What is the ETL process called now?
Summary written with AI from the document's text.
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