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Questions and Answers

Which statement about OLTP is true?

  • OLTP focuses on managing database modifications. (correct)
  • OLTP typically involves thousands of users.
  • OLTP is designed for long transactions.
  • OLTP supports complex queries and multidimensional views.

    • What distinguishes OLAP from OLTP?

  • OLAP is primarily used by clerks and IT professionals.
  • OLAP requires a normalized database structure.
  • OLAP is focused on data analysis and decision-making. (correct)
  • OLAP transactions are generally shorter than OLTP transactions.

    • In terms of user interaction, how do OLAP and OLTP systems differ?

  • OLTP is equipped for complex data analysis by knowledge workers.
  • OLTP allows for more sophisticated reporting capabilities.
  • OLAP systems are typically used by clerks handling transactions.
  • OLAP supports only a few hundred users at a time. (correct)

    • Which characteristic is associated with a data warehouse compared to a DBMS?

    <p>Data warehouse allows for multidimensional views.</p> Signup and view all the answers

    Which of the following is true regarding normalization in OLTP and OLAP?

    <p>OLAP systems are not subjected to normalization rules.</p> Signup and view all the answers

    What is the primary objective of a data warehouse?

    <p>Reporting and data analysis</p> Signup and view all the answers

    Which of the following processes is NOT part of the ETL framework?

    <p>Loading data into external databases</p> Signup and view all the answers

    The term 'OLAP' in the context of data warehouses refers to what?

    <p>Online Analytical Processing</p> Signup and view all the answers

    What distinguishes a star schema from a snowflake schema?

    <p>Complexity of dimension tables</p> Signup and view all the answers

    What does the integration layer do in a data warehouse architecture?

    <p>Integrate data from the staging layer</p> Signup and view all the answers

    Which of the following challenges does a data warehouse NOT generally face?

    <p>Data redundancy elimination</p> Signup and view all the answers

    What is the primary difference between the ETL and ELT processes?

    <p>ETL performs transformations before loading</p> Signup and view all the answers

    In a star schema, what does the fact table represent?

    <p>Key metrics and measures for analysis</p> Signup and view all the answers

    What does the central component of a star schema represent?

    <p>Transaction fact</p> Signup and view all the answers

    Which of the following is NOT a benefit of using a star schema?

    <p>Improved data integrity</p> Signup and view all the answers

    How does a snowflake schema differ from a star schema?

    <p>It has centralized fact tables connected to multiple dimensions</p> Signup and view all the answers

    Which of the following statements about OLAP is true?

    <p>OLAP allows for complex analytics queries</p> Signup and view all the answers

    What is a key disadvantage of using a snowflake schema?

    <p>It can lead to complex queries</p> Signup and view all the answers

    Which type of analytics is OLAP primarily designed for?

    <p>Complex quantitative analysis</p> Signup and view all the answers

    What is the primary focus of relational databases (OLTP)?

    <p>Insert and update operations</p> Signup and view all the answers

    Which of the following is a challenge faced when using OLAP cubes?

    <p>Poor API support for complex functions</p> Signup and view all the answers

    What is the purpose of the 'slice' operation in OLAP cubes?

    <p>To fix location and analyze other dimensions</p> Signup and view all the answers

    Which of the following best describes the 'dice' operation in cube processing?

    <p>Selecting specific values of multiple dimensions to create a sub-cube</p> Signup and view all the answers

    What does the 'roll-up' operation accomplish in OLAP?

    <p>Navigates from detailed data to summary data</p> Signup and view all the answers

    Which SQL query would effectively find the total sales for each product?

    <p>SELECT SUM(P.sales) FROM ProductSales AS P GROUP BY product;</p> Signup and view all the answers

    In data cube terminology, what does 'cell' refer to?

    <p>A single data point representing a measure</p> Signup and view all the answers

    Which operation best describes moving from summary sales data to individual product sales?

    <p>Drill Down</p> Signup and view all the answers

    What is the function of a dimension in the context of an OLAP cube?

    <p>To provide context for measures through categorization</p> Signup and view all the answers

    Which SQL query would retrieve the total sales for each city assuming city data is part of the sales dataset?

    <p>SELECT SUM(P.sales) FROM ProductSales AS P GROUP BY city;</p> Signup and view all the answers

    Study Notes

    INF2003: Database Systems - Data Warehouse

    • Course: INF2003 Database Systems, Data Warehouse
    • Semester: AY24/25 Trimester 1
    • Instructor: Zhang Wei
    • Date: Nov. 12, 2024

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    Data Warehouse

    • Definition: a central repository of integrated data
    • Orthogonal data dimensions:
      • Application dimension: data from multiple sources (sales, salaries, operations, HR)
      • Time dimension: includes historical and current data
    • Objective: reporting and data analysis, key component of business intelligence
    • Focus on OLAP (online analytical processing) with historical data instead of OLTP (online transaction processing)
    • Also known as enterprise data warehouse
    • Architecture (like a computer system):
      • Operational system (e.g., marketing, sales)
      • Integration layer
      • ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform)
      • Data warehouse

    Data Loading - ETL

    • ETL: extract, transform, load
      • Extract: data from different sources (operations, external)
      • Transform: data transformation (data type conversion, combining columns, data cleansing, masking)
      • Load: loading into data warehouse
    • Staging layer: stores raw data extracted from different sources
    • Integration layer: integrates data from the staging layer to another database (e.g., data warehouse)

    Conceptual Modeling - Schemas

    • Two popular schemas:
      • Star schema: simplest, most popular, 1 fact table + n referencing dimension tables in a star shape
      • Snowflake schema: more normalized, fact table + multiple related dimension tables
    • Fact table: center of star schema, contains business process data, facts (numeric values and foreign keys)
    • Dimension tables: branch points of star, storing descriptive attributes (e.g., time, geography, product, employee)

    Star Schema

    • Benefits: denormalized (relatively), simpler queries, simple join logic, good query performance (read intensive)
    • Disadvantages: data integrity is not enforced, often, data loading is highly controlled, not flexible， not suitable for complex analytics

    Snowflake Schema

    • ER diagram resembles a snowflake
    • Centralized fact tables connected to multiple dimensions
    • Dimensions are normalized into multiple related tables
    • Benefits: storage saving
    • Disadvantages: complex query, (alleviated over time), no end to normalization, may still be slow

    OLAP: Online Analytical Processing

    • Focus on OLTP (online transaction processing): transaction, atomic changes of state (e.g., banking, sales, trading)
    • Process small, quick, and interactive operations. Optimized for INSERT and UPDATE operations
    • Goals: availability, speed, concurrency, and recoverability for operations
    • Derived from OLTP but allows complex analytics queries
    • Deliberately de-normalized for high SELECT throughput
    • Applications: sales reports, marketing reports, budgeting, forecasting
    • Market shares (years ago): diverse, leading players (Microsoft, Oracle, IBM, SAP)
    • Core: OLAP cube

    OLAP Cube

    • Multi-dimensional array of data (or hyper-cube if the dimension is higher than 3)
    • Similar to a cross-tabbed report (from 1980's)
    • Easy for visualization
    • Pre-computation can support efficient query processing

    Cube Operations

    • Slice: (subset of a cube) - pick a rectangular sub-set of the cube
    • Dice: (sub-cube) -sub-cube with specific values in multiple dimensions
    • Drill Down: moving from summary data to detailed data
    • Roll Up: moving from detailed data to summary data
    • Pivot: rotate the cube to see different faces/perspectives

    OLTP vs. OLAP

    • OLTP: Online Transaction Processing
      • Basic tasks: Insert, update, delete information
      • Aims to maintain accuracy of data
      • Typical functions: banking, sales
      • Suitable for rapid data entry and data maintenance
    • OLAP: Online Analytical Processing
      • Focuses on analyzing data
      • Typical functions: generating reports, dashboards
      • Aims to identify patterns and trends

    DBMS vs Data Warehouse

    • DBMS (Database Management System)
      • Designed for OLTP (online transactional processing)
      • Optimized for insert, update, delete operations
      • Well-tuned for transaction management and concurrency control
    • Data Warehouse
      • Optimized for OLAP (online analytical processing)
      • Optimized for complex queries, summarization, multidimensional views
      • Often not normalized, but intended for complex analytics

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