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What does "DWH" stand for in the context of this document?
What does "DWH" stand for in the context of this document?
Data Warehousing
According to Bill Inmon, what is a data warehouse?
According to Bill Inmon, what is a data warehouse?
What are some of the key requirements for a data warehouse?
What are some of the key requirements for a data warehouse?
Data marts can be best described as a single design that encompasses all business processes.
Data marts can be best described as a single design that encompasses all business processes.
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In the context of a data warehouse architecture, what is usually the primary purpose of the "presentation server"?
In the context of a data warehouse architecture, what is usually the primary purpose of the "presentation server"?
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What is the significance of "data staging" within the data warehouse process?
What is the significance of "data staging" within the data warehouse process?
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Data aggregation is only relevant for presenting data in a single format.
Data aggregation is only relevant for presenting data in a single format.
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Describe the process of "extracting" data in the context of data staging.
Describe the process of "extracting" data in the context of data staging.
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What are the key benefits of employing "bit map indexes" in a data warehouse?
What are the key benefits of employing "bit map indexes" in a data warehouse?
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Materialized views are primarily designed for real-time updates.
Materialized views are primarily designed for real-time updates.
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What primary advantage do materialized views provide in a data warehouse environment?
What primary advantage do materialized views provide in a data warehouse environment?
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What does the OLAP operation "Roll-up" accomplish?
What does the OLAP operation "Roll-up" accomplish?
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What is the primary difference between the OLAP operation "slice" and "dice"?
What is the primary difference between the OLAP operation "slice" and "dice"?
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What is the primary purpose of the StarNet Query Model?
What is the primary purpose of the StarNet Query Model?
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Which of the following data modeling schemas is often characterized by its structure resembling a snowflake, with multiple levels of dimension tables branching out from the central fact table?
Which of the following data modeling schemas is often characterized by its structure resembling a snowflake, with multiple levels of dimension tables branching out from the central fact table?
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Study Notes
Data Warehousing and Dimensional Modeling
- Data warehousing is a subject-oriented, integrated, non-volatile, and time-variant collection of data used to support management decisions.
- A data warehouse integrates historical data from operational systems and external sources.
- Data is updated periodically, not continuously.
- Data warehousing requires extensive storage space and time for query processing.
- Decision-makers use data warehouses to support their decision-making processes.
- Examples include banking, insurance, manufacturing, and healthcare.
- Data warehousing is an ever changing environment, with user needs, business conditions, the nature of data, and technological progress all influencing its use.
Definition & Characteristics
- A data warehouse is a subject-oriented, integrated, non-volatile, and time-variant collection of data used to support management decisions
- Historical data from operational systems and external sources are integrated.
- Data is updated periodically, not continuously.
- Storage space and query time are significant factors.
- Data warehousing is used in decision-making processes.
Context and Requirements
- Contextual use of a data warehouse is ever-changing, influenced by user needs, business conditions, data nature, and technological advancement.
- Decision makers face issues with data requirements, including data accessibility, achieving consistent results from different roles, and the manipulation of data.
Data Volume vs Value
- Data volume increases from operational to decision support systems.
- Value in data increases with strategic knowledge, selected information, and primary data sources.
From Database Perspective
- Data warehouse activities focus on accessibility for decision-makers, integrating data, effective query definitions, conciseness (key business processes), multi-dimensional representation, maintaining data correctness, and incremental completeness.
Building DWH (Data Warehouse) Requirements
- Separating loading, tallying, and processing
- Ensuring scalability for hardware and software
- Adapting to business development
- Supporting new data sources, applications, and front-ends
- Considering data provenance, life-cycle, performance, loading, query, security, and administrative issues are all necessary aspects of building a data warehouse.
Building DWH Methodology
- Data warehouse development involves a high-level corporate data model, distributed data marts, model refinement, a multi-tiered data warehouse, and an enterprise data warehouse.
- Data staging areas, involve cleaning, transforming, combining, duplicate, and preparing source data for use in the warehouse.
Presentation Layer/Server
- The presentation layer/server is the physical machine where data is organized and stored for querying by users.
- Data models include relational (star schemas) and dimensional (data cubes). Relational is more common for data access and dimensional for better data access to users.
Data Marts
- Data marts are subsets of data warehouses, designed for specific business functions, departments, or locations.
- These subsets are usually more manageable and easier to implement than their parent-warehouse.
- Incremental development, and restricting queries are some of the benefits.
- Data marts make up a data warehouse in many cases.
Simple DWH Architecture
- This architecture involves a source layer for operational data, a middleware layer for data processing, a data warehouse layer for storage and a reporting/analysis layer for results.
Two-Phase DWH Architecture
- This architecture is a more comprehensive structure compared to the simple datawarehouse architecture including operational data, external data, ETL tools for transforming data, and finally data marts storing processed data.
Independent Data Marts DWH Architecture
- This method is more suitable for larger organizations with multiple data warehouse needs.
- Data is stored separately but integrated with metadata and reporting tools and analysis tools.
Hub & Spoke DWH Architecture
- Data is organized around a central hub (data warehouse) and connected by spokes (data marts), providing a flexible and adaptable system.
Basic Processes of the Data Warehouse
- Data extraction, involving reading and understanding source data.
- Data transformation involving cleaning (correcting errors, resolving conflicts), parsing, combining sources.
- Creating surrogate keys, building aggregates for performance improvements.
Data Cube for Dimensional Modeling
- Data cubes are 3-dimensional structures that support multi-dimensional analysis.
Data Cubes and Aggregations
- Data is aggregated in a cube structure based on relevant dimensions (product, time, etc.).
- Dimensions (e.g., Time, Stores, Product), give ways to access data, such as, month, year, product category.
Data Aggregations - Time Dimension Granularity
- Data can be aggregated by daily, weekly, monthly, or yearly timeframes to gain different insights into data warehousing
Basic Processes of the Data Warehouse
- Data warehousing processes include extracting, transforming, and loading (ETL) data into staging areas.
- Data is cleaned and transformed within the staging area.
- Data is loaded and indexed into the appropriate data marts.
- Data is checked and reviewed for quality assurance.
Indexes and Star Queries
- Indexes are critical to improve performance speed of querying.
- Suitable indexes (e.g., bitmap indexes) are implemented to handle specific operations (joins, queries)
View Materialisation
- View are generated to present data in an appropriate format for analysis.
- View materialisation is a technique to pre-calculate query results and store them in a materialized view
- This allows for faster query execution, improving performance.
OLAP Operations
- Operations on data cubes that allow users to view subsets of the data in various perspectives (e.g., Slicing, Dicing).
- Operations on data cubes that allow users to increase or decrease levels of summary (e.g., Drill-down, Roll-up).
OLAP - 2D
- The basic functionality that data analysis tools provide. Includes promotion, different products, different dates.
OLAP - Roll-up
- A useful analysis technique in which the aggregated result of multiple values is collected by dimensions and displayed in a spreadsheet-like format.
OLAP - Drill-down
- Useful approach in analysis to extract detailed information form aggregated data, such as data collected over a period of time.
OLAP - Filter
- Technique which removes data that does not meet criteria set by the user.
Codd's OLAP Rules
- Codd's OLAP rules define essential features for effective OLAP systems. Includes multidimensional view, intuitive data management, effective query accessibility, and appropriate analysis tools.
KPIs (Key Performance Indicators)
- KPIs, quantifiable metrics, are relevant for companies to see how well their projects, strategy, business (or other) are performing.
Kimball's List of Killer Queries
- These queries are complex and common queries that are likely to pose significant challenges in a data warehousing environment.
Dimensional Queries
- Dimensional queries are specific queries which deal with understanding the relationship between different elements/dimensions (product, time, marketing etc.).
Star Schema
- A common database model used in data warehousing. Facts and dimensions are stored in separate tables linked by primary and foreign keys. This supports fast query processing and analysis.
Snowflake Schema
- A more complex version of a star schema, designed to handle dimensions with rich relationships.
- Snowflake schema provides a more flexible data structure, enabling more complex queries and analysis.
Customer Relationship Lifecycle
- The model describes the changes in behavior for a company dealing with its customers over the years. This is commonly summarized through graph form.
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Description
Explore the key concepts and characteristics of data warehousing and dimensional modeling. This quiz covers the integration of historical data, its role in decision-making, and the evolving nature of data warehouses across various industries.
