Data Warehousing and Dimensional Modeling

Data Warehousing

<p>False (B)</p> Signup and view all the answers

<p>To store and organize data for direct querying by end users, report writers, and applications (C)</p> Signup and view all the answers

<p>Data staging is a crucial stage in the data warehouse process where raw data from multiple sources is cleansed, transformed, and prepared for loading into the data warehouse itself. This phase ensures data quality and consistency before it's used for analysis.</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Extracting data involves acquiring raw data from the source systems. This process encompasses steps like identifying and selecting the necessary data, evaluating its quality, and copying it into the staging area.</p> Signup and view all the answers

<p>Bit map indexes can significantly improve query performance in data warehouses, particularly when dealing with attributes that have a high number of distinct values. This allows for fast retrieval of data based on specific criteria.</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Materialized views improve query performance in a data warehouse environment by pre-computing the results of frequently executed queries, thereby reducing the time required to retrieve data.</p> Signup and view all the answers

<p>Aggregates data across multiple dimensions to provide a more summarized view (C)</p> Signup and view all the answers

<p>Slice operates on a single dimension, while dice operates on multiple dimensions (C)</p> Signup and view all the answers

<p>The StarNet Query Model aims to optimize data querying by providing a structured and efficient approach for navigating hierarchical dimensions and retrieving information relevant to user requirements.</p> Signup and view all the answers

<p>Snowflake schema (C)</p> Signup and view all the answers

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A subject-oriented, integrated, non-volatile, and time-variant collection of data designed to support management decisions.

Data warehouses integrate historical data from operational systems and external sources. They are updated spasmodically and are intensive on storage space and query time. They are typically used by decision-makers.

Data warehouses provide a multi-dimensional representation of data, ensuring data correctness and completeness for comprehensive understanding.

A data staging area is a storage area and processing pipeline that cleans, transforms, combines, duplicates, and prepares source data for use in the data warehouse.

A presentation server is the physical machine where the data warehouse data is organized and stored for direct querying by end-users, report writers, and applications. It utilizes either relational models with star schemas or dimensional models with data cubes.

Data Marts are logical subsets of the complete data warehouse, focusing on a specific business process, department, or location. They provide incremental development, restricted query basis, and performance improvements.

The process of extracting data from source systems, transforming it into a usable format, and loading it into the data warehouse.

Source systems capture the transactions of a business, such as operational systems and legacy systems.

The process of combining data from multiple sources based on matching key values or performing fuzzy matches on non-key attributes, including looking up textual equivalents of legacy codes.

The process of creating surrogate keys for dimension records to avoid dependence on legacy keys, ensuring referential integrity between dimension and fact tables.

Aggregate tables pre-computed summary data to enhance query performance and reduce querying complexity.

A data cube is a multidimensional representation of data, where each axis represents a dimension, and the cells contain the values for those dimensions.

Aggregation involves creating summary data at different levels of granularity, such as daily, weekly, monthly, or yearly, for specific dimensions.

The Star Schema is a data modeling approach for data warehouses, with a central fact table surrounded by multiple dimension tables connected via foreign keys.

The Snowflake Schema is a variation of the Star Schema, where dimension tables are normalized, creating a hierarchical structure with multiple levels.

Conformed Dimensions refer to the concept of having the same dimension table used consistently across multiple data marts, ensuring consistent data interpretation.

The Data Warehouse Bus Architecture focuses on building data marts with conformed dimensions and facts, allowing for seamless integration among data marts.

The Data Warehouse Business Process involves identifying a coherent set of business activities for analysis, such as order processing, to guide data warehouse design.

A slice represents a subset of the original data cube, viewing data for a single value along a specific dimension.

A dice represents a smaller subset of the data cube, focusing on a range of values along multiple dimensions.

Roll-up involves aggregating data to a higher level of granularity, moving from details to summaries.

Drill-down involves disaggregating data to a lower level of granularity, moving from summaries to details.

Bit Map Indexes are specialized indexes commonly used in data warehousing, representing data values as bitmaps, optimized for comparison, join, and aggregate operations.

View Materialization involves storing the pre-computed results of a view, improving query performance, and providing summaries and cross-tabs.

Codd's OLAP rules are a set of guidelines that define characteristics and functionalities of OLAP systems, focusing on multi-dimensional data, intuitive data management, client-server architecture, and various reporting features.

Kimball's List of Killer Queries identifies common and complex data warehouse query patterns, such as simple constraints, subqueries, correlated subqueries, behavioral queries, and classification queries.