"Data warehouses are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise."
This is the process of collecting and storing data from various sources in a centralized database for analytical and reporting purposes.
Data Modeling: The process of designing a database structure to store and organize data efficiently.
ETL (Extract-Transform-Load): The process of extracting data from various sources such as databases, XML files, flat files, etc., transform it through data cleansing and restructuring, and then load it into the data warehouse.
Data integration: Integrating data from different sources into a single data warehouse for analysis.
Data Quality: The process of assessing the quality of data for an organization in terms of accuracy, completeness, consistency, and integrity.
OLAP (Online Analytical Processing): A computer-based approach for analyzing and presenting multidimensional data.
Data Mining: The process of discovering meaningful patterns and insights from large datasets.
Data Visualization: Using graphical representations to communicate complex data.
Business Intelligence: A set of tools or software that enables businesses to analyse data and make more informed decisions.
Data Architecture: The overall structure and design of the data warehouse, including data model design, mapping, and metadata management.
Dimensional Analysis: The process of grouping data into categories or dimensions for analysis.
Data Mart: A subset of the data warehouse containing data for a specific department or business unit.
Fact table: A table in data warehousing that contains transactional data, including measurements and facts.
Dimension tables: Tables that store information about the entities or dimensions being analysed in a data warehouse.
Data Governance: The processes put in place to ensure data quality, security, and compliance.
Data Warehouse Appliances: A preconfigured set of servers, storage, and software, designed to deliver high-performance data warehousing solutions.
Data Warehouse Design: The process of designing and optimizing data warehouses.
Master Data Management: The process of managing a company's most critical data to ensure consistency and accuracy across the organization.
Metadata Management: The process of managing data about data in a data warehouse.
Query Optimization: The process of defining and organizing data in a way that optimizes query performance.
Data Warehousing Protocols: A set of rules that governs the transmission and reception of data within a data warehouse environment.
Operational Data Store (ODS): An ODS is a type of data warehouse that collects and stores data from a variety of sources in real-time or near-real-time. It provides an integrated view of data to support business operations, such as customer service or supply chain management.
Enterprise Data Warehouse (EDW): An EDW is a centralized repository for all business data. It provides a complete view of an organization's data, from multiple sources for decision making and analysis.
Virtual Data Warehouse (VDW): A VDW is a logical data warehouse that leverages existing data sources without physically integrating them into a single database. It provides a centralized view of the data without the need for data replication.
Cloud Data Warehouse: A Cloud Data Warehouse is a data warehouse that is hosted in the cloud. It provides a flexible and scalable solution to store and analyze data.
Analytical Data Store: An Analytical Data Store is a variation of an ODS that has been optimized for analytical performance by structuring the data in a particular way.
Data Mart: A Data Mart is a subset of an EDW or ODS that is designed to serve a specific business unit or department's reporting and analysis needs. It provides a highly focused view of the data.
Data Lakes: A Data Lake is a repository that allows for the storage of structured and unstructured data at scale. It enables businesses to store large amounts of data in its native format without the need for pre-defined structures.
Operational Intelligence Warehouses (OIW): An OIW is a data warehouse that is designed to analyze and visualize real-time data from operational systems such as CRM or Customer Experience Management (CEM).
Real-Time Data Warehouses: Real-time data warehouses allow organizations to store and analyze data as it is generated, providing insights and analysis in real-time.
Federated Data Warehouse: A Federated data warehouse is a distributed data warehouse that aggregates data from multiple sources while preserving the integrity of each source.
Personal Data Warehouse: A personal data warehouse is a data warehouse that is created by an individual to manage and analyze their personal data, such as health records or financial records.
Hybrid Data Warehouse: A Hybrid Data Warehouse is a combination of on-premise and cloud-based data warehousing solutions. It leverages the strengths of both approaches to deliver a more flexible and scalable solution.
In-Memory Data Warehouse: An In-Memory Data Warehouse is a type of data warehouse that stores data in memory rather than on disk. This approach can deliver faster query performance, but requires a large amount of memory.
Reference Data Warehouse: A Reference Data Warehouse is a repository of data that serves as a reference for other data sources. It provides a consistent and accurate view of data across an organization.
Open-Source Data Warehouse: An open-source data warehouse is a data warehouse solution that is developed and maintained by a community of contributors rather than a single vendor. It provides an alternative to proprietary data warehousing solutions.
"It is considered a core component of business intelligence."
"This is beneficial for companies as it enables them to interrogate and draw insights from their data and make decisions."
"Data warehouses store integrated data from one or more disparate sources."
"The data stored in the warehouse is uploaded from the operational systems."
"The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the data warehouse for reporting."
"Extract, transform, load (ETL) and extract, load, transform (ELT) are the two main approaches used to build a data warehouse system."
"A data warehouse is used for reporting and data analysis."
"They store current and historical data in one single place."
"Creating analytical reports for workers throughout the enterprise."
"Also known as an enterprise data warehouse (EDW)."
"The data may pass through an operational data store."
"To ensure data quality before it is used in the data warehouse for reporting."
"Extract, transform, load (ETL) is one of the main approaches used to build a data warehouse system."
"Extract, load, transform (ELT) is one of the main approaches used to build a data warehouse system."
"Enables them to interrogate and draw insights from their data and make decisions."
"The data stored in the warehouse is uploaded from the operational systems."
"They store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise."
"Enables them to interrogate and draw insights from their data and make decisions."
"Data warehouses are central repositories of integrated data from one or more disparate sources." Note: Please make sure to rephrase the questions in your actual study or assignment to avoid direct copy-pasting.