Data Source

A Data Source is a system component that provides data sets (to support data-driven processes and data-driven systems).

Context:
- It can generate Data Content through data generation mechanisms.
- It can provide Data Access via access interfaces.
- It can maintain Data Format through format specifications.
- It can ensure Data Quality via quality controls.
- It can support Data Availability through delivery mechanisms.
- It can often include Data Schema with structural elements.
- It can often enable Data Discovery through metadata services.
- It can often support Data Integration via integration protocols.
- It can often maintain Data Freshness through update mechanisms.
- ...
- It can range from being a Batch Data Source to being a Real-Time Data Source, depending on its delivery mode.
- It can range from being a Static Data Source to being a Dynamic Data Source, depending on its update frequency.
- It can range from being a Simple Data Source to being a Complex Data Source, depending on its data structure.
- It can range from being a Raw Data Source to being a Processed Data Source, depending on its processing level.
- It can range from being a Single Data Source to being a Federated Data Source, depending on its distribution model.
- It can range from being a Structured Data Source to being an Unstructured Data Source, depending on its data format.
- It can range from being a Point Data Source to being a Data Flow Source, depending on its temporal continuity.
- ...
- It can integrate with Data Consumer for data consumption.
- It can connect to Data Pipeline for data flow.
- It can support Data Repository for data storage.
- ...
Example(s):
- Enterprise Data Sources, such as:
  - Market Data Systems, such as:
    - New York Stock Exchange Data Feed for financial trading.
    - Bloomberg Data Feed for market analysis.
  - Business Intelligence Sources, such as:
    - Sales Analytics Feed for performance tracking.
    - Customer Data Platform for behavior analysis.
- Research Data Sources, such as:
  - Academic Repositorys, such as:
    - UCI ML Repository for machine learning.
    - Real-World Dataset for empirical research.
  - Clinical Sources, such as:
    - Clinical Trial Data for medical research.
    - Healthcare Dataset for patient analysis.
- Technology Data Sources, such as:
  - Stream Systems, such as:
    - Real-Time Data Stream for live monitoring.
    - Event Data Feed for system tracking.
  - Sensor Networks, such as:
    - IoT Sensor Feed for device monitoring.
    - Environmental Sensor for condition tracking.
- Web Data Sources, such as:
  - Content Feeds, such as:
    - Web Feed for content syndication.
    - RSS Feed for information distribution.
  - E-Commerce Feeds, such as:
    - Product Data Feed for e-commerce.
    - Product Offer Data Feed for retail distribution.
- AI Training Sources, such as:
  - Model Training Datas, such as:
    - LLM Training Dataset for model training.
    - Multimodal Dataset for AI development.
  - Specialized Corpuses, such as:
    - DEFT Corpus for text analysis.
    - Wikipedia Data Feed for information extraction.
- Integration Sources, such as:
  - Enterprise Integrations, such as:
    - Integration Server System for data aggregation.
    - AWS Data Pipeline for data orchestration.
  - Data Lake Sources, such as:
    - Data Lake Feed for unified storage.
    - Data Warehouse Stream for analytical processing.
- Government Data Sources, such as:
  - Public Datasets, such as:
    - Census Data Feed for demographic analysis.
    - Open Government Data for public research.
  - Regulatory Feeds, such as:
    - Compliance Data Stream for regulatory reporting.
    - Policy Update Feed for governance tracking.
- Social Data Sources, such as:
  - Social Media Feeds, such as:
    - Social Network Stream for engagement analysis.
    - User Generated Content Feed for trend analysis.
  - Community Datas, such as:
    - Forum Data Stream for discussion analysis.
    - Review Platform Feed for sentiment tracking.
- Weather Data Sources, such as:
  - Meteorological Feeds, such as:
    - Weather Station Data for condition monitoring.
    - Satellite Data Feed for atmospheric analysis.
  - Climate Datas, such as:
    - Historical Climate Feed for trend analysis.
    - Forecast Data Stream for prediction modeling.
- ...
Counter-Example(s):
- Computation Resource, which processes rather than provides data.
- Data Sink, which consumes rather than provides data.
- Data Cache, which temporarily stores rather than originates data.
- Data Proxy, which forwards rather than generates data.
See: Data Provider, Data Origin, Data System, Data Collection Task, Real-Time Computing, E-Commerce, Internet Marketing.

References

2024

(Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Data_feed Retrieved:2024-12-23.
- Data feed is a mechanism for users to receive updated data from data sources. It is commonly used by real-time applications in point-to-point settings as well as on the World Wide Web. The latter is also called web feed. News feed is a popular form of web feed. RSS feed makes dissemination of blogs easy. Product feeds play increasingly important role in e-commerce and internet marketing, as well as news distribution, financial markets, and cybersecurity. Data feeds usually require structured data that include different labelled fields, such as "title" or "product".

2014

(Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/data_feed Retrieved:2014-2-10.
- Data feed is a mechanism for users to receive updated data from data sources. It is commonly used by real-time applications in point-to-point settings as well as on the World Wide Web. The latter is also called web feed. News feed is a popular form of web feed. RSS feed makes dissemination of blogs easy. Product feeds play increasingly important role in e-commerce and internet marketing. Data feeds usually requires structured data. At the present time unstructured data, e.g. HTML pages, dominate the Web. As a result, data feeds have the potential to make a bigger impact on the Web in the future.