Data source
A data source is an origin, system, repository, or process from which data is obtained for use or processing.
Effective Data Analysis
A data source is a system, repository, or collection from which data used for analysis or visualization is obtained.
- How it is used
- Supplies internal or external information for analyses, stakeholder decisions, and visualizations.
- How deep it goes
- Develops practical choices about source selection, retrieval, refresh, and documentation rather than specifying one universal ingestion procedure.
- Examples
- Uses public demographic and survey data for a store-location decision and compares warehouse-ingestion options for business datasets.
- What you can do after
- Evaluate and select data sources, choose a suitable refresh method, and document retrieval for rerunnable analysis.
MongoDB 8.0 in Action, Third Edition
A source stage is a stream-processing stage that ingests records from an external data source such as a collection, Kafka topic, or document array.
- How it is used
- Starts an Atlas stream processor by ingesting documents from a selected connection or document array.
- How deep it goes
- Details source types, connection fields, timestamp mapping, and shell assembly; does not explain connection setup in the supplied examples.
- Examples
- Uses Kafka topics, MongoDB change streams, static sensor documents, and a solar stream persisted to an Atlas collection.
- What you can do after
- Operate/configure it by placing $source first, selecting an input, and previewing documents with `sp.process()` before adding downstream stages.
Read how MongoDB 8.0 in Action, Third Edition explains data source →
Cloud Observability in Action
A data source is a configured backend from which an application retrieves data for querying or visualization.
- How it is used
- Connects observability frontends to backends for querying, dashboards, and alerts.
- How deep it goes
- Explains configuration and workflows with Docker Compose examples, but defers backend-specific query implementation.
- Examples
- Uses Prometheus, Loki, Jaeger, CloudWatch, MySQL, Docker Compose, and Grafana provisioning files.
- What you can do after
- Operate/configure Grafana data sources and connect them to local telemetry backends for exploration and alerting.
Read how Cloud Observability in Action explains data source →
Pro Angular 16
A data source is a component that supplies application data independently of where that data originates.
- How it is used
- Supplies model data behind a repository so application features need not know whether products come from memory or HTTP.
- How deep it goes
- Works through in-memory and REST-backed implementations, repository wiring, provider configuration, and observable-to-signal conversion; defers fuller signal interoperability detail.
- Examples
- Uses in-memory product arrays, derived product categories, and a REST switch that fetches products and saves checkout orders.
- What you can do after
- Build it from scratch with an in-memory source, then configure repositories and providers to use a REST-backed source.
Machine Learning System Design
Data origin is the source, generating process, or provenance from which data is obtained.
- How it is used
- Identifies where dataset inputs and labels originate so source selection follows the ML system’s goals.
- How deep it goes
- Surveys source categories and design questions, then follows upstream failures and consistency risks without specifying a source-integration procedure.
- Examples
- Uses purchase histories, trades, sensor readings, synthetic data, and promotional offers fed by customer, clickstream, and call center records.
- What you can do after
- Evaluate/understand which internal and external sources a system needs, and recognize when upstream failures or changed origins threaten its data.
Read how Machine Learning System Design explains data source →
Delivering Machine Learning Projects: From design to deployment
A source system is an operational system that originates or stores data later used by another system or process.
- How it is used
- Frames data discovery, modeling, testing, and integration as activities requiring source documentation and access planning.
- How deep it goes
- Explains discovery artifacts, provenance, sampling, fusion risks, and iteration, but stops short of technical extraction procedures.
- Examples
- Uses samples, operational snapshots, surveys, experiments, observational data, and controlled test environments as source examples.
- What you can do after
- Apply it via a discovery ticket to assess availability, provenance, representativeness, constraints, and testing readiness.
Read how Delivering Machine Learning Projects: From design to deployment explains data source →
Essential TypeScript 5, Third Edition
A data source is a component or value from which an application obtains the data it displays or processes.
- How it is used
- Provides product and category data to a web application while allowing local test data to give way to a web service.
- How deep it goes
- Works through shared interfaces, asynchronous loading, display updates, and an Angular alternative; leaves Observable’s more complex sequence features unexplored.
- Examples
- Uses local product records, category-filtered HTML, Axios GET and POST requests, and an injectable Angular data source.
- What you can do after
- Build it from scratch with interchangeable local and remote loaders, or apply it via Angular dependency injection.
Read how Essential TypeScript 5, Third Edition explains data source →
Snowflake Data Engineering
A source system is an operational or external system from which data is extracted for downstream storage and processing.
- How it is used
- Anchors extraction, ingestion, and pipeline design by identifying where operational data originates and how downstream consumers will use it.
- How deep it goes
- Explains source classification, access preparation, incremental change detection, streams, and tooling choices, but stops short of source-specific implementation code.
- Examples
- Uses bakery information-system tables, CSV and JSON files, Snowflake Marketplace data, secure data sharing, APIs, and IoT streams as source examples.
- What you can do after
- Build extraction plans that select access methods, detect changes incrementally, unify multiple sources, and move data into Snowflake.
Kubernetes in Action, Second Edition
A data source is an origin object from which a newly provisioned PersistentVolume is initialized with data.
- How it is used
- Initializes dynamically provisioned PersistentVolumes with existing data, especially for ReadOnlyMany claims.
- How deep it goes
- Defines dataSourceRef and demonstrates claim cloning, while focusing on PersistentVolumeClaim sources rather than other source types.
- Examples
- Uses demo-read-write-once to initialize demo-read-only-many and populate reader pods with existing files.
- What you can do after
- Apply it via Kubernetes manifests to clone a claim and mount the initialized result read-only.
Read how Kubernetes in Action, Second Edition explains data source →
Azure Security
A data source is an application, service, or system that produces data available for collection and analysis.
- How it is used
- Uses security-relevant application, service, and system data to provide Sentinel with visibility for defined detection and investigation use cases.
- How deep it goes
- Defines source categories and three collection routes, then stops short of configuring an individual connector or building its ingestion logic.
- Examples
- Uses security alerts, network logs, endpoint telemetry, SaaS activity, threat intelligence, custom logs, and compromised collaboration identities.
- What you can do after
- Operate/configure Sentinel data collection by prioritizing security-value sources and checking the Data Connectors dashboard for supported integrations.
Data for All
A data source is a system, observation, activity, or other origin from which data is obtained.
- How it is used
- Explains how organizations combine data sources to analyze, model, predict, prescribe, simulate, and optimize decisions.
- How deep it goes
- Traces the shift from single-source aggregation to integrated multifaceted data, without detailing collection or integration procedures.
- Examples
- Uses location data, customer facts, macroeconomic indicators, and facility site-selection variables as concrete examples.
- What you can do after
- Evaluate/understand how combining data sources produces richer models, targeted actions, and possible conflicts of interest.
Digital Twins in Action
A data source is a system, device, person, or process that produces or supplies data.
- How it is used
- Supplies the information selected and integrated to make a digital twin useful for specific decisions.
- How deep it goes
- Develops a priority-and-effort selection procedure and outlines extraction and validation needs, without detailing an integration implementation.
- Examples
- Uses utility bills, meter photographs, building plans, weather services, and maintenance records in a home-source checklist.
- What you can do after
- Practice prioritizing available sources by decision value and incorporation effort before adding sensors or digitizing records.
The Art of AI Product Development
A data source is a repository, system, or stream from which information can be retrieved for analysis or processing.
- How it is used
- Supports product-roadmap analysis by supplying evidence for judgments, planning, and stakeholder communication.
- How deep it goes
- Distinguishes source semantics, structure, storage, access, and query methods, but stops short of connector implementation.
- Examples
- Uses sales calls, interviews, usage data, reviews, competitive-intelligence tools, databases, and web research.
- What you can do after
- Apply retrieval-augmented generation or Text2SQL according to source structure and choose connectors for repository type.
Read how The Art of AI Product Development explains data source →
Related concepts
- pipelineused by · A pipeline is an ordered or coordinated sequence of connected processing stages or tasks that passes data, work, or results toward an outcome.
- data integrationused by · Data integration is the process of combining data from multiple sources into a coherent form for consistent access, processing, or analysis.
- retrieval-augmented generationused by · Retrieval-augmented generation is a technique that retrieves relevant information from external sources and supplies it as context to a generative model when producing an output.
- connectorused by · A connector is a software element or mechanism that links components, applications, or resources to enable or represent their interaction, communication, or data exchange.
- request messageuses · An HTTP request is a message sent by a client to a server that specifies a method and target and may include headers and a body.
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