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Process of obtaining and collecting data from various sources and systems.
DESCRIPTION
Data acquisition encompasses capturing signals, files, events, and records from devices, applications, and external providers. In industrial contexts, it includes sampling physical signals and digitizing them; in data platforms, it spans APIs, CDC, batch imports, and streaming. Robust acquisition ensures reliability, latency targets, and data quality, feeding downstream processing and analytics. Common patterns include ingestion via message queues, connectors, and ETL/ELT pipelines, with schema and contract controls. Observability and retries maintain resilience.
ALTERNATIVE DEFINITIONS
Wikipedia
Data acquisition is the process of sampling signals that measure real-world physical conditions and converting the resulting samples into digital numeric values.
Focuses on signal sampling and digitization in instrumentation.
NI (National Instruments)
Data acquisition is the process of measuring an electrical or physical phenomenon such as voltage, current, temperature, pressure, or sound with a computer.
Highlights measurement of physical phenomena and computer-based acquisition.
RELATED TERMS
Extract-Transform-Load Streaming Batch Processing APIs Change Data Capture Queue Sensor Pipelines Data Integration
COMPETING TERMS
KNOWLEDGE AREAS
Data Engineering IoT & Edge Data Data Integration & Interoperability Data Management
TARGET AUDIENCE
Data Engineer Software Developer DevOps Engineer Data Architect Cloud Architect
EXTERNAL SOURCES
- Wikipedia - Data acquisition
- Confluent - Kafka Connect for scalable ingestion
- Microsoft - Azure patterns for data loading
TOP QUESTIONS
What are common patterns in Data Acquisition?
Batch file loads, streaming via message buses, API pulls, and CDC from databases are standard. Choice depends on latency, volume, and reliability needs.
Category: Overview
How is quality managed during Data Acquisition?
Use schemas, contracts, validation rules, and observability. Quarantine bad records, implement retries, and monitor SLAs on latency and completeness.
Category: Quality
What tools help with Data Acquisition in streaming systems?
Kafka Connect, Debezium (CDC), and cloud ingestion services simplify connectors and scaling. They ensure fault tolerance and offset management.
Category: Tools
What is the difference between Data Acquisition and Data Ingestion?
Acquisition focuses on capture from sources; ingestion encompasses transporting data into storage or processing systems. In practice, terms often overlap.
Category: Misunderstanding
What are risks in Data Acquisition?
Data loss, schema drift, and duplication are common. Security risks arise if credentials and endpoints are mishandled.
Category: Risks
How to handle schema changes during Data Acquisition?
Adopt schema registries, versioning, and backward-compatible changes. Use contracts and automated tests to detect and manage evolution.
Category: Best Practice
What is required to implement reliable Data Acquisition?
Secure connectivity, scalable pipelines, schema validation, idempotent processing, and monitoring. Clear SLAs and ownership close the loop.
Category: Requirements
How does IoT influence Data Acquisition?
Edge devices and gateways introduce protocols (MQTT, OPC UA) and constraints. Local buffering and filtering reduce bandwidth and latency.
Category: Domain
How to start building a Data Acquisition pipeline?
Identify sources and SLAs, choose protocols and connectors, design schemas, implement validation and monitoring, then iterate under load testing.
Category: Getting Started