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The practice of combining data from multiple sources into a consistent, unified view for downstream consumption.
DESCRIPTION
Data integration brings disparate datasets together through ingestion, transformation, and harmonization so analysts and applications can rely on a consistent view. Common patterns include ETL, ELT, CDC, and API-based ingestion. Effective integration minimizes data silos, improves quality, and accelerates analytics. Modern architectures blend batch and streaming pipelines, with orchestration and metadata playing key roles. Governance and lineage ensure trust and compliance in integrated datasets.
ALTERNATIVE DEFINITIONS
IBM
Data integration is the process of combining data from multiple sources to provide a unified view.
Concise definition focusing on unification across sources.
Oracle
Data integration is the process of combining data from different sources to create a unified view for analytics and business operations.
Highlights both analytics and operational use cases.
RELATED TERMS
Extract-Transform-Load ELT Pipelines Data Ingestion Change Data Capture Data Replication Enterprise Service Bus API Integration
COMPETING TERMS
Data Virtualization Data Federation
KNOWLEDGE AREAS
Data Integration & Interoperability Data Engineering Data Management Data Architecture
TARGET AUDIENCE
Data Engineer Data Architect Enterprise Architect Data Steward Data Management Consultant
EXTERNAL SOURCES
- Wikipedia - Data integration
- IBM - Vendor overview and concepts
- Oracle - Oracle guide on data integration
- Microsoft - Azure data architecture guidance
- Apache NiFi - Open-source data flow tool docs
TOP QUESTIONS
Why is Data Integration important for analytics?
Data Integration eliminates silos and harmonizes data, enabling consistent metrics and faster insights. It creates a trusted foundation for BI, ML, and operations across the enterprise.
Category: Relevancy
How does Data Integration differ from Data Virtualization?
Data Integration physically moves and transforms data into target stores, while Data Virtualization provides a unified view without relocation. Choose based on latency, performance, and governance needs.
Category: Comparison
What patterns are common in Data Integration?
Common patterns include ETL, ELT, Change Data Capture, batch ingestion, streaming ingestion, and API-based data pulls. Many organizations run hybrid patterns to meet diverse requirements.
Category: Overview
What are typical risks in Data Integration projects?
Risks include poor data quality, brittle pipelines, uncontrolled schema changes, and security gaps. Clear contracts, testing, and governance mitigate these issues.
Category: Risks
How do you manage schema evolution in Data Integration?
Use schema registries, versioned contracts, backward-compatible changes, and automated validation. Proactive communication with producers and consumers reduces breaking changes.
Category: Management
When should organizations choose ELT over ETL for Data Integration?
ELT suits cloud warehouses/lakehouses where transformation can be pushed down for scalability. ETL fits when data must be cleansed before landing or when targets are constrained.
Category: Decision
What tools are commonly used for Data Integration?
Tools include Apache NiFi, Airflow, dbt, Fivetran, Azure Data Factory, AWS Glue, and Kafka for streaming. Choice depends on ecosystem and workload patterns.
Category: Tooling
How does Data Integration support governance and lineage?
Well-designed pipelines capture metadata, lineage, and quality metrics, enabling traceability and compliance. Catalogs and observability tools make controls auditable.
Category: Governance
What is a common misunderstanding about Data Integration?
Many assume integration is purely technical; in reality, aligning business definitions and metadata is equally critical for reliable outcomes.
Category: Misunderstanding