Azure Data Factory — Technology Profile & Architecture

Core Information

#core_information

Name, description, vendor website, logo, technology type and tags.

Overview & Role

Azure Data Factory (ADF) is a cloud-based data integration service that allows you to create, schedule, and orchestrate your ETL (extract, transform, load) and ELT (extract, load, transform) workflows. It provides a code-free visual environment and supports a wide range of data sources and destinations, enabling data movement and transformation at scale.

Domain Classification & Tags

ETL

Performance Profile

#performance_profile

Typical read and write throughput, payload size and processing latency.

Read Performance Profile

Throughput: Highly variable, depends on source system and activity type (e.g., 100 MB/s to 1 GB/s for Data Flow, 10-100 MB/s for Copy Activity)

Payload Size: Highly variable, depends on source data volume and activity type (e.g., MBs to TBs per job)

Processing Latency: Highly variable, depends on source system, data volume, and activity complexity (e.g., minutes to hours for large jobs, seconds for small lookups)

Write Performance Profile

Throughput: Highly variable, depends on sink system and activity type (e.g., 100 MB/s to 1 GB/s for Data Flow, 10-100 MB/s for Copy Activity)

Payload Size: Highly variable, depends on sink data volume and activity type (e.g., MBs to TBs per job)

Processing Latency: Highly variable, depends on sink system, data volume, and activity complexity (e.g., minutes to hours for large jobs, seconds for small writes)

Architecture Diagram

#architecture_diagram

Reference diagram of the technology's internal architecture.

Component Architecture & Topology

open_in_new Standalone Tab

Features

#features

Catalogued product capabilities and what each one does.

Empirical data for features is currently being compiled in the global catalog.

Typical Use Cases

#typical_use_cases

Scenarios the technology is commonly chosen for.

Empirical data for typical use cases is currently being compiled in the global catalog.

Known Customers

#known_customers

Publicly referenced organisations using the technology.

Empirical data for known customers is currently being compiled in the global catalog.

Known Integrations

#known_integrations

Other products and services it is documented to work with.

Empirical data for known integrations is currently being compiled in the global catalog.

Connectors

#connectors

Directional data connections to other technologies, with direction and maturity.

Empirical data for connectors is currently being compiled in the global catalog.

Reference Architectures

#reference_architectures

Published architectures where the technology is used or mentioned.

No published reference architecture blueprints currently link to Azure Data Factory.

Security Features

#security_features

Built-in security and access-control capabilities.

Empirical data for security features is currently being compiled in the global catalog.

Known Issues

#known_issues

Documented limitations, defects and operational pitfalls.

Empirical data for known issues is currently being compiled in the global catalog.

Guidelines

#guidelines

Recommended practices for adopting and operating the technology.

Empirical data for guidelines is currently being compiled in the global catalog.

Standards & Compliance

#standards_and_compliance

Standards, certifications and control requirements it maps to.

Empirical data for standards & compliance is currently being compiled in the global catalog.

Sources

#sources

Documentation and research references behind the recorded information.

Empirical data for sources is currently being compiled in the global catalog.

Expert Validation

#expert_validation

Whether domain experts reviewed and confirmed the content.

Empirical data for expert validation is currently being compiled in the global catalog.