BI Evaluated
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Metabase

Self-service BI tool for querying and visualising databases.

Category / Domain BI
I/O Performance Empirical Profile

Core Information

#core_information

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

Overview & Role

Self-service BI tool for querying and visualising databases.

Domain Classification & Tags

BI

Performance Profile

#performance_profile

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

Read Performance Profile

Throughput: Highly variable, depends on underlying database and query complexity. Can range from 1-100s of queries/sec for simple dashboards to 0.1-1 query/sec for complex reports.

Payload Size: Highly variable, depends on query results. Can range from KB (e.g., single metric) to MBs (e.g., large tables, detailed reports).

Processing Latency: Highly variable, depends on underlying database and query complexity. Can range from 100s of ms (simple queries) to 10s of seconds or minutes (complex reports on large datasets). Metabase itself adds minimal overhead (tens to hundreds of milliseconds) to the database query time.

Write Performance Profile

Throughput: Not applicable in the traditional sense. Metabase is a read-only BI tool; it does not write data to the underlying databases. Its 'writes' are configuration changes, dashboard saves, or question definitions.

Payload Size: Not applicable. Configuration data is typically small (KB).

Processing Latency: Not applicable. Saving a dashboard or question is typically very fast, in the order of 100-500 ms, as it's just saving metadata to Metabase's internal application database.

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 Metabase.

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.