Greenplum
Greenplum is an open-source, massively parallel processing (MPP) data platform for analytics, machine learning, and AI. It is built on PostgreSQL and designed for large-scale data warehousing and advanced analytics workloads, allowing for high-performance querying and processing of petabytes of data.
Core Information
#core_informationName, description, vendor website, logo, technology type and tags.
Overview & Role
Greenplum is an open-source, massively parallel processing (MPP) data platform for analytics, machine learning, and AI. It is built on PostgreSQL and designed for large-scale data warehousing and advanced analytics workloads, allowing for high-performance querying and processing of petabytes of data.
Domain Classification & Tags
Performance Profile
#performance_profileTypical read and write throughput, payload size and processing latency.
Read Performance Profile
Throughput: Varies widely, from 100s of MB/s to several GB/s per segment, aggregating to tens of GB/s for large clusters
Payload Size: Typically large, ranging from 100s of KB to several MB per row/record, or full table scans
Processing Latency: Sub-second for simple queries on indexed data, seconds to minutes for complex analytical queries on large datasets
Write Performance Profile
Throughput: Varies widely, from 10s of MB/s to 100s of MB/s per segment, aggregating to GB/s for large clusters during bulk loads
Payload Size: Typically large, ranging from 100s of KB to several MB per row/record, or bulk inserts/updates
Processing Latency: Seconds to minutes for large batch inserts/updates, sub-second for single row inserts (less common use case)
Architecture Diagram
#architecture_diagramReference diagram of the technology's internal architecture.
Features
#featuresCatalogued product capabilities and what each one does.
Empirical data for features is currently being compiled in the global catalog.
Typical Use Cases
#typical_use_casesScenarios the technology is commonly chosen for.
Empirical data for typical use cases is currently being compiled in the global catalog.
Known Customers
#known_customersPublicly referenced organisations using the technology.
Empirical data for known customers is currently being compiled in the global catalog.
Known Integrations
#known_integrationsOther products and services it is documented to work with.
Empirical data for known integrations is currently being compiled in the global catalog.
Connectors
#connectorsDirectional data connections to other technologies, with direction and maturity.
Empirical data for connectors is currently being compiled in the global catalog.
Reference Architectures
#reference_architecturesPublished architectures where the technology is used or mentioned.
No published reference architecture blueprints currently link to Greenplum.
Security Features
#security_featuresBuilt-in security and access-control capabilities.
Empirical data for security features is currently being compiled in the global catalog.
Known Issues
#known_issuesDocumented limitations, defects and operational pitfalls.
Empirical data for known issues is currently being compiled in the global catalog.
Guidelines
#guidelinesRecommended practices for adopting and operating the technology.
Empirical data for guidelines is currently being compiled in the global catalog.
Standards & Compliance
#standards_and_complianceStandards, certifications and control requirements it maps to.
Empirical data for standards & compliance is currently being compiled in the global catalog.
Sources
#sourcesDocumentation and research references behind the recorded information.
Empirical data for sources is currently being compiled in the global catalog.
Expert Validation
#expert_validationWhether domain experts reviewed and confirmed the content.
Empirical data for expert validation is currently being compiled in the global catalog.