Greenplum
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| Greenplum | |
|---|---|
 Jgreenplum · CC BY-SA 3.0 · source | |
| Name | Greenplum |
| Developer | Pivotal Software; VMware; EMC; EMC Greenplum |
| Released | 2005 |
| Programming language | C, C++, SQL, Python |
| Operating system | Linux |
| License | Open source (Apache); commercial editions |
Greenplum Greenplum is a distributed, massively parallel processing (MPP) data warehouse platform designed for analytics at scale. It integrates a PostgreSQL-derived engine with shared-nothing architecture to support large-scale Amazon Web Services, Microsoft Azure, Google Cloud Platform, Dell EMC hardware and appliance ecosystems. Greenplum has been used alongside projects and products such as Hadoop, Apache Spark, PostgreSQL, Apache Kafka, and Tableau for enterprise analytics, reporting, and machine learning workloads.
History
Greenplum originated from technology developed by founders associated with Brighthouse, Pivotal Software, and earlier research at University of California, Berkeley clusters. It evolved through commercial development at Greenplum, Inc. and acquisition by EMC Corporation, later becoming part of Pivotal and then influenced by VMware strategies. Key historical milestones intersect with the rise of Amazon Redshift, Teradata, Vertica, and open source movements like Apache Hadoop and PostgreSQL enhancements. Industry events such as Strata Data Conference, SIGMOD, and partnerships with Cloudera and Hortonworks shaped ecosystem adoption and interoperability.
Architecture
Greenplum uses a shared-nothing MPP architecture influenced by designs from Ingres, Postgres-XL, and distributed databases used at Google and Yahoo!. The system comprises a master node, standby masters, and multiple segment instances running on commodity servers from vendors such as Dell, HP Enterprise, and Cisco. It employs a query planner and optimizer integrating techniques from Cost-based optimization research presented at VLDB and SIGMOD conferences. Data is distributed across segments using hash and range distribution strategies similar to approaches in Teradata and IBM Netezza. Networking and storage layers are often integrated with Infiniband, iSCSI, and NFS infrastructures common in Supercomputing centers.
Features
Greenplum delivers features inspired by PostgreSQL including SQL dialect support, extensibility with PL/Python, and user-defined functions used in projects like SciPy and NumPy. Analytical features include columnar storage options comparable to Amazon Redshift and Vertica, parallel query execution akin to Snowflake patterns, and integration with MADlib for in-database machine learning similar to TensorFlow integrations. It supports external table access via HDFS and connectors to Apache Kafka, JDBC and ODBC clients used by tools such as Power BI and QlikView. Security and governance align with standards from OAuth, LDAP, and policy frameworks discussed at IETF meetings.
Deployment and Editions
Greenplum is available in open source editions under the Apache License as well as commercial distributions tailored by Pivotal, VMware, and ecosystem partners. Deployments range from on-premises racks provided by Dell EMC appliances to cloud deployments on AWS Marketplace, Google Cloud Marketplace, and Microsoft Azure Marketplace. Containerized and orchestration deployments leverage Docker and Kubernetes platforms validated at KubeCon and supported by cloud-native tooling from HashiCorp and Red Hat. Packaging and provisioning often use automation tools like Ansible, Terraform, and Chef.
Use Cases and Performance
Greenplum has been applied to use cases in sectors served by organizations such as Bank of America, Verizon, Walmart, AT&T, and healthcare providers collaborating with Philips and Siemens. Typical workloads include large-scale ETL pipelines modeled after solutions at Facebook, real-time analytics competing with Apache Druid, and batch ML training akin to pipelines at Uber and Airbnb. Performance comparisons appear alongside benchmarks for TPC-DS and TPC-H published by vendors like Intel and research groups at Stanford University and MIT. Scaling strategies borrow from cloud elasticity patterns detailed by Netflix and Spotify engineering blogs.
Administration and Tools
Administration uses command-line tooling and GUIs integrating with enterprise platforms such as Cloudera Manager, Ambari, and VMware vSphere. Monitoring and observability are commonly implemented with Prometheus, Grafana, Nagios, and log aggregation via ELK Stack from Elastic NV. Backup and recovery practices align with standards from Veritas and cloud-native snapshots in AWS and Azure. Data ingestion and orchestration integrate with Apache NiFi, Airflow from Apache Software Foundation, and proprietary ETL tools from Informatica and Talend.
Community and Development
Greenplum development is driven by contributors from organizations including Pivotal, VMware, EMC Corporation, and independent developers collaborating through repositories and issue trackers similar to projects on GitHub and GitLab. Research collaborations intersect with academic groups at UC Berkeley, Carnegie Mellon University, and University of Washington and are discussed in venues like VLDB, SIGMOD, and ICDE. The project ecosystem includes extensions, connectors, and community plugins paralleling efforts seen in PostgreSQL extensions and Apache Hive UDF communities.