Run petabyte-scale analytics and AI workloads with Tanzu Greenplum massively parallel processing data warehouse. Greenplum unifies structured, semi-structured, unstructured, vector, and geospatial data on a single platform with in-database machine learning and vector search for RAG processing.
Best for
Organizations need to run analytics and AI workloads on petabyte-scale data without moving data between separate systems. Legacy data warehouses are expensive, proprietary, and lack modern AI capabilities like vector search and in-database machine learning.
Traditional data warehouses degrade as data volumes grow beyond terabytes. Query performance slows, ingestion bottlenecks emerge, and infrastructure costs escalate.
Greenplum's shared-nothing MPP architecture automates parallel processing of data and queries. Its cost-based query optimizer (GPORCA) executes complex joins at breakthrough performance on petabyte-scale data volumes.
Moving data between analytics platforms and ML systems adds latency, complexity, and cost. Data scientists spend more time on data engineering than model development.
Greenplum provides in-database ML through Apache MADlib, Python, R, Keras, and TensorFlow. Train, test, and deploy models in SQL without moving data out of the warehouse.
Proprietary data warehouse platforms create vendor lock-in, require specialized skills, and limit deployment flexibility. Migration to modern platforms becomes prohibitively complex.
Greenplum is built on open-source PostgreSQL with the same version and tools across all deployment targets: AWS, Azure, GCP, VCF private cloud, OpenStack, and bare metal.
Shared-nothing architecture distributes data and queries across all nodes in parallel. The GPORCA query optimizer is specifically designed for large-scale analytical workloads.
Scales interactive and batch-mode analytics to petabyte-scale datasets without degrading query performance or throughput.
Apache MADlib provides advanced algorithms including multi-layer perceptron and convolutional neural networks. Support for Python, R, Keras, and TensorFlow analytical libraries.
Train, test, and deploy models in SQL, reducing errors when putting models into production at scale.
pgvector support enables vector management for Retrieval-Augmented Generation (RAG) processing. Expanded text search supports both lexical and AI-powered semantic searches.
Organizations use Greenplum as a vector database alongside their analytics workloads, eliminating the need for a separate vector store.
The Platform Extension Framework (PXF) queries datasets across Amazon S3 object stores, HDFS, and other relational databases via JDBC without moving data.
Leverages PostgreSQL's Foreign Data Wrapper API to access remote data sources in parallel with query optimization for federated datasets.
Available on AWS, Azure, and GCP marketplaces with BYOL and hourly consumption models. Also deploys on VCF and OpenStack private clouds.
On-premises deployment through Dell Greenplum Reference Architecture or HP and Cisco certified configurations. Same version and tools across all targets.
Intelligent fault detection, fast online differential recovery, full and incremental backup, and disaster recovery. Security and authentication features address enterprise policy and regulatory requirements.
Supports B-tree, Hash, Bitmap, Block Range, text, geospatial, and AI vector index types for optimized data retrieval.
Data teams typically run analytics in one system and ML in another, creating data movement overhead, inconsistencies, and operational complexity. This slows time-to-insight and increases infrastructure costs.
Greenplum combines data warehouse, machine learning, deep learning, graph, text, and statistical methods in one scale-out MPP database. Data scientists work with Python, R, and SQL without extracting data to separate ML platforms.
Legacy enterprise data warehouses from proprietary vendors are expensive to operate and difficult to modernize. Organizations pay premium licensing for capabilities that open-source alternatives now provide.
Greenplum provides full data warehouse functionality with MPP performance on PostgreSQL. Organizations replatform legacy EDWs to reduce cost and complexity while gaining modern analytics and AI capabilities.
Organizations building AI applications with Retrieval-Augmented Generation need vector database capabilities. Running a separate vector store alongside the data warehouse adds infrastructure complexity and data synchronization challenges.
Greenplum's pgvector support handles vector management for RAG processing alongside analytics workloads. For IoT applications, Greenplum ingests and analyzes vast data streams with RabbitMQ integration and low-latency writes for real-time event processing.
Tanzu Greenplum is a massively parallel processing (MPP) data warehouse and analytics platform built on open-source PostgreSQL. It handles petabyte-scale data with in-database machine learning, vector database capabilities for RAG processing, geospatial analytics, text search, and GPU acceleration.
Organizations use it to unify structured, semi-structured, unstructured, vector, and geospatial data on a single platform for analytics and AI workloads.
Greenplum provides in-database ML through Apache MADlib with support for advanced algorithms including multi-layer perceptron and convolutional neural networks. It supports Python and R analytical libraries, Keras, and TensorFlow.
With pgvector support, Greenplum serves as a vector database for RAG (Retrieval-Augmented Generation) processing, enabling both lexical and AI-powered semantic searches alongside traditional analytics.
Greenplum deploys on AWS, Microsoft Azure, and Google Cloud with BYOL and hourly consumption models. It also runs on VMware Cloud Foundation and OpenStack private clouds.
On-premises deployment is available through Dell Greenplum Reference Architecture or HP and Cisco certified configurations. The same Greenplum version and tools work across all deployment targets for a consistent experience.
Yes. Organizations use Greenplum to replatform legacy enterprise data warehouses and replace expensive proprietary databases. Greenplum provides full data warehouse functionality with MPP performance at lower cost and complexity.
Its PostgreSQL foundation and broad ecosystem compatibility simplify migration from legacy platforms while adding modern capabilities like in-database ML and vector search that proprietary systems lack.
VirtualizationWorks helps organizations evaluate Tanzu Greenplum for analytics and AI workloads, plan migration from legacy data warehouses, and understand licensing options across cloud and on-premises deployments.
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