EDB Postgres AI for WarehousePG: Reclaiming control of the enterprise data warehouse

Proprietary warehouses delivered scale — but at the cost of control, predictable pricing, and real flexibility. Enterprises are doing the math.

Partner Content For many enterprises, the data warehouse has shifted from strategic asset to operational liability. Decades-old proprietary platforms such as Teradata, alongside cloud-only services including Snowflake, have delivered scale and performance. But they have done so at the cost of vendor lock-in, unpredictable pricing, and limited architectural flexibility.

As regulatory pressure increases and AI-driven analytics become core to competitiveness, organizations are reassessing whether their warehouse platforms truly serve long-term business goals.

EDB Postgres® AI (EDB PG AI) addresses these challenges with WarehousePG, an open source, petabyte-scale data warehouse designed to restore control, predictability, and data sovereignty without sacrificing performance. Built on Postgres and engineered for massively parallel analytics, WarehousePG provides a modern escape hatch from restrictive systems while delivering up to 58% lower total cost of ownership (TCO).

Open source, petabyte-scale analytics with Postgres at the core

Enterprise data warehouses are being pushed beyond their original design assumptions. Petabyte-scale datasets, hybrid deployment requirements, sovereign data mandates, and AI-driven analytics now coexist in production environments that demand both extreme performance and architectural flexibility.

Traditional proprietary platforms and cloud-only warehouses struggle to meet these requirements simultaneously, forcing organizations into trade-offs between cost, control, and capability.

EDB Postgres AI for WarehousePG addresses this gap by delivering a fully open source, petabyte-scale data warehouse built on Postgres, engineered for high-performance analytics, in-database AI, and deployment flexibility across on-premises, cloud, and hybrid environments.

Architecture: Postgres-based MPP at scale

Its massively parallel processing (MPP) architecture enables WarehousePG to scale out across hundreds of nodes. Rather than relying on a single-server scale-up model, WarehousePG distributes both data and query execution across multiple segment nodes, coordinated by a central coordinator node.

The coordinator is responsible for query parsing, optimization, and execution planning. Once a query plan is generated, work is distributed to the segments, which operate in parallel on their local data partitions. This approach allows WarehousePG to efficiently execute complex analytical queries—large joins, aggregations, window functions, and transformations—across petabyte-scale datasets.

This architecture removes the inherent bottlenecks of monolithic databases while retaining full SQL compatibility with Postgres, significantly reducing the learning curve for existing data teams.

Predictable performance without proprietary constraints

Unlike cloud-native warehouses that rely on consumption-based pricing and opaque resource management, WarehousePG provides deterministic workload behavior and predictable performance. Resource allocation and query execution are explicitly controlled within the cluster, enabling consistent response times even under mixed analytical workloads.

Because WarehousePG is Apache 2.0–licensed and built on open source Postgres, enterprises avoid proprietary storage formats and vendor-controlled execution engines. Data remains fully accessible, portable, and deployable anywhere the organization requires—on premises for regulatory compliance, in public cloud for elasticity, or in hybrid configurations for cost optimization.

This architectural independence and EDB's core-based pricing enable up to 58% TCO reduction, particularly for organizations migrating from high-cost proprietary platforms or unpredictable cloud warehouses.

Hybrid storage and SQL access to data lakes

Modern analytical environments increasingly span multiple storage tiers. WarehousePG addresses this through the Platform Extension Framework (PXF), which enables direct SQL access to external data stored in object stores and distributed file systems, including Amazon S3 and Hadoop Distributed File System (HDFS).

With PXF, data engineers can query formats such as Parquet, AVRO, JSON, and CSV without copying data into the warehouse. This significantly reduces ETL complexity and storage duplication while enabling a hybrid "warm and cold data" strategy. Frequently accessed datasets remain in WarehousePG's high-performance storage, while infrequently accessed data resides in low-cost object storage.

From a technical perspective, this approach preserves SQL semantics across heterogeneous storage layers, allowing analytics teams to work with a single logical data model.

Real-time ingestion with FlowServer

Batch-oriented pipelines alone are no longer sufficient for many analytical use cases. WarehousePG includes a dedicated FlowServer component for real-time and near-real-time data ingestion.

FlowServer supports high-throughput event streaming from platforms such as Apache Kafka and RabbitMQ, enabling use cases such as operational analytics, fraud detection, and real-time monitoring. By ingesting streaming data directly into the warehouse, organizations eliminate latency between operational systems and analytical insight.

This architecture allows streaming and batch workloads to coexist within the same analytical platform, simplifying infrastructure and reducing data movement.

In-database AI, ML, and vector processing

A defining feature of EDB Postgres AI for WarehousePG is its support for in-database analytics and AI, eliminating the need to move large datasets to external machine learning (ML) platforms.

WarehousePG integrates MADlib for SQL-based machine learning, enabling users to train and score models directly within the database using familiar relational constructs. For more advanced use cases, the platform supports in-database Python ML frameworks, allowing data scientists to operate at scale without exporting data.

Native vector support via the pgvector extension enables similarity search, semantic search, and retrieval-augmented generation (RAG) workloads directly within the warehouse. This capability is increasingly critical for AI-driven applications that combine structured enterprise data with unstructured content such as documents and logs.

By collocating data, analytics, and AI, WarehousePG reduces pipeline complexity and accelerates time to insight.

High availability and enterprise readiness

WarehousePG is designed for production-grade reliability. High availability is achieved through a standby coordinator, ensuring continued operation in the event of a primary coordinator failure. Segment-level fault tolerance enables workloads to continue executing even when individual nodes are unavailable.

Enterprise features include workload management, predictable query scheduling, and comprehensive observability, ensuring stable operation under heavy analytical demand.

Crucially, organizations gain access to 24x7 support from EDB's Postgres experts, bridging the gap between open source flexibility and enterprise operational requirements.

Migration without disruption

For organizations modernizing from legacy analytical platforms, WarehousePG provides a low-risk path forward. Existing Greenplum workloads can be migrated via a binary swap, enabling rapid modernization without rewriting queries or retraining teams. High SQL parity also simplifies migrations from other SQL-based proprietary data warehouses.

This approach allows enterprises to modernize incrementally, preserving business continuity while regaining control over their analytics stack.

Rebuilding the warehouse for modern analytics

EDB PG AI for WarehousePG demonstrates that petabyte-scale analytics, AI readiness, and data sovereignty do not require proprietary platforms or cloud lock-in. By combining Postgres compatibility, MPP scalability, hybrid storage, real-time ingestion, and in-database AI and ML capabilities, WarehousePG delivers a technically robust foundation for modern enterprise analytics.

For organizations seeking a data warehouse that prioritizes architectural control, predictable performance, and open source economics, WarehousePG offers a compelling, future-proof alternative.

Contributed by EDB.