For many enterprises, the evolution of the data warehouse has transitioned from being a strategic asset to an operational liability. Traditional proprietary platforms, such as Teradata, and cloud-exclusive services like Snowflake, have provided scalability and performance. However, these solutions often come with the drawbacks of vendor lock-in, unpredictable pricing, and limited architectural flexibility. As regulatory pressures mount and AI-driven analytics become essential for maintaining a competitive edge, organizations are reevaluating whether their current warehouse platforms align with their long-term business objectives.
EDB Postgres® AI (EDB PG AI) presents a solution to these challenges through its WarehousePG, an open-source, petabyte-scale data warehouse designed to restore control, predictability, and data sovereignty without compromising performance. Built on Postgres and optimized for massively parallel analytics, WarehousePG offers a modern alternative to restrictive systems, boasting a potential reduction of up to 58% in total cost of ownership (TCO).
Open source, petabyte-scale analytics with Postgres at the core
Enterprise data warehouses are increasingly being pushed beyond their original design parameters. The coexistence of petabyte-scale datasets, hybrid deployment requirements, sovereign data mandates, and AI-driven analytics in production environments necessitates both extreme performance and architectural flexibility. Traditional proprietary platforms and cloud-only warehouses often struggle to meet these demands simultaneously, forcing organizations to make compromises between cost, control, and capability.
EDB Postgres AI for WarehousePG fills this gap by providing a fully open-source, petabyte-scale data warehouse built on Postgres. It is engineered for high-performance analytics, in-database AI, and deployment flexibility across on-premises, cloud, and hybrid environments.
Architecture: Postgres-based MPP at scale
WarehousePG employs a massively parallel processing (MPP) architecture that allows it to scale out across hundreds of nodes. Instead of relying on a single-server scale-up model, it distributes both data and query execution across multiple segment nodes, all coordinated by a central coordinator node. This coordinator manages query parsing, optimization, and execution planning. Once a query plan is established, tasks are allocated to the segments, which operate in parallel on their local data partitions. This architecture enables WarehousePG to efficiently execute complex analytical queries—such as large joins, aggregations, window functions, and transformations—across petabyte-scale datasets, eliminating the bottlenecks typical of monolithic databases while maintaining full SQL compatibility with Postgres.
Predictable performance without proprietary constraints
In contrast to cloud-native warehouses that depend on consumption-based pricing and opaque resource management, WarehousePG offers deterministic workload behavior and predictable performance. Resource allocation and query execution are explicitly managed within the cluster, ensuring consistent response times even under mixed analytical workloads. With its Apache 2.0 licensing and foundation on open-source Postgres, enterprises can avoid proprietary storage formats and vendor-controlled execution engines. This ensures that data remains fully accessible, portable, and deployable wherever needed—whether on-premises for regulatory compliance, in the public cloud for scalability, or in hybrid configurations for cost efficiency. This architectural independence, combined with EDB’s core-based pricing, facilitates up to a 58% reduction in TCO, particularly for organizations transitioning from costly proprietary platforms or unpredictable cloud warehouses.
Hybrid storage and SQL access to data lakes
Modern analytical environments increasingly encompass multiple storage tiers. WarehousePG addresses this need through the Platform Extension Framework (PXF), which allows 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 like Parquet, AVRO, JSON, and CSV without the need to duplicate data within the warehouse. This significantly simplifies ETL processes and reduces storage redundancy while enabling a hybrid “warm and cold data” strategy. Frequently accessed datasets can be retained in WarehousePG’s high-performance storage, while less frequently accessed data can be stored in cost-effective object storage. This approach preserves SQL semantics across diverse storage layers, allowing analytics teams to operate with a unified logical data model.
Real-time ingestion with FlowServer
In today’s fast-paced analytical landscape, batch-oriented pipelines alone are often insufficient. WarehousePG includes a dedicated FlowServer component for real-time and near-real-time data ingestion. FlowServer supports high-throughput event streaming from platforms like Apache Kafka and RabbitMQ, facilitating use cases such as operational analytics, fraud detection, and real-time monitoring. By enabling the direct ingestion of streaming data into the warehouse, organizations can eliminate latency between operational systems and analytical insights. This architecture allows both streaming and batch workloads to coexist within the same analytical platform, simplifying infrastructure and minimizing data movement.
In-database AI, ML, and vector processing
A standout feature of EDB Postgres AI for WarehousePG is its support for in-database analytics and AI, which negates the need to transfer large datasets to external machine learning (ML) platforms. WarehousePG integrates MADlib for SQL-based machine learning, allowing users to train and score models directly within the database using familiar relational constructs. For more advanced applications, the platform supports in-database Python ML frameworks, enabling data scientists to operate at scale without the need to export data. Additionally, native vector support via the pgvector extension facilitates similarity search, semantic search, and retrieval-augmented generation (RAG) workloads directly within the warehouse. This capability is increasingly vital for AI-driven applications that merge structured enterprise data with unstructured content, such as documents and logs. By collocating data, analytics, and AI, WarehousePG streamlines pipeline complexity and accelerates time to insight.
High availability and enterprise readiness
WarehousePG is engineered for production-grade reliability. High availability is ensured through a standby coordinator, which maintains operations in the event of a primary coordinator failure. Segment-level fault tolerance allows workloads to continue executing even when individual nodes are unavailable. Enterprise features include workload management, predictable query scheduling, and comprehensive observability, ensuring stable performance under heavy analytical demand. Organizations also benefit from 24×7 support from EDB’s Postgres experts, bridging the gap between open-source flexibility and enterprise operational needs.
Migration without disruption
For organizations transitioning from legacy analytical platforms, WarehousePG offers a low-risk modernization pathway. Existing Greenplum workloads can be migrated through a binary swap, facilitating rapid modernization without the need to rewrite queries or retrain teams. The 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 exemplifies that petabyte-scale analytics, AI readiness, and data sovereignty do not necessitate proprietary platforms or cloud lock-in. By integrating Postgres compatibility, MPP scalability, hybrid storage, real-time ingestion, and in-database AI and ML capabilities, WarehousePG provides a robust foundation for contemporary enterprise analytics. For organizations aiming for a data warehouse that emphasizes architectural control, predictable performance, and open-source economics, WarehousePG presents a compelling and future-proof alternative.
Contributed by EDB.
