For numerous enterprises, the data warehouse has transformed from a strategic asset into an operational burden. Long-standing proprietary platforms like Teradata, along with cloud-exclusive services such as Snowflake, have delivered scalability and performance—but at the expense of vendor lock-in, unforeseen pricing, and restricted architectural adaptability.
As regulatory scrutiny intensifies and AI-powered analytics become central to competitive advantage, organizations are re-evaluating whether their current warehouse platforms truly align with long-term business objectives.
EDB Postgres® AI (EDB PG AI) tackles these challenges head-on with WarehousePG, an open-source, petabyte-scale data warehouse crafted to restore control, predictability, and data sovereignty—all without compromising performance. Built on Postgres and engineered for massive parallel analytics, WarehousePG offers a modern way to break free from restrictive systems while cutting total cost of ownership (TCO) by up to 58%.
Open-Source, Petabyte-Scale Analytics with Postgres at Its Core
Enterprise data warehouses are now being stretched beyond their original design limits. Petabyte-sized datasets, hybrid deployment needs, data sovereignty requirements, and AI-driven analytics all coexist in production environments that demand both exceptional performance and architectural flexibility.
Traditional proprietary platforms and cloud-only warehouses struggle to meet these demands simultaneously, forcing organizations to make trade-offs between cost, control, and functionality.
EDB Postgres AI for WarehousePG fills this gap by delivering a fully open-source, petabyte-scale data warehouse built on Postgres. Engineered for high-performance analytics, in-database AI, and flexible deployment across on-premises, cloud, and hybrid environments, it addresses the limitations of legacy and cloud-exclusive systems.
Architecture: Postgres-Based MPP at Scale
WarehousePG’s massively parallel processing (MPP) architecture 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, overseen by a central coordinator node.
The coordinator handles query parsing, optimization, and execution planning. Once a query plan is finalized, tasks are distributed to the segments, which operate in parallel on their local data partitions. This approach enables WarehousePG to efficiently run complex analytical queries—including large joins, aggregations, window functions, and transformations—across petabyte-scale datasets.
This architecture eliminates the inherent bottlenecks of monolithic databases while maintaining full SQL compatibility with Postgres, greatly reducing the learning curve for existing data teams.
Predictable Performance Without Proprietary Restrictions
Unlike cloud-native warehouses that rely on consumption-based pricing and opaque resource management, WarehousePG offers deterministic workload behavior and consistent performance. Resource allocation and query execution are fully controlled within the cluster, ensuring steady response times even under mixed analytical workloads.
As an Apache 2.0-licensed solution built on open-source Postgres, WarehousePG frees enterprises from proprietary storage formats and vendor-controlled execution engines. Data remains fully accessible, portable, and deployable wherever the organization needs it—on-premises for regulatory compliance, in the public cloud for elasticity, or in hybrid setups for cost optimization.
This architectural independence, combined with EDB’s core-based pricing, enables up to a 58% reduction in TCO—especially for organizations migrating from high-cost proprietary platforms or unpredictable cloud warehouses.
Hybrid Storage and SQL Access to Data Lakes
Modern analytical environments are increasingly spread across multiple storage tiers. WarehousePG addresses this through its Platform Extension Framework (PXF), which enables direct SQL access to external data stored in object stores and distributed file systems, such as Amazon S3 and Hadoop Distributed File System (HDFS).
With PXF, data engineers can query formats like Parquet, AVRO, JSON, and CSV without copying data into the warehouse. This significantly reduces ETL complexity and storage redundancy while enabling a hybrid “warm and cold data” strategy: frequently accessed datasets stay in WarehousePG’s high-performance storage, while infrequently used data resides in low-cost object storage.
From a technical standpoint, this approach preserves SQL semantics across diverse storage layers, allowing analytics teams to work with a single logical data model.
Real-Time Ingestion with FlowServer
Batch-only pipelines are no longer enough 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 like 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 insights.
This architecture lets streaming and batch workloads coexist within the same analytical platform, simplifying infrastructure and reducing data movement.
In-Database AI, ML, and Vector Processing
A key feature of EDB Postgres AI for WarehousePG is its support for in-database analytics and AI, removing the need to move 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 structures. For more advanced use cases, the platform supports in-database Python ML frameworks, enabling 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 becoming increasingly critical for AI-driven applications that combine structured enterprise data with unstructured content like documents and logs.
By centralizing data, analytics, and AI, WarehousePG reduces pipeline complexity and speeds up time to insight.
High Availability and Enterprise Readiness
WarehousePG is designed for production-grade reliability. High availability is achieved through a standby coordinator, ensuring uninterrupted operation if the primary coordinator fails. Segment-level fault tolerance allows workloads to continue running 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 24/7 support from EDB’s Postgres experts, bridging the gap between open-source flexibility and enterprise operational needs.
Migration Without Disruption
For organizations modernizing from legacy analytical platforms, WarehousePG offers 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 proves 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.
Beijing Qianxing Jietong Technology Co., Ltd.
Sandy Yang/Global Strategy Director
WhatsApp / WeChat: +86 13426366826
Email: yangyd@qianxingdata.com
Website: www.qianxingdata.com/www.storagesserver.com
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