How PostgreSQL Became the Foundation of Modern Data Platforms
PostgreSQL is more than a relational database; it is an open-source engineering ecosystem. For decades, PostgreSQL has proven that reliability, correctness, and extensibility can coexist without vendor lock-in.
What makes PostgreSQL truly exceptional is not just its feature set, but the fact that entire databases are built on top of it to solve problems that core PostgreSQL intentionally avoids.
This blog explores the five most influential PostgreSQL-derived databases, explains why they exist, what makes each one unique, and why PostgreSQL itself continues to dominate the open-source database world.
Why PostgreSQL Is the Base for So Many Databases
PostgreSQL provides something rare in software:
- A clean and well-structured codebase
- A battle-tested storage and transaction model
- Strong SQL compliance
- A permissive open-source license
Because of this, PostgreSQL can be extended, forked, and re-architected without compromising correctness.
Instead of reinventing relational theory, these projects build on PostgreSQL’s proven core and focus on specialized problems.
1. Neon: PostgreSQL Reimagined for the Cloud
GitHub Repository
https://github.com/neondatabase/neon
Purpose
Neon transforms PostgreSQL into a cloud-native, serverless database by separating compute from storage.
Why Neon Was Created
Traditional PostgreSQL assumes:
- Persistent local disks
- Long-running database servers
This model works well on physical machines but struggles in cloud environments where:
- Compute needs to scale rapidly
- Idle resources must be minimized
- Developers expect instant environments
Neon solves this by decoupling PostgreSQL compute nodes from durable cloud storage.
How Neon Works
Neon stores data and WAL in a remote storage layer, while compute nodes:
- Can be created or destroyed instantly
- Are stateless
- Attach to storage on demand
This architecture enables database branching, rapid recovery, and near-instant scaling.
Key Features
- Compute–storage separation
- Serverless PostgreSQL
- Instant database branching
- Fast recovery using WAL replay
- Designed for cloud elasticity
Use Cases
- SaaS platforms
- Development and testing environments
- Multi-tenant systems
- Cloud-native applications
Why Neon Is Unique
Neon keeps PostgreSQL logically unchanged while completely modernizing its infrastructure model.
It proves PostgreSQL can be truly cloud-first without sacrificing compatibility.
2. Apache Cloudberry: Distributed Analytics with PostgreSQL DNA
GitHub Repository:
https://github.com/apache/cloudberry
Purpose
Cloudberry is built for large-scale analytical workloads using a massively parallel processing (MPP) architecture.
PostgreSQL Lineage
Cloudberry is a fork of Greenplum, which itself is a deep PostgreSQL fork.
It inherits PostgreSQL’s SQL engine while extending it for distributed execution.
Why Cloudberry Exists
PostgreSQL excels at transactional workloads (OLTP), but:
- Analytical queries require scanning huge datasets
- Single-node execution becomes a bottleneck
Cloudberry distributes data and queries across multiple nodes, enabling parallel execution.
Key Features
- MPP architecture
- Distributed query planner
- Parallel execution across segments
- Optimized for OLAP workloads
- PostgreSQL-compatible SQL
Use Cases
- Data warehouses
- Business intelligence platforms
- Analytical reporting
- Large-scale data analytics
Why Cloudberry Is Unique
Cloudberry allows organizations to reuse PostgreSQL skills while scaling analytics horizontally — a major advantage in data-heavy environments.
3. BDR: Multi-Master PostgreSQL Replication
GitHub Repository:
https://github.com/2ndQuadrant/bdr-postgres
Purpose
BDR enables multi-master replication, allowing multiple PostgreSQL nodes to accept writes simultaneously.
Why BDR Was Needed
Native PostgreSQL replication models are:
- Primarily single-writer
- Optimized for high availability, not global writes
BDR introduces logical replication capable of handling distributed write workloads.
How BDR Works
BDR replicates changes at the logical level:
- Captures row-level changes
- Applies them across nodes
- Detects and resolves conflicts
This enables PostgreSQL clusters spread across regions.
Key Features
- Multi-master replication
- Logical replication engine
- Conflict detection and resolution
- Global high availability
- Minimal application changes
Use Cases
- Globally distributed applications
- High-availability systems
- Multi-region deployments
Why BDR Is Unique
BDR tackles one of the hardest problems in databases: multi-writer consistency—while preserving PostgreSQL semantics.
4. OrioleDB: A Modern Storage Engine for PostgreSQL
GitHub Repository:
https://github.com/orioledb/orioledb
Purpose
OrioleDB introduces a modern, MVCC-aware storage engine for PostgreSQL that replaces traditional heap storage on a per-table basis, eliminating table bloat and VACUUM overhead while improving OLTP performance.
Why OrioleDB Exists
PostgreSQL’s heap storage:
- Requires VACUUM
- Can accumulate table bloat
- Adds maintenance overhead
OrioleDB addresses these limitations by redesigning the storage layer.
How OrioleDB Improves PostgreSQL
OrioleDB integrates:
- Built-in crash recovery
- Efficient MVCC cleanup
- Reduced write amplification
The result is a PostgreSQL fork with less maintenance and better write performance.
Key Features
- No VACUUM requirement
- Reduced table bloat
- Faster write operations
- Built-in crash safety
- Optimized concurrency handling
Use Cases
- High-write OLTP systems
- Long-running databases
- Performance-critical workloads
- Systems with limited maintenance windows
Why OrioleDB Is Unique
It shows that PostgreSQL can evolve at the storage engine level without breaking SQL or applications.
5. Greenplum: PostgreSQL at Enterprise Scale
GitHub Repository:
https://github.com/greenplum-db/gpdb
Purpose
Greenplum extends PostgreSQL into a distributed analytics platform for enterprise data workloads.
Architecture Overview
Greenplum uses:
- A shared-nothing architecture
- A master node for query planning
- Multiple segment nodes for execution
Queries are decomposed and executed in parallel across segments.
Key Features
- MPP architecture
- Advanced query optimizer
- Parallel data processing
- Column-oriented storage
- PostgreSQL-compatible SQL
Use Cases
- Enterprise data warehouses
- Large-scale analytics
- Data science workloads
- ETL pipelines
Why Greenplum Is Unique
Greenplum demonstrates that PostgreSQL can power enterprise-grade big data systems without abandoning relational principles.
The Broader Impact of PostgreSQL as an Open-Source Project
PostgreSQL’s success is not accidental.
Its governance model prioritizes:
- Stability over hype
- Correctness over shortcuts
- Community over control
Because of this, PostgreSQL enables innovation without fragmentation.
Each derived database solves a specific problem while respecting PostgreSQL’s foundations.
Why PostgreSQL Still Remains the Most Used Open-Source Relational Database
Despite powerful derivatives:
- PostgreSQL remains the default choice
- Its ecosystem continues to grow
- Cloud providers deeply integrate it
PostgreSQL evolves carefully, ensuring long-term trust — which is why it remains the backbone of modern data systems.
conclusion
PostgreSQL is not just surviving in a world of specialized databases — it is powering them.
- Neon modernizes PostgreSQL for the cloud.
- Cloudberry and Greenplum scale analytics.
- BDR enables global writes.
- OrioleDB modernizes storage.
Together, they prove one thing clearly:
PostgreSQL is not just a database — it is a foundation for innovation.