For years, enterprises have maintained separate systems for processing transactional (OLTP) and analytical (OLAP) data, often moving data between them through complex ETL pipelines. However, the rise of autonomous agents and AI applications that require immediate access to operational data—while generating vast amounts of their own operational data—has exposed the cost and complexity of maintaining those silos. The industry's response has been swift, with data warehouse and database vendors proposing competing approaches to collapse these data silos.
In recent weeks, Databricks unveiled LTAP, EDB introduced converged analytics, and late last year Snowflake launched pg_lake. Now, distributed PostgreSQL provider pgEdge has entered the fray with ColdFront, a PostgreSQL-native hot-and-cold data tiering architecture that automatically moves older data into Apache Iceberg object storage while keeping PostgreSQL as the only database that applications need to interact with. ColdFront is currently in beta.
How ColdFront Works
In ColdFront's architecture, 'hot' refers to newer data that remains in PostgreSQL for low-latency transactions, while 'cold' refers to older historical data that is transparently tiered to Iceberg object storage. The key differentiator is that PostgreSQL remains the primary interface for all queries, regardless of where the data resides. When an application queries recent data, it runs directly against PostgreSQL. When the query involves older records stored in Iceberg, ColdFront uses DuckDB's embedded analytical engine to execute the query transparently, without requiring any application changes or separate query paths.
This approach stands in contrast to other architectures. Databricks' LTAP keeps operational applications connected to a lakehouse where analytics and AI are performed, shifting the center of gravity away from PostgreSQL. EDB keeps PostgreSQL as the operational source of truth but exposes data through Iceberg for analytical engines, requiring archived data to be brought back into active PostgreSQL before modification. Snowflake's pg_lake writes PostgreSQL data directly into Iceberg so both PostgreSQL and Snowflake can query the same data, but applications must distinguish between PostgreSQL and analytical tables. ColdFront treats Iceberg only as a transparent storage tier behind PostgreSQL, automatically moving older data out of the database while keeping applications on the same tables and SQL.
Why Writable Cold Storage Matters
A standout feature of ColdFront is its support for a writable cold storage tier. Unlike most tiering systems where archived data is read-only, ColdFront allows older records stored in Iceberg to be updated or deleted through PostgreSQL without requiring application changes or complex restore-delete-rearchive procedures. This capability is critical for regulated industries that must comply with data protection and privacy laws, such as GDPR, which grant individuals the right to have their data erased or modified. In most tiering systems, a GDPR deletion request on archived data involves restoring the data, deleting the records, and re-archiving—a process that can take hours. ColdFront's architecture allows this to be done through a single SQL statement.
Enterprises in financial services, healthcare, and government increasingly need to retain growing volumes of historical operational data generated by AI applications for audit and regulatory purposes. They also need the ability to correct, delete, or modify records even after the data has moved into lower-cost storage. ColdFront's writable cold tier simplifies these processes, reducing operational overhead and enabling faster compliance responses.
The DuckDB Dependency
Despite their architectural differences, all the vendors in this space are converging on a common component: DuckDB, the embedded analytical engine. ColdFront uses DuckDB to execute queries against data stored in Iceberg. Snowflake's pg_lake routes Iceberg queries through pgduck_server, and Databricks' Lakebase also relies on DuckDB internally for parts of its analytical processing. As a result, DuckDB is rapidly becoming the de facto embedded analytics engine for this new generation of PostgreSQL-Iceberg architectures.
This growing dependence creates a concentration risk, as analysts have pointed out. If DuckDB faces licensing changes, security vulnerabilities, performance bottlenecks, or governance issues, the impact would ripple across multiple products simultaneously. CIOs should understand the maturity and roadmap of the shared components these architectures increasingly depend on. However, the similarity in shared components does not make evaluation of these competing architectures easier for CIOs, as each approach makes different tradeoffs in where the center of gravity for data lies.
Enterprise Considerations
Most enterprises already have established data architectures, and CIOs should evaluate these platforms based on where their data, developers, and operational workflows already reside rather than assuming one architecture fits every environment. For enterprises still defining their long-term data strategy, standardizing on Apache Iceberg first is recommended, since all four architectures support the open table format. Enterprises will retain the flexibility to replace the front-end database or analytical platform later without migrating the underlying data.
However, even that portability has limits, particularly around Iceberg catalog governance. All four approaches write to Iceberg, but they use different catalogs, and their interoperability across vendors remains an open problem. When agents from different systems need to query the same Iceberg tables, catalog federation becomes a real operational challenge.
pgEdge's ColdFront represents a significant step in reducing the complexity of managing both transactional and analytical workloads in the age of AI. By keeping PostgreSQL as the primary interface and enabling writable cold storage, it addresses key pain points for enterprises dealing with data proliferation, regulatory compliance, and the need for real-time access to historical data. Whether it gains traction over competing architectures will depend on its ability to deliver on performance, ease of integration, and the strength of its community around PostgreSQL.
As the industry continues to converge on Iceberg as the standard for open table formats and DuckDB as the analytical engine of choice, the next battleground will likely be around governance, interoperability, and the ability to seamlessly manage data across multiple tiers and clouds. Enterprises should watch these developments closely and plan for a future where the line between transactional and analytical systems becomes increasingly blurred.
Source:InfoWorld News