The convergence of transactional and analytical data processing has become a pressing priority as enterprises deploy AI applications that generate and consume vast amounts of operational data in real time. For years, organizations maintained separate systems for online transaction processing (OLTP) and online analytical processing (OLAP), accepting the cost and complexity of moving data between them. But the rise of autonomous agents and AI workloads that require immediate access to both current and historical data has exposed the limitations of this fragmented approach.
In response, a wave of new architectures has emerged from major data platform vendors. Databricks unveiled its Lakehouse Transactional Analytical Processing (LTAP) framework, EDB introduced converged analytics on PostgreSQL, and Snowflake launched pg_lake. Now, distributed PostgreSQL provider pgEdge has entered the fray with ColdFront, a beta-stage hot-and-cold data tiering architecture that promises to unify OLTP and OLAP storage in a PostgreSQL-native manner while supporting the growing demands of AI.
ColdFront architecture: PostgreSQL at the center
ColdFront automatically moves older data—referred to as cold data—from the primary PostgreSQL database to Apache Iceberg object storage, while keeping PostgreSQL as the sole database interface for applications. This means that applications continue to interact with the same tables and SQL queries, with no need for ETL pipelines, separate query paths, or application modifications. Recent data remains in PostgreSQL for fast transactional access, while queries against older records are transparently executed using DuckDB’s embedded analytical engine. According to pgEdge cofounder Phillip Merrick, this approach eliminates the complexity of managing separate systems and allows developers to treat all data as if it resides in a single database.
The key differentiator of ColdFront is its support for a writable cold tier. Unlike most tiering systems where archived data becomes read-only, ColdFront allows updates, deletes, and corrections to older records stored in Iceberg directly through PostgreSQL SQL statements. This is particularly valuable for regulated industries that must comply with data protection laws such as GDPR, where deletion or modification requests for archived data traditionally require restoring the data, making changes, and re-archiving—a process that can take half a day. With ColdFront, a single SQL UPDATE or DELETE statement accomplishes the same task, simplifying compliance workflows.
Comparison with competing architectures
Analysts note that ColdFront differs from rival approaches in where the center of gravity for data lies. Databricks’ LTAP keeps operational applications connected to a lakehouse where analytics and AI are performed, requiring enterprises to adopt a proprietary platform as the operational core. EDB’s converged analytics keeps PostgreSQL as the operational source of truth but exposes data through Iceberg for analytical engines; however, archived data must still be brought back into active PostgreSQL before it can be modified. 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, as described by HFS Research leader Ashish Chaturvedi, treats Iceberg purely as a transparent storage tier behind PostgreSQL. This design makes it easier for enterprises that want to maintain a single operational database while taking advantage of cost-effective object storage for historical data. The writable cold tier also alleviates the tradeoffs seen in other architectures, particularly for organizations that need to balance data residency, sovereignty, and regulatory compliance. Amit Chandak, chief analytics officer at Kanerika, notes that AI applications generate increasing volumes of historical operational data for audit and regulatory purposes, and the ability to modify records in cold storage without complex recovery procedures is a significant advantage.
The DuckDB dependency and concentration risk
Despite architectural differences, all vendors in this space share a growing reliance on DuckDB, the embedded analytical engine. ColdFront uses DuckDB to execute queries against Iceberg data. Snowflake’s pg_lake routes Iceberg queries through pgduck_server, and Databricks’ Lakebase also internally depends on DuckDB for parts of its analytical processing. According to Info-Tech Research Group advisory fellow Igor Ikonnikov, DuckDB is rapidly becoming the de facto embedded analytics engine for this new generation of PostgreSQL-Iceberg architectures, creating a concentration risk. If DuckDB faces licensing changes, security vulnerabilities, performance bottlenecks, or governance issues, the impact would ripple across multiple products simultaneously. CIOs are advised to understand the maturity and roadmap of these shared components when evaluating competing platforms.
Strategic considerations for CIOs
Most enterprises already have established data architectures, making the evaluation of these competing approaches a complex task. Michael Leone, principal analyst at Moor Insights & Strategy, recommends that CIOs base their decisions on where their data, developers, and operational workflows currently reside rather than assuming one architecture fits all scenarios. For organizations still defining their long-term data strategy, Leone suggests standardizing on Apache Iceberg first, as all four architectures support the open table format. This approach preserves flexibility to replace the front-end database or analytical platform later without migrating the underlying data.
However, portability has limits. Ikonnikov warns that Iceberg catalog governance remains an open problem. Different vendors use different catalogs, and interoperability across vendors is not yet seamless. When AI agents from different systems need to query the same Iceberg tables, catalog federation becomes a real operational challenge. As the industry converges on Iceberg as a common storage layer, these governance issues will need to be addressed to realize the full potential of unified transactional and analytical processing.
pgEdge’s ColdFront is currently available in beta, and its success will depend on how well it addresses the needs of enterprises that require both low-latency transactional access and cost-effective archival storage, especially in regulated environments. The architecture’s ability to handle writable cold storage without disrupting existing applications gives it a unique value proposition in a rapidly evolving market.
Source: InfoWorld News