Database Landscape 2025: Postgres Dominance, MCP Adoption, and Emerging File Format Competition

Across the global data infrastructure landscape, 2025 has reinforced PostgreSQL’s status as the most widely adopted relational engine while new paradigms such as model‑centric database interfaces and next‑generation columnar formats gain traction. A detailed examination of the year reveals three intertwined narratives that dominate the conversation: the continued expansion of PostgreSQL’s ecosystem, the standardization of database interaction through Anthropic’s Model Context Protocol, and the revival of the file‑format battle that has long been a backdrop to storage and data‑processing decisions.

1. PostgreSQL’s Ascendancy and Ecosystem Growth

PostgreSQL’s dominance was underscored by a wave of corporate investment and product launches. November 2025 saw the release of PostgreSQL v18, adding an asynchronous I/O subsystem that reduces OS page‑cache reliance, support for skip scans, and further optimizer enhancements. While the features themselves are evolutionary, the fact that major players—Databricks, Snowflake, and Microsoft—are layering their services on PostgreSQL speaks to a strategic shift: the database community is converging on a single, extensible platform.

The acquisition of Neon by Databricks for $1 billion and Crunchy Data by Snowflake for $250 million illustrates a clear trend: the biggest public‑cloud investors are monetising PostgreSQL through managed‑service offerings rather than public listings. Microsoft’s HorizonDB and Amazon’s ongoing Aurora PostgreSQL update further cement this as the de facto “cloud‑native” version of PostgreSQL.

Parallel to these vertical‑market moves, two independent initiatives—Supabase’s Multigres and PlanetScale’s Neki—signal a shift toward horizontal scaling. Both projects aim to shard PostgreSQL using middleware similar to Vitess’ MySQL approach. While still in early stages, these efforts suggest that the PostgreSQL community is finally addressing the classic “single‑primary” bottleneck that has limited its OLAP scalability.

2. Model Context Protocol (MCP) Becomes the Interface Standard

Anthropic’s MCP was announced in late 2024 and rapidly gained traction as Google and Microsoft integrated support into their LLM stacks. MCP provides a JSON‑RPC layer that translates LLM tool calls into database queries, enabling AI agents to interrogate heterogeneous data stores without bespoke adapters.

In 2025, every commercial database vendor—ranging from ClickHouse to MongoDB—released an MCP server. PostgreSQL, lacking a vendor‑managed server, saw community‑driven implementations from Timescale, Supabase, and Xata. This standardised interface has opened the door for “agent‑first” data applications, but it also magnifies the need for robust security controls: many MCP servers simply forward raw SQL, exposing underlying data to potentially untrusted prompts.

Industry experts recommend that organizations restrict MCP‑exposed accounts to read‑only or narrowly scoped permissions, and that they employ query‑time filtering or sandboxing to guard against accidental data exposure. Enterprise stacks such as Oracle Database Firewall and IBM Guardium, designed for transaction integrity, are seen as best practices for an MCP‑enabled environment.

3. The File‑Format Wars Continue

Parquet has long dominated columnar storage, but 2025 introduced a new cohort of formats—CWI FastLanes, CMU‑Tsinghua F3, SpiralDB Vortex, AnyBlox, and Microsoft Amudai—alongside earlier releases like Meta Nimble and IoTDB TsFile. SpiralDB’s Vortex, now stewarded by a Linux‑Foundation steering committee, received significant attention for its hybrid decoder approach, combining native Rust crates with WASM decoders embedded in files.

Despite new entrants, Parquet remains the most widely adopted format, with 94 % of production schemas still using v1, even in 2025. The challenge lies more in heterogeneous library support than in the format spec itself. Cross‑compatibility efforts, such as AnyBlox’s single‑WASM decoder, aim to lower the barrier for clients to consume newer feature sets without wholesale upgrades.

4. Consolidations, Legal Battles, and Declines

High‑profile acquisitions shaped the year: DataStax was bought by IBM ($3 billion), Snowflake purchased Crunchy Data, and Microsoft’s acquisition of MongoDB’s DocumentDB led to the open‑source MongoDB‑compatible project under the Linux Foundation. In contrast, GPU‑accelerated engines such as HeavyDB and Kinetica struggled to maintain relevance; both announced closures or sell‑offs by mid‑2025.

Legal disputes also entered the arena. FerretDB’s migration of MongoDB queries onto PostgreSQL triggered a lawsuit from MongoDB, highlighting the tension between open‑source compatibility and intellectual‑property claims. While the outcome remains uncertain, the case raises important questions about the boundaries of protocol replication.

5. Key Takeaways for Practitioners

1. PostgreSQL is the central platform for both OLTP and OLAP workloads. Enterprises should consider PostgreSQL‑based DBaaS offerings for scalability while evaluating sharding projects like Multigres as they mature.
2. MCP adoption is now mainstream. When building LLM‑enabled analytics pipelines, secure the MCP interface with minimal privilege accounts and consider integrating with enterprise firewalls.
3. File‑format choice remains critical. Organizations must weigh the maturity of a format against their ecosystem’s library support, especially when planning data‑migration or storage‑optimisation projects.
4. Risk of GPU‑accelerated database consolidation. While GPU engines can accelerate specific workloads, the commercial viability of such systems has decreased; focus on cost‑effective, CPU‑centric solutions unless GPU resources are already available.
5. Legal readiness matters. Companies experimenting with schema or protocol adaptation should conduct thorough IP assessments to avoid litigation.

Looking ahead, the database sector will likely see continued consolidation around PostgreSQL, increased standardisation of AI–database interfaces, and a gradual shift toward hybrid file‑format solutions that ease cross‑vendor ingestion. For engineers and architects, the key is to keep abreast of these shifts and integrate them into data‑strategy roadmaps early.