Stop Separating Your Databases From Analytics
Here's something most data teams learn the hard way: splitting transactional workloads from analytical ones works great on paper and falls apart in practice. You build the pipeline. It breaks. You rebuild it. It drifts. Six months later your "single source of truth" is actually three sources with different timestamps and someone's best guess about which one matters.
EDB is betting that this split architecture — which has been the default for over a decade — is now actively harmful. Not just inefficient, but dangerous when you're trying to run AI agents that need fresh data to make decisions. Their answer: keep Postgres as the operational backbone and pull analytics into it, not the other way around.
This isn't EDB being contrarian for its own sake. The pressure's real. Databricks just announced LTAP (Lakehouse Transaction and Analytical Processing) based on Neon Postgres, Snowflake's expanding into operational workloads, and Microsoft folded transactional capabilities into Fabric. Everyone's converging because the old separation is becoming a liability when AI agents need to read, reason over, and act on business data in near real-time.
EDB's argument is that they're approaching this from the right direction. Databricks is building from the lakehouse outward, trying to pull transactional capability in through Lakebase. EDB's building from Postgres — where enterprises already run their most critical workloads — and expanding outward.
The Architecture Nobody Has to Explain Twice
Let me draw this out because the distinction matters more than most vendors let on.
Operational data lives in Postgres. That's your source of truth for transactions, user state, everything that has to be fast and consistent. Historical and tiered data gets stored in Apache Iceberg-managed object storage — cheap, scalable, open format. Then Iceberg acts as a shared catalog layer that connects Postgres to analytical engines like ClickHouse, WarehousePG, and Spark.
The key move here is that those analytical engines query the same data through a common catalog. No separate copies. No ETL pipelines moving data between systems. The operational data stays in Postgres, the historical data sits in Iceberg storage, and analytics engines read both through the catalog without needing to maintain their own copies.
I've spent too many years watching data teams manage five different systems for what should be two. Each one has its own security model, its own licensing, its own failure modes. EDB's approach collapses that down. You're governing one database with one security boundary, and the analytics layer reads through it.
That's not just simpler operations. That's fewer places for things to go wrong from a security standpoint.
Data Sovereignty Isn't a Buzzword — It's a Budget Line
This is where EDB actually differentiates from the cloud-native crowd.
Max Romanenko, EDB's chief engineering officer, puts it plainly: "For us, it's always been about the data sitting on infrastructure the customer owns and controls." That's not marketing copy. That's a positioning statement that says you can run this on-premises, in a private cloud, wherever your compliance requirements demand.
Stephanie Walter at HyperFrame Research calls this out directly: it resonates with CIOs focused on sovereignty, regulated data, and hybrid deployment. You get to run AI and analytics closer to the data, on infrastructure your enterprise controls, without creating yet another proprietary data estate.
And then there's the pricing. EDB uses a per-core model. That means your costs scale with what you deploy, not with how many queries you run or how much data you process. Ashish Chaturvedi at HFS Research notes this offers more predictable costs than Databricks LTAP for CIOs already struggling to manage analytics and AI budgets.
Here's the honest take: predictable bills aren't necessarily lower bills. Igor Ikonnikov at Info-Tech Research Group warns that the hardware requirements for high-speed operational data processing are higher and relatively more expensive compared to cheap lakehouse storage. You're trading variable cloud costs for higher fixed infrastructure costs. For some enterprises, that trade makes total sense. For others, it doesn't.
But the predictability alone is worth something. I've seen teams get blindsided by cloud data platform bills that tripled because someone ran an unoptimized query against a billion-row table. Per-core pricing removes that anxiety.
Fewer Platforms Means Fewer Attack Surfaces
This section might be where security teams actually lean in.
Every additional data store you deploy is another system to patch, another set of credentials to manage, another network segment to monitor. EDB's converged architecture reduces the number of platforms enterprises need to manage because operational, analytical, and AI workloads all access data through the same Postgres-Iceberg foundation.
Devin Pratt at IDC puts it well: reducing the number of specialized data stores means fewer systems to license and secure. That's not just an operational benefit — it's a security one. Less architectural tax across the board.
When you're running AI agents that need access to both transactional and historical data, the governance question gets harder fast. Who can query what? Where does PII live? How do you audit access across systems that were never designed to talk to each other? EDB's approach sidesteps a lot of that complexity by keeping everything on one foundation with one security model.
I'm not saying this eliminates governance challenges. It just concentrates them in a place you already know how to secure.
The ETL Pipeline Problem Nobody Talks About Enough
Let's be real: most enterprise data architectures are held together by ETL pipelines that nobody fully understands anymore.
Someone built them five years ago. The original architect left. The documentation is stale. But they're moving data from the transactional database to the warehouse, and if you touch anything in that chain, everything breaks. So nobody touches it.
EDB's zero-ETL approach eliminates most of that plumbing. Operational data stays in Postgres. Analytical engines read it through Iceberg's catalog. No pipelines to build, no pipelines to break, no pipelines to debug at 2 AM when a report is due.
Walter notes that this reduces the number of systems developers must integrate and maintain. Pratt adds that zero-ETL means far less plumbing to build and break, so engineers spend their time creating value.
I'll add my own observation: fewer pipelines means fewer places for data to get stale, inconsistent, or — worst case — exfiltrated. Every ETL job is a data movement. Every data movement is a potential risk surface. Removing them isn't just an efficiency play.
Agentic Database: Autonomous DBA or Just a Fancy Dashboard?
EDB's converged analytics announcement came with another feature: an "agentic database" that automates routine DBA tasks. The system monitors hundreds of operational and performance metrics, detects anomalies, recommends corrective actions, and — where enterprise policies permit — can automatically apply fixes.
EDB claims these automated agents optimize and tune databases up to 10 times faster than manual approaches.
Walter's take: it's more an evolution of autonomous database concepts than a wholly new category. Oracle and others have offered autonomous capabilities for years. Where EDB can differentiate, she says, is in extending those capabilities with AI-driven reasoning, automated remediation, and governance controls that let enterprises determine how much authority the system receives.
That last point matters. A lot of autonomous database features I've seen are basically "we'll fix things for you, and if we break them, too bad." EDB's framing — that enterprises control how much authority the agents get — is at least theoretically more responsible. Whether it works in practice depends on how well those governance controls actually constrain the agents when they misbehave.
I'll believe it when I see it. But the direction's right.
The Competitive Landscape: Everyone's Converging
EDB isn't the only one doing this. Snowflake's expanding operational workload support through open table formats. Microsoft combined transactional and analytical services under Fabric. Databricks just announced LTAP.
The market is clearly moving toward convergence because the underlying pressure is real: AI agents need immediate access to operational data, historical context, and governance controls — all at the same time.
But EDB's positioning is distinct. They're not trying to be a lakehouse with transactional capabilities bolted on. They're starting from Postgres — the database most enterprises already trust, already operate, and already have skills around — and expanding from there. That's a different bet than building from the analytics layer outward.
For enterprises that already run Postgres (and there are a lot of them, given the open-source ecosystem), this convergence path feels like a natural evolution rather than a platform migration. That's worth something.
Who This Actually Fits
EDB Postgres AI supports petabyte-scale data management with what they call "zero data sprawl." The platform targets sovereign AI deployments — meaning you control where the data lives, who processes it, and how it's governed.
Real-world use cases from their customer base include processing 30 billion-plus trades per day and repatriating 50 TB of warehouse data from the cloud back to on-premises infrastructure. That's not startup-scale stuff.
This approach fits enterprises that:
- Run regulated workloads where data residency matters
- Want predictable infrastructure costs rather than variable consumption pricing
- Already have Postgres skills and want to extend them into analytics
- Are building AI agents that need real-time access to operational data
- Want to reduce the complexity of managing multiple specialized data stores
It might not fit teams that need maximum query flexibility for exploratory analytics, or organizations that want to avoid upfront hardware investment. Every architecture has tradeoffs.
But for the right enterprise, EDB's Postgres-first converged analytics is a genuinely compelling option — not because it's the newest thing, but because it solves real problems with infrastructure most teams already understand.