The Story Every Data Engineer Wonders About

When people talk about data engineering, the most common explanation is simple: a data engineer moves data from one system to another. In most real-world setups, this means moving data from an OLTP system—where transactions are written continuously—into an OLAP system that is optimized for analytics, reporting, and business insights. This explanation is usually sufficient for anyone new to the field.

Over time, however, many data engineers begin to question why transactional and analytical workloads must exist in completely separate systems. While the technical reasons are well understood—OLTP systems prioritize ACID guarantees and write performance, while OLAP systems are optimized for large-scale reads—the separation creates friction. A significant portion of data engineering effort goes into building, maintaining, and troubleshooting pipelines whose primary purpose is to bridge this gap.

This is where Snowflake’s move to bring Postgres into its ecosystem becomes interesting. Instead of treating OLTP and OLAP as disconnected systems that require constant integration, Snowflake is moving toward a model where transactional and analytical workloads coexist under a single platform, reducing complexity and operational overhead.

In this blog, I explore why this shift matters, what problems it actually solves for data engineers, and how it changes the way we think about data architecture.

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The Elephant in the Room

Snowflake Postgres may appear to be a small announcement, but it quietly addresses a long-standing question in modern data architectures:

Why do transactional data and analytical data still live in separate worlds?

Before answering that, it helps to clearly understand what Snowflake Postgres is—and what it is not.

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What Exactly Is Snowflake Postgres?

Snowflake Postgres is a fully managed PostgreSQL service provisioned directly from a Snowflake account.

In practical terms:

• It is a fully compatible PostgreSQL database
• Existing Postgres clients and drivers work without change
• Snowflake manages scaling, availability, security, and governance

From a developer’s perspective, it feels familiar. From an operational perspective, much of the underlying complexity disappears.

This offering builds on Snowflake’s 2025 acquisition of Crunchy Data, a company known for running PostgreSQL reliably at enterprise scale. That acquisition now looks less like an isolated move and more like a foundational step.

Snowflake is not trying to replace Postgres, nor is it turning itself into a traditional OLTP database. Instead, it is bringing Postgres into the same control plane that already supports analytics and AI workloads. That distinction is subtle, but important.

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Why This Was Needed?

For years, most data architectures followed a sensible separation:

• PostgreSQL (or similar systems) for application and transactional workloads
• Snowflake for analytics, reporting, and business intelligence

Each system did what it was best at. The model worked.

However, as data volumes grew and expectations shifted toward fresher insights and AI-driven use cases, the cost of this separation became more visible.

The Day-to-Day Reality

In practice, this separation often meant:

• Continuous replication of data from Postgres into Snowflake
• Lag between production events and analytical visibility
• Multiple security and governance models to manage
• More systems to monitor, scale, and troubleshoot
• Extra logic solely to keep data “reasonably fresh”

This was not poor design. It was simply the best architecture available when transactional and analytical systems lived on different platforms.

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What Snowflake Postgres Changes

Snowflake Postgres shortens the distance between two critical points in a data system:

• where data is created
• where data is analyzed or used by AI

In traditional setups, these points are separated by layers of pipelines, replication tools, and orchestration logic. Snowflake Postgres reduces that distance.

This shift is less about raw performance and more about simplifying the overall system.

Practical Impact

At a practical level, this results in:

• Reduced need for constant replication of transactional data
• Fewer pipelines, which means fewer failure points
• Unified governance, access control, and auditing
• Easier access to fresher operational data for analytics and AI

When combined with open table formats such as Apache Iceberg and AI capabilities like Snowflake Cortex, this approach points toward a unified data foundation rather than a collection of loosely connected systems.

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Why This Matters for Data Engineers

Cleaner Pipelines and Lower Operational Overhead

Traditional architectures rely on CDC tools, ETL or ELT pipelines, and orchestrators to keep transactional and analytical systems in sync. Each layer is necessary, but together they add operational weight.

With Snowflake Postgres, operational data and analytical workloads share the same platform. This reduces the number of moving parts and allows data engineers to spend more time on modeling, optimization, and business use cases rather than pipeline maintenance.

Fewer Sync Issues and More Trust in Data

Separated systems often introduce:

• Late or missing records
• Schema drift
• Partially updated datasets after failures

With Postgres integrated into Snowflake, governance and security remain consistent, data duplication is reduced, and analytical outputs more accurately reflect operational reality. This leads to greater trust in dashboards and reports.

Faster Analytics and AI Experimentation

Many modern use cases—such as personalization, fraud detection, and near real-time analytics—depend on fresh operational data. Traditionally, this freshness comes at the cost of complex synchronization logic.

Snowflake Postgres narrows this gap, making it easier to experiment and iterate without redesigning data movement pipelines for every new use case.

Unified Security and Governance

Managing separate access controls, auditing systems, and compliance models across platforms is expensive and error-prone.

With Snowflake Postgres:

• Authentication is centralized
• Permission models are unified
• Auditing and lineage follow a consistent approach

This simplifies compliance with standards such as SOC 2 and GDPR while reducing operational burden.

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Real-World Context: Why the Split Existed

Many modern companies follow a familiar pattern:

• Postgres for transactions
• Snowflake for analytics

This design exists for good reasons. Companies such as DoorDash, Canva, and GitLab have documented how separating OLTP and OLAP systems in some or the other way has allowed them to scale without compromising application performance and their details are shared as follows:

1. DoorDash

DoorDash operated a massive multi-sided marketplace (merchants, dashers, and consumers). They use a fleet of PostgreSQL databases to handle the high-concurrency transactional load of order placements and real-time status updates, while centralizing all business logic and long-term trends in Snowflake.

• Use Case: Their transactional Postgres instances handle millions of "hot" updates per second (e.g., a dasher’s GPS coordinates or an order status change). This data is then streamed into Snowflake to power their "Customer 360" view, allowing them to perform complex cohort analysis and marketing personalization that would be impossible on a live production database.
• https://careersatdoordash.com/blog/how-doordash-transitioned-from-a-monolith-to-microservices/
• https://www.scribd.com/document/703145385/doordash-maximizes-the-power-of-data-and-bi-with-snowflake#:~:text=Bi%20With%20Snowflake-,DoorDash%20needed%20to%20maximize%20the%20power%20of%20its%20data%20to,recommendations%2C%20and%20understand%20market%20impacts.

2. Canva

Canva, the global design platform, serves over 170 million monthly active users. They famously transitioned from a fragmented data stack to a unified one, keeping PostgreSQL as the source of truth for user accounts and design metadata, while moving all analytical heavy lifting to Snowflake.

• Use Case: Canva uses Postgres for "Point Lookups" (e.g., when a user logs in and needs to see their specific designs instantly). For their analytical side, they use Snowflake to run millions of experiments and train ML models that suggest design elements to users. They specifically value the separation of storage and compute in Snowflake to handle massive data spikes during feature launches.
• https://blog.quastor.org/p/the-architecture-of-canva-s-data-platform-b20a

3. GitLab

GitLab is a "Postgres-first" company; their entire application is built on top of a highly optimized PostgreSQL backend. However, for internal business intelligence, financial reporting, and product usage analysis, they maintain a "paved path" that replicates this data into Snowflake.

• Use Case: GitLab’s production environment is one of the world’s most complex Postgres deployments (using tools like Patroni for high availability). They use a pipeline to extract data from these production databases into Snowflake so that their "Data Team" can analyze user behavior across millions of repositories without ever risking a slowdown of the GitLab.com application.
• https://handbook.gitlab.com/handbook/engineering/architecture/abstractions/postgresql/
• https://handbook.gitlab.com/handbook/enterprise-data/platform/snowflake/

These examples show why the split became necessary as data usage matured. Snowflake Postgres does not invalidate these architectures—it offers an alternative when tighter integration between operational data, analytics, and AI becomes valuable.

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Trade-Offs (Because There Are Always Trade-Offs)

Snowflake Postgres is not a universal solution.

It does not mean:

• Every OLTP workload should move into Snowflake
• Traditional Postgres deployments will become obsolete
• Existing architectures need immediate rewrites

For applications with strict latency requirements or deeply embedded infrastructure, standalone Postgres will continue to make sense.

What Snowflake Postgres does offer is choice. For data-heavy products where operational and analytical workloads are tightly linked, teams can now design systems with fewer boundaries and fewer moving parts.

Good architecture is not about following trends. It is about understanding constraints, evaluating trade-offs, and choosing the setup that introduces the least friction for the problem at hand.

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Final Thoughts

Snowflake Postgres may look like a small feature today.

But in hindsight, it could mark the point where analytics platforms stopped being passive data stores and started becoming active system backbones—not by replacing existing systems, but by reducing the boundary between transactions, analytics, and AI.

That shift is subtle, practical, and worth paying attention to.

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