Openflow is not just a new feature – it’s a shift in how we think about data integration and delivery, especially for AI-native applications that need to interact with large volumes of real-time data.

As a solution architect, I’ve seen many waves of innovation in the data and cloud space. But occasionally, a platform introduces something that makes you sit up and think, “This is going to change how we build with Snowflake.” Snowflake’s Openflow is exactly that kind of leap.

What is Snowflake Openflow?

Openflow is a managed data integration framework built on Apache NiFi that enables developers to build event-driven, data-centric pipelines within the Snowflake ecosystem. It is designed to connect any data source to any destination using a rich set of processors and connectors, supporting both structured and unstructured data.

You can think of it as Snowflake’s way of giving you the building blocks – such as Openflow processors, connectors, and controller services – to create smart, responsive data flows that can:

  • Ingest and process unstructured data (e.g., from Google Drive, SharePoint, Box) for AI applications built using Snowflake Cortex
  • Replicate change data capture (CDC) from databases into Snowflake for centralized reporting
  • Ingest real-time events from streaming platforms (e.g., Apache Kafka) for near real-time analytics
  • Integrate with SaaS platforms (e.g., LinkedIn Ads, Salesforce) for marketing analytics and insights
  • Build and orchestrate data flows using Snowflake and NiFi-native components within a governed environment.

Openflow abstracts much of the infrastructure complexity. In BYOC (Bring Your Own Cloud) or on-prem deployments, customers are responsible for provisioning and managing their own data planes, while Snowflake provides a centralized control plane for monitoring and governance via Snowsight.

In contrast, when using Snowpark Container Services (SPCS), Snowflake manages both the control and data planes, offering a more streamlined, fully managed experience – ideal for teams looking to reduce operational overhead and accelerate deployment.

Perspective as a solution architect

When I look at Openflow, I don’t just see a technical feature – I see a shift in how we approach data integration.

Traditionally, developers often had to move data out of their core data platforms into external systems for processing or integration – introducing latency, complexity, and governance risks. While Openflow still involves data movement, it enables integration workflows to be designed and executed via the Snowflake ecosystem itself, reducing the need for external integration      tools.

This allows teams to deliver data to operational systems, trigger actions based on data changes, and streamline last-mile delivery – all while maintaining control, observability, and governance through Snowflake’s unified control plane in Snowsight.

  • Use prebuilt and custom connectors

Openflow includes 30+ prebuilt connectors for SaaS applications, databases, file systems, and streaming platforms (e.g., Kafka, SharePoint, Google Drive), enabling rapid integration with common enterprise systems.

For more specialized needs, developers can extend Openflow by building custom processors using Apache NiFi components. However, it’s important to note that this requires familiarity with NiFi development practices, and such custom components will be supported by Snowflake. Organizations should evaluate supportability and maintenance implications before adopting custom extensions.

  • Deploy in flexible environments
  • Bring Your Own Cloud (BYOC): Deploy Openflow within your own virtual private cloud (VPC) across cloud providers (AWS, Azure, and GCP     ). This model gives you full control over the data plane, while Snowflake manages the control plane for centralized governance, monitoring, and orchestration via Snowsight.
  • Snowpark Container Services (SPCS): For teams seeking a fully managed experience, Openflow can be deployed using Snowflake-managed infrastructure through SPCS. In this model, Snowflake manages both the control and data planes, simplifying operations and accelerating time to value. This is ideal for organizations that want to reduce infrastructure overhead while maintaining governance and observability.
  • Hybrid deployments: Openflow also supports hybrid environments, allowing organizations to mix BYOC and SPCS deployments based on workload, data locality, or compliance needs – all governed through a unified control plane.
  • Integrate with Snowflake Cortex for AI
  • Snowflake Openflow enables seamless integration with Snowflake Cortex, allowing developers to build intelligent, AI-powered data pipelines.Openflow supports the ingestion and transformation of multimodal data – including text, images, audio, and more – from sources like SharePoint, Slack, Box, and Google Drive.
  • Using built-in processors, Openflow can invoke Snowflake Cortex models to parse, pre-process, and enrich unstructured data directly within the pipeline.
  • These capabilities support use cases such as document classification, OCR, chat with your data, and real-time AI decision-making – all within a governed and observable environment.
  • Governance and observability

Snowflake Openflow is built with enterprise-grade security and observability features to ensure that data pipelines are secure, governed, and transparent across all deployment models.

  • Role-Based Access Control (RBAC): Openflow adheres to Snowflake’s RBAC model, allowing fine-grained control over who can access, modify, or execute data flows and associated resources.
  • Encryption and secrets management:
    • All data in transit is encrypted using TLS.
    • Openflow supports integration with multiple secrets managers, including AWS Secrets Manager, Azure Key Vault, and HashiCorp Vault, for securely managing credentials and keys.
    • Secrets are encrypted using Snowflake’s hierarchical key model, which includes root, account, table, and file-level keys, and supports Tri-Secret Secure for enhanced protection.
  • Observability and monitoring:
    • Openflow provides real-time monitoring, DAG (directed acyclic graph) visualization, and alerting capabilities.
    • These capabilities ensure that Openflow pipelines are not only powerful and flexible but also secure, auditable, and compliant with enterprise governance standards.

Developer workflow example

To illustrate how developers can get started with Snowflake Openflow, let’s walk through a simple yet realistic example of building a data pipeline that ingests, transforms, and loads data into Snowflake – all within a governed and observable environment.

This example assumes you’re building a pipeline to process customer feedback data for sentiment analysis using Snowflake Cortex.

Step-by-step workflow

  1. Set up Openflow in your environment
    Deploy Openflow using one of the supported models:
    • BYOC (Bring Your Own Cloud): Provision Openflow in your own VPC (AWS, Azure, or GCP) using a managed Kubernetes cluster.
    • SPCS (Snowpark Container Services): Use Snowflake-managed infrastructure for a fully managed experience.
  2. Create a deployment and runtime
    In Snowsight or via SQL:
    • Deployment integration: Defines the infrastructure (e.g., Kubernetes cluster) where Openflow will run.
    • Runtime integration: Specifies the execution environment for your data flows.

These are created using:

   CREATE OPENFLOW DATA PLANE INTEGRATION …

   CREATE OPENFLOW RUNTIME INTEGRATION …

  1. Design the data flow
    Use the Openflow UI’s drag-and-drop canvas to build your pipeline:
    • Add a GetFile or GenerateRecord processor to simulate or ingest customer feedback data.
    • Use QueryRecord to filter or enrich the data (e.g., remove nulls, extract keywords).
    • Add a Cortex Processor to invoke a sentiment analysis model from Snowflake Cortex.
    • Use PutDatabaseRecord to write the results into a Snowflake table.
  2. Configure controller services
    Set up services like SnowflakeConnectionService to securely connect to your Snowflake account using secrets stored in AWS Secrets Manager, Azure Key Vault, or HashiCorp Vault.
  3. Run and monitor the flow
    Execute the flow from the UI and monitor it using:
    • DAG visualization to understand flow logic
    • Real-time logs and metrics for performance insights
    • Alerts for failure or SLA violations.

 How Openflow helps industries: Real-world use cases:

  1.      Retail and e-commerce: Real-time inventory optimization
         use case:

A retail company wants to automate inventory replenishment to avoid stockouts. Inventory data from multiple sources – such as ERP systems, warehouse databases, or IoT shelf sensors – is ingested into Snowflake using Openflow. When stock levels fall below a defined threshold, Openflow triggers a downstream workflow that:

  1. Calls a replenishment API to initiate restocking
  2. Sends a Slack alert to notify the warehouse or procurement team
  3. Updates a Snowflake table with the new inventory status, which feeds into a real-time BI dashboard.

How Openflow enables this:

  • Data ingestion: Openflow connects to source systems (e.g., via JDBC, REST APIs, or file drops) and ingests inventory data into Snowflake.
  • Flow logic: A QueryRecord processor evaluates stock levels against thresholds.
  • Triggering actions: An InvokeHTTP processor calls the replenishment API, and a PutSlack processor (or custom webhook) sends alerts.
  • Governance: All actions are logged and monitored via Snowsight, ensuring traceability and compliance.

Why it matters:

This approach enables real-time inventory visibility and automated restocking using data already centralized in Snowflake. It reduces the risk of out-of-stock scenarios, improves operational efficiency, and eliminates the need for external orchestration tools – all while maintaining governance and observability.

2. Financial services: Fraud detection and alerting

Use case:     

A financial institution wants to detect and respond to suspicious transactions in real time. Transaction data is continuously ingested into Snowflake from payment systems or banking applications using Openflow. When a pattern indicative of fraud is detected – such as rapid withdrawals or unusual geolocation activity – Openflow triggers a workflow that:

  1. Invokes a fraud scoring model (e.g., via Snowflake Cortex or an external API)
  2. Flags the account by updating a status field in a Snowflake table
  3. Sends a case notification to the risk investigation team via email, Slack, or a case management system.

How Openflow enables this:

  • Streaming ingestion: Openflow can ingest real-time transaction data from Kafka, REST APIs, or file drops.
  • Pattern detection: A QueryRecord processor or custom logic evaluates transactions against fraud rules or thresholds.
  • Model invocation: An InvokeHTTP processor can call a fraud scoring API or trigger a Cortex model.
  • Alerting and routing: Based on the score, Openflow routes the flagged transaction to the appropriate team or system.

Why it matters:

This setup enables automated, real-time fraud detection without duplicating data or relying on external orchestration tools. It reduces fraud losses, accelerates response times, and ensures that all actions are governed and observable within the Snowflake ecosystem.

Security, governance and observability built-in
One of my favorite aspects is that Openflow doesn’t bypass Snowflake’s governance, access control, or auditing. You get end-to-end visibility into what’s happening, who triggered what, and how your data is being used – which is critical for industries like finance, healthcare, and government.

Additionally, Openflow offers robust observability features, including logging, metrics, and alerting, which help teams monitor pipeline health and performance.

Whether you’re deploying across multiple data planes – in Snowflake-managed environments, BYOC, or on-prem – everything is centrally managed and monitored through a unified Control Plane in Snowsight, ensuring consistent governance and operational oversight.

Final thoughts

As a solution architect, I’ve worked with many data platforms – but Snowflake Openflow stands out as a transformative capability for building AI-native, event-driven applications with Snowflake.

Openflow lowers the barrier to enterprise-grade data integration by enabling teams to connect, transform, and deliver data across systems – all within the governed, secure, and scalable Snowflake ecosystem.

By enabling real-time, AI-driven decision-making, Openflow empowers organizations to act on data as it arrives – whether it’s triggering a fraud alert, replenishing inventory, or routing insights to business applications.

If you’re building modern data or AI applications, Openflow deserves your serious attention. The future is event-driven, AI-native, and data-centric – and Openflow is emerging as a powerful foundation for building that future with agility, control, and confidence.