This blog post is Human-Centered Content: Written by humans for humans.

Building robust, automated ingestion pipelines often means stitching together cloud storage, monitoring tools, ETL jobs and error handling logic, all living outside your data warehouse. With Snowflake Openflow, ingestion becomes native, event-driven and easier to manage, all from within Snowflake itself.

Today, we’ll cover what Openflow is, how it works and whether it’s right for you. Here are the sections we’ll cover:

• What is Openflow?
• How is it Different From Snowpipe?
• How Exactly Does it Work?
• Is it a Good Option for You?
• Cost and Agility
• When a Hybrid Approach Makes Sense

What Is Openflow and How Does it Work?

Snowflake Openflow is a fully managed, native ingestion framework that makes it easier to get data flowing into your Snowflake environment and just as importantly, to actually see what’s going on when something breaks. You can pull in files from cloud storage like S3, Azure Blob, or GCS, or connect directly to sources like PostgreSQL, Kafka, MySQL, Kinesis, and many, many more. The list keeps growing, so check out the Openflow connectors | Snowflake Documentation to see what’s available today. And best of all? You don’t need any third-party orchestration tools or external compute to make it all work.

Now, just in case you skimmed past that a bit too fast, let me say it again: Openflow is the first truly native way to bring data into Snowflake directly from operational systems and other sources, not just flat files sitting in cloud storage. That means no extra services just to stage your data. No glue code. Just straight from source to Snowflake, all within the platform. Okay, now let’s start with some broad benefits, and keep narrowing down into specifics. 

Openflow turns ingestion into something that’s:

• Declarative: You define ingestion logic with SQL or GUI, not Python scripts or DAG. This means you Say WHAT you want done, not HOW to do it.
• Observable: You can gain instant visibility into pipeline status, file history and errors directly within Snowflake both through the UI and via SQL metadata queries.
• Scalable: It can handle both batch and streaming data without switching tools.
• Flexible: Capable of ingesting not just structured and semi-structured files in internal or external stages, but also connections to relational databases and event streams.

For those who monitor and create pipelines from source to Bronze Layer, this isn’t just a minor convenience. Instead of juggling more tools, cloud services and credentials just to get data loaded, Openflow lets you stay inside one platform, move faster and reduce maintenance. This is not the be-all end-all, and there is certainly still a use for other ingestion tools — an important topic we will touch on a bit later.

That being said, we went over some high level traits of Openflow. To get a bit more specific, here’s what Openflow specifically brings to the table:

• Support for both batch and streaming ingestion workflows from the Cloud
• Prebuilt connectors for sources like PostgreSQL, MySQL, Kafka, and Kinesis and many more Openflow connectors | Snowflake Documentation
• Automatic schema evolution that adapts to changing source data structures
• Built-in observability through the Snowflake UI as well as our familiar SQL functions

To put it simply, compared to external tools integrating with Snowflake, Openflow lets your team do more faster with less complex infrastructure, all while maintaining clear insight into what’s working (and more importantly, what’s not). You might be asking, “Ivan, this seems nice and all, but it sounds to me like you’re describing just another option for streaming. Doesn’t Snowflake already have something for this?” Fantastic question. Read on! 

How Is This Any Different from Snowpipe?

Many of you already use Snowpipe and may be wondering whether Openflow is worth a closer look. Snowpipe simplified file ingestion by enabling continuous, event-driven loading from cloud storage.

Openflow builds on that foundation. Where Snowpipe focuses narrowly on file-based streaming ingestion, Openflow supports both batch and streaming patterns and extends source compatibility to systems like those 3rd party connectors we’ve mentioned a few times. These are not things Snowpipe can currently do. It also introduces powerful features like inline transformations, automatic schema drift handling (no more updates to your COPY INTO statements) and monitoring within the Snowflake UI on top of our regular SQL query monitors.

This evolution was catalyzed by Snowflake’s acquisition of Datavolo, a platform built by the creators of Apache NiFi and designed for flexible, high-velocity data integration across all modalities. As Snowflake put it:

“With Datavolo, Snowflake will deliver a fully managed, extensible and open data engineering platform that offers end-to-end support for every data, workload and deployment model. We’re excited to bring these powerful capabilities to our data engineering community in the coming months, and we can’t wait to see what you build on this powerful platform.”

If you’d like to read the full article regarding from Snowflake, take a look here: Snowflake to acquire Datavolo to boost multimodal data integration capabilities.

For those think better in terms of rows and columns, here’s a quick comparison to summarize how Openflow stands apart from Snowpipe:

I hope this shows you that Openflow isn’t just a next-gen Snowpipe. While it may seem that I have been singing its praises this whole time, I do not think that Openflow is trying to be a one-size-fits-all ingestion solution. Extensive transformations, highly specialized APIs and connectors to 3rd party services not already included will still warrant the need for other tooling. However, it cuts down infrastructure overhead where it is not absolutely necessary, and frees up engineering time to focus on those tougher projects.

How Exactly Does it Work? 

Now that you have a better understanding of WHAT it actually is and how it relates to existing Snowflake and alternative service, we can take at a high level look at how it all works.

The pre-reqs: 

1. A Snowflake account with image repositories set up
2. Cloud (AWS for example) with ability to create a Cloud formation stack + ability to set up EKS inside a new or existing VPC

And the high level look:

• Snowflake Role setup: you will want to configure your runtimes, deployments and image repository if you haven’t already, along with a suitable Openflow admin role that has access to all of these. This will be important for maintaining security and avoiding rogue pipelines.
• Source setup: You configure an external stage pointing to a cloud bucket (e.g., S3, Azure Blob). Openflow monitors this stage for new files. This will require some initial cloud resource provisioning.
• Pipeline definition: You use a CREATE PIPELINE SQL statement to define:
  • The source stage and file format
  • The destination table
  • Optional transformation logic
  • Set up trigger method (cron or event-driven)
• Event integration: With AUTO_INGEST set up in the trigger method, Snowflake integrates with cloud-native event systems (like S3 event notifications) to trigger ingestion automatically when new files arrive. This is exactly like in Snowpipe.
• Networking requirements: Snowflake must be granted secure access to your storage, typically via:
  • IAM roles and trust relationships (for AWS)
  • Optional use of VPC endpoints for private access
  • If you are already integrated with a cloud, this will likely be done already
• CloudFormation support: Snowflake provides CloudFormation templates to automate setup on AWS including notification configs, IAM roles and event routing.
• Serverless ingestion: When triggered, ingestion is handled by Snowflake-managed compute, billed per second. No warehouses (at least in the traditional sense) or external jobs required.
• Built-in observability: You can track file history, pipeline status and errors directly via the UI or similarly to Snowpipe via SQL functions like COPY_HISTORY() and SYSTEM$PIPE_STATUS().

Is Openflow a Good Option for You?

When choosing the right data ingestion strategy, it’s not just about pure features. It’s about what makes the most sense for your team’s capabilities, your cost model and how quickly you need to deliver insights. Like any tool, it fits best in the right context. That being said, there are a lot of things to be excited about if you are already invested in the Cloud.

Cost and Agility Advantages

If you’re already using Snowflake as your primary data platform, Openflow fits neatly into your existing cost structure. It follows the same usage-based pricing model as the rest of Snowflake, so you only pay for the compute and storage you actually use, billed through Snowflake credits. There are no separate ingestion service contracts, no row-based pricing and no additional platform licensing fees.

While you may still incur modest cloud infrastructure costs such as event notifications or storage access fees from S3 or Azure these are comparable to what you’d expect when supporting pipelines in third-party ingestion tools.

The cloud resources are only part of the picture, and here’s where Openflow can offer a significant cost-saving opportunity: most third-party ingestion tools still rely on Snowflake (and as a result, Snowflake credits) for the heavy lifting. They often stage data in cloud storage and orchestrate COPY INTO operations, which means they’re consuming Snowflake credits in the background. But on top of that, they typically charge a separate licensing fee for their platform.

And here’s another benefit that might not show up on a feature list: using Openflow could help you skip the lengthy procurement and approval cycles that come with introducing a new third-party ingestion tool. If your organization already has Snowflake approved (which you likely do if you’re reading), you don’t need to push a new vendor through legal, security or budgeting hoops just to get data flowing. That can mean weeks saved before your first row even lands.

Once again, for those who prefer neat bulleted lists:

• Lower infrastructure overhead: Pipelines run on Snowflake-managed compute, so there’s no need to manage external pipelines for supported sources.This means faster time to stand up.
• Fewer tools for simple use cases: You won’t need to wire up Fivetran, Airflow, Lambda, or custom scripts just to ingest files or data from supported connectors.
• Faster development cycles: Pipelines are defined in SQL in a declarative manner, which means familiar tools and languages for fast workflows.
• Less context-switching: Staying within Snowflake for ingestion reduces friction and speeds up creation of new pipelines. Your engineers will thank you for this. 
• Predictable pricing: You’re billed based on actual usage. You don’t double dip on licensing and Snowflake credits, which is an obvious win.

As we’ve already mentioned, Openflow is not designed to replace full-fledged orchestration or transformation layers, and you shouldn’t try to force it into that role. Instead, it shines when used as the ingestion backbone within a modern data architecture.

One moment please, we just got in some interesting news… and would you look at that, Snowflake thought of something to perfectly compliment this lack of a robust transformation layer in Openflow. As someone who works mainly in the transformation layer myself, I was stoke to hear Snowflake’s announcement this year with the introduction of Workspaces, which bring native support for dbt projects directly within the Snowflake platform. This creates a compelling all in one package, end-to-end workflow: Openflow handles ingestion with minimal setup and zero third-party licensing, while Workspaces orchestrate dbt to manage robust, and all this in the same application. 

While we personally have not tested the full picture in production quite yet, it’s definitely something to watch in the coming weeks. If everything fits together the way we expect, the impact could be significant: faster engineering cycles, less context-switching and more consolidated (and likely reduced) billing.

That said, you might be perfectly comfortable with your current setup and not ready to move both ingestion and transformation entirely into Snowflake. The good news is that Openflow can still add value in a hybrid approach, whether you pair it with another ingestion tool or stick with your existing transformation stack outside of Workspaces.

When a Hybrid Approach Makes Sense

In many cases, the most practical and cost-effective solution is to use Openflow alongside existing ingestion and transformation tools. There are also cases where Openflow provides features that you don’t find yourself needing. For example, if you are already using Snowpipe and don’t require the ability to parse and have total control over your error handling, and you don’t plan on utilizing custom connectors or working with dynamic schema files, the incentive to make that shift quickly goes away. That being said, here are some situations you may want to consider when deciding if Openflow can replace or integrate well with your current setup:

• Diverse source types: While Openflow now supports files, PostgreSQL, MySQL, Kafka, Kinesis, etc., you’ll still need other tools for:
  • SaaS APIs (Salesforce, Stripe, etc.)
  • FTP or legacy systems
  • Unstructured sources without connectors
  • Tools like Fivetran, Airbyte or custom scripts may still be necessary in these cases.
• Complex transformations: Openflow supports light bronze layer transformations via COPY INTO, SELECT, etc., but isn’t suited for:
  • Multi-step logic
  • Joins, merges or deduplication
  • Dependency-aware data modeling
    These are better handled with tools like dbt or Matillion downstream
  • Could use Workspaces, but this is still relatively new
• Mixed tooling environments: Your team may already use orchestration platforms like Airflow or Data Factory. Openflow can:
  • Simplify new ingestion workloads
  • Reduce cost and maintenance for file-based or supported sources
  • Fit cleanly into existing pipelines without disruptions

Whether you’re looking to cut down on your assortment of tools, or just make things a little less painful for your engineers, Openflow is a native and pragmatic way to bring more of your data pipeline under one roof, and is only going to get more powerful over time. If you’re already using Snowflake, it’s worth exploring where Openflow can help you work more efficiently without having to re-architect your entire stack. For a full set of documentation for you to explore more in depth we’ve linked it here so you can start toying around. Bring on the age of integrated ingestion!