Snowflake has long been recognized as the champion of ELT (Extract, Load, Transform), providing streamlined and scalable solutions for transforming data where it lives — in the cloud. But with the introduction of Openflow, Snowflake is signaling a bold expansion into the world of ETL (Extract, Transform, Load), aiming to meet the growing complexity and diversity of modern data pipelines.
This post explores what Openflow is, how it fits into the data engineering ecosystem, and why this new feature matters — especially for those already using Snowflake or tools like Apache NiFi.
Overview
Openflow is Snowflake’s new dataflow orchestration engine, built on the powerful open-source project Apache NiFi. For those unfamiliar, NiFi is a popular, battle-tested orchestration tool that enables the automation of data flows between systems using a no-code, drag-and-drop interface. Openflow takes this proven engine and integrates it natively into the Snowflake platform.
What sets Openflow apart is its extensibility and power. It allows you to create sophisticated ETL ingestion flows and perform advanced data transformations before loading data into Snowflake. The diagram below shows an out-of-the-box flow for HubSpot and some of its transformation features. This is just one of many connectors available in Openflow.
Currently, Openflow is available on AWS and integrates directly into the Snowflake UI, allowing for seamless data pipeline management alongside your traditional Snowflake workflows.
Openflow and ETL
Openflow represents a significant expansion of features into the ETL space. Traditionally, Snowflake has focused on the ELT approach for data loading, which works especially well for structured data. However, ETL workflows make Snowflake more versatile for scenarios involving:
- Semi-structured and Unstructured streaming and micro-batch data ingestion
- Complex transformations
- Unstructured data processing
- Reverse ETL
Openflow’s early focus includes support for Change Data Capture (CDC) workflows for popular databases, such as SQL Server, PostgreSQL, and MySQL. It also comes with built-in connectors for Kafka, Kinesis, and other streaming services, making it a strong contender for real-time data engineering use cases. Semi-structured data is also a focus with pre-built connectors for Google Ads, AWS Ads, and HubSpot, among others.
Below, you’ll find several out-of-the-box, pre-built flow connectors.
That said, if your workflows are primarily SQL-based and you’re happy with your current ELT tools (e.g., dbt, Airbyte, Fivetran), Openflow might feel like overkill — at least for now.
Self-Hosted Apache NiFi
Openflow offers a strong alternative to self-hosting Apache NiFi. If your workloads are already on Snowflake, then you can take advantage of:
- Deep Snowflake Integration: Openflow lives inside your Snowflake UI and plugs into your existing security, logging, and governance models.
- Out-of-the-box connectors: Snowflake provides 20+ custom connectors, ready to use without manual configuration, that can handle more complex transformations for you.
- Enhanced transformation capabilities: You can design complex ETL flows that handle unstructured or semi-structured data natively.
- Extendable using NiFi primitives: You’re not locked in — you can still use custom NiFi processors and design flows using familiar patterns.
These features enable you to ingest your data faster and achieve a faster time to value. With the Openflow BYOC (bring your own containers) feature, you can even self-host Openflow easily with the provided CloudFormation template that will deploy the Openflow infra via Terraform. You can see the CloudFormation template below.
Who Should Consider Using Openflow?
Openflow isn’t for everyone — but for some teams, it’s a game-changer. You should consider piloting Openflow if:
- You work with diverse data sources that need transformation before landing in Snowflake.
- You are currently running Apache NiFi on-prem or on Cloudera — moving to Openflow could dramatically simplify your stack.
- You manage an existing NiFi cluster and want to explore hybrid or federated pipeline orchestration via Snowflake.
- Your team is not deeply versed in scripting or Python and would benefit from a no-code pipeline design.
- You need to support reverse ETL workflows and want a single tool that can support both data ingestion and export.
Early Impressions & Takeaways
As part of the public preview, we’ve had the chance to experiment with Openflow — and here’s what stood out:
- No-code ETL is truly powerful. For SQL-focused teams, Openflow makes it dramatically easier to build complex pipelines without needing to write a single line of Python.
- Documentation is still a work in progress. The preview phase naturally brings some rough edges in certain areas.
- BYOC (Bring Your Own Containers) is required. Today, Openflow must be deployed using a Snowflake-provided CloudFormation template, which spins up an EKS cluster managed via Terraform. While that’s convenient for testing, your infrastructure team may want to manage this differently in a production environment. For example, in our deployment, you could imagine there would be potential cost savings if, for non-critical runtimes, you ran spot instance nodes instead of on-demand nodes.
- Potential for unstructured workflows. One of the most exciting possibilities is utilizing Openflow to ingest and process images, PDFs, audio, and other unstructured data, thereby opening up new use cases for ML and AI workflows in Snowflake.
Final Thoughts
Snowflake Openflow is just getting started, but it could reshape how teams build ETL workflows within the Snowflake ecosystem. For teams already using NiFi, this could be a no-brainer migration. For others, it’s a compelling new tool to keep an eye on — especially as Snowflake continues to expand into unstructured and real-time data processing.
We’re excited to see how Openflow evolves and how it helps data teams unlock new use cases in the Data Cloud.
Want to talk about whether Openflow is right for your stack? Contact us — as a Snowflake partner, we can help you evaluate, pilot, and scale it the right way.
