Openflow vs. dlt for Snowflake users
Adrian Brudaru,
Co-Founder & CDO
As a data platform manager, you architect the operational model for your organization. The choice between Snowflake Openflow and an open-source library like dlt is not an either-or. Each tool has distinct strengths and weaknesses, making them more complimentary than competing, being suitable for different use cases.
This article analyses the two distinct paradigms these tools represent, helping you make the right strategic investment for your team.
The TL:DR
What’s Openflow?
Openflow is a GUI-based tool for data ingestion built on Apache NiFi technology. It provides an interface for creating EL workflows inside Snowflake, aligning with the company’s goal of enabling AI and data processing “without leaving Snowflake.”
When to use Openflow?
Openflow is a good choice when your data sources are among the 20 supported sources, and there is no need for customization. It is best suited for organizations with a strong DevOps (Terraform) team.
What’s dlt?
dlt is an open source python library created by dltHub, that enables core data teams to self-serve with data like senior data engineers. dlt provides python beginners a simple way to create robust custom code pipelines quickly, while dltHub provides thousands of LLM contexts for fast development and various devtools to empower your team.
When to use dlt?
dltHub is a good fit when you need more than 20 supported data sources or require customizations. It is best suited for core data teams that use Python and productivity gain tools such as Cursor.
The core difference between Openflow and dlt lies in the type of team and workflow they enable.
Technology Comparison
General
| Openflow (NiFi) | dltHub | |
|---|---|---|
| Interface | Canvas GUI only (but inside the Snowflake web UI) | Python code; GUI planned for the end of next year |
| What's in the box | 20 sources in GA maintained by Snowflake |
|
| Who can deploy it | Strong DevOps (Terraform) team should manage Amazon Elastic Kubernetes on AWS (until hosted Snowflake is available) | Any Python engineer |
| AI-support | No support currently | Integrates with productivity tools like Cursor, Copilot, Claude Code, etc. |
| Customization | Limited to not available | Full customization with code ownership |
Deployment & Performance
| Openflow (NiFi) | dltHub | |
|---|---|---|
| Performance | Execution model processes one record at a time and is single-threaded. Tuning requires NiFi-specific knowledge. Some early users report performance as a deal breaker for Apache NiFi. | Good default parameters leveraging parallelization and async in Python. All settings can be tuned by a Python developer. |
| Deployment | BYOC (Bring your own compute): Terraform file to deploy a NiFi cluster on AWS. Requires an EKS (Kubernetes) cluster. Hosting on Snowflake infrastructure is on the roadmap. | Works with any orchestration tool of your choice (e.g., Prefect). |
| Logging | Not supported currently. You have to write “logs” to a real data table; otherwise, you need to log into AWS, access your EKS pod, and read logs (received poorly by the community). | Regular Python logging; access to metrics and traces through Python code. dlt Dashboard provides deep introspection of runs. |
| Managing multiple workflows | Several NiFi flows can cause a “noisy neighbor problem,” where a workflow hogs resources, resulting in throttling. The issue should be solved at the Kubernetes cluster level by your DevOps team. | Optimize at the Python process level; can launch jobs on separate machines for CPU / RAM / disk access; managed by dltHub. |
Other parameters
| Openflow (NiFi) | dltHub | |
|---|---|---|
| Version control | The .json file describing the entire workflow can only be versioned outside Snowflake. You need to add nodes to do HTTP requests to download from GitHub. | Same as any regular Python code with .py files. |
| Monitoring UI | You can see steps updating in real time and view data records as they are loaded in the Snowflake web UI. | Use your orchestrator’s UI for monitoring. |
| Runtime language | Java; troubleshooting requires debugging the underlying Java code. | Python |
| Education | Limited, small community | Courses, blogs, Slack; LLMs are good at it—by dltHub and community. |
Strategic context
The Story Behind Openflow: An Integrated Vision
In late 2024, Snowflake acquired Datavolo, bringing their expert NiFi team and user-friendly GUI into the fold. This technology was then integrated into the ecosystem and introduced as Openflow.
Openflow is positioned as Snowflake's native solution for data ingestion, especially for AI tasks like building chatbots that can read PDFs with Cortex AI. The goal is to create a seamless experience right within Snowflake. To help customers transition, Snowflake is partnering with Squadron Data, bridging the gap between existing systems and the new Openflow environment.
This move is a clear signal of Snowflake's strategy: to offer an integrated, low-code tool for ingestion and AI, creating a more unified platform story, similar to what competitors like Databricks are doing with Lakeflow.
The dltHub Approach: A Developer-First Framework
dltHub is the first integration tool purpose-built on a different philosophy: empowering the developer through an open, code-native framework. As an open-source Python library, it is designed from the ground up to integrate seamlessly into the existing data workflows.
Pipelines are defined in Python, meaning they can be version-controlled with Git, tested like any other software, and customized without limitation. This approach grants teams full ownership and control.
The strategy is centered on accelerating developer productivity. Developers can use AI coding assistants to rapidly scaffold new connectors, which they can then customize and verify for their own use. This combines the speed of AI with the reliability of human oversight, making it faster than ever to build robust, custom pipelines. The strength of the community is found in its active support channels, comprehensive educational resources, and the collaborative nature of open-source development.
Strategic Outlook
Openflow: A Foundation for the Future
Openflow represents an important step in Snowflake's strategy to create an all-in-one platform. It provides a visual, GUI-driven approach for users to design and manage data pipelines, particularly for new and exciting Cortex AI use cases.
In its current form, the platform is best suited for teams who value a visual workflow and have the dedicated DevOps resources to manage the underlying infrastructure.
Unless Snowflake rebuilds Openflow into a serverless, Python-extensible system, it will remain a niche, high-maintenance GUI tool primarily used to demo Cortex AI pipelines. If Snowflake delivers a fully hosted and serverless version, Openflow could be significantly improved in usability and adoption.
dltHub: Empowering Engineers to Scale
dltHub's future is focused on building upon its developer-centric foundation. Its strategic advantage lies in its inherent flexibility and extensibility. Because it's a code-based library, it scales as engineering teams grow in sophistication.
The roadmap includes building a collaborative ecosystem where the connectors that developers create can be more easily shared and discovered, creating a powerful network effect. This, combined with plans for a GUI to improve accessibility, positions dltHub as the go-to choice for teams who prioritize control, customization, and long-term maintainability in their data infrastructure.
Conclusion: Better Together in a Diverse Ecosystem
Openflow and dltHub represent two distinct but valuable visions for the future of data ingestion.
Openflow is Snowflake's commitment to a self-contained ecosystem aiming to provide a tightly integrated, visual solution within its ecosystem. As it evolves, it will become a powerful choice for teams deeply invested in the Snowflake platform.
dltHub, on the other hand, champions an open, developer-centric approach. It empowers engineers with the flexibility of code, accelerated by AI, allowing them to build custom, robust pipelines with full ownership. Its strength lies in its extensibility and vibrant community support, with a clear vision for fostering even deeper collaboration in the future.