by Trent Foley
The evolution of data engineering practices has ushered in an era where flexibility, rapid ingestion, and governance are paramount. As businesses look to streamline data integration while preparing for AI-driven workloads, Snowflake’s Openflow—built on Apache NiFi—is stepping into the spotlight.
What is Snowflake Openflow?
Openflow, Snowflake’s managed solution based on the Apache NiFi engine, represents the next frontier in modern ETL. Introduced in 2024 following Snowflake’s acquisition of Datavolo, Openflow offers a native, enterprise-grade ingestion platform integrated directly into the Snowflake ecosystem. Openflow separates its architecture into a Snowflake-managed control plane and a deployable data plane, offering:
- Deployment flexibility through BYOC (Bring Your Own Cloud) or Snowpark Container Services (SPCS)
- Support for all data types, including structured, semi-structured, streaming, and unstructured data
- Hundreds of prebuilt connectors, including sources like SharePoint, SQL Server, Google Drive, Kafka, and Meta Ads
- AI-ready pipelines that feed data into Snowflake Cortex and other LLM-based tools
This makes Openflow a formidable solution for organizations looking to build secure, observable, and scalable ingestion pipelines. Openflow is already demonstrating value in production environments:
- CDC and Replication from SQL-based systems into Snowflake
- Ingestion of Rich Unstructured Content from sources like Box and SharePoint for AI and analytics
- Streaming Analytics Pipelines using Kafka or Kinesis, feeding real-time dashboards and models
- SaaS Integrations for marketing and sales analytics, e.g., LinkedIn Ads or Salesforce
These capabilities empower teams to build pipelines that not only serve operational needs but also feed into cutting-edge AI workloads.
The Modern Data Engineering Workflow
Modern data engineering workflows demand far more than data ingestion and transformation. They encompass robust version control, automated testing, continuous integration and deployment, monitoring, and governance—essentially applying software engineering discipline to data workflows. This paradigm, often referred to as DataOps, integrates:
- Source Control Management (SCM): All pipeline definitions and configurations are stored in Git or equivalent repositories.
- CI/CD Pipelines: Automated deployment of data workflows ensures consistency across dev, test, and prod environments.
- Observability and Monitoring: Integrated logging, metrics, and alerts ensure data pipelines are reliable and recoverable.
- Environment Parity: Workflows are tested and deployed consistently across multiple environments.
- Collaboration and Governance: Teams can work concurrently on pipeline components while meeting security and compliance standards.
Platforms like Openflow make this level of rigor achievable by merging the flexibility of visual design with the power of Git-based development and cloud-native deployment.
How to Fit NiFi into the Modern Data Engineering Workflow
Pairing NiFi with the NiFi Registry and Git repositories enables versioning of Process Groups directly from the NiFi UI. Flows are written as JSON bundles into Git, aligning data workflows with software development best practices. This promotes change tracking, rollback capabilities, and collaborative development among engineering teams. NiFi’s Git integration bridges a critical gap in DevOps for ETL/ELT pipelines, allowing flows to be developed, reviewed, and deployed using standard CI/CD workflows. This is particularly valuable for enterprises that require both agility and traceability.
Addressing Workflow Development Challenges
Developing data pipelines solely through a UI introduces complexity in change tracking, collaboration, and reusability. NiFi’s interface, while powerful, can suffer from “locking” issues and team inefficiencies. Hybrid approaches are increasingly common: visual tools like Openflow are used for rapid, scalable data ingestion, while custom code handles complex transformations. This balances velocity and maintainability, particularly for lean teams.
The size of the development team heavily influences the suitability of visual ETL tools. While single developers benefit from rapid prototyping in tools like Openflow, larger teams often face versioning and collaboration friction. Version control via Git, centralized flow registries, and YAML-based pipeline definitions are emerging as best practices to scale these tools for enterprise adoption. An opportunity exists for a YAML-based NiFi flow editor as an option for larger teams collaborating on integration projects. Such an option would mitigate “locking” and team collaboration limitations by enabling concurrent development and standardized approvals.
The ETL Comeback and Rise of Openflow
ETL is experiencing a renaissance, especially in AI-focused data architectures. Rather than relying on batch-heavy ELT models, many organizations are reverting to ETL workflows to pre-process and enrich data closer to the source. Openflow offers a strategic advantage in this movement by integrating ingestion into the broader Snowflake platform. It allows teams to simplify data acquisition without compromising security or scalability.
The Future of Managed ETL in the Cloud
Openflow represents a strategic pivot for Snowflake—from a focus solely on analytics to an end-to-end platform encompassing ingestion, transformation, AI, and governance. It unifies what was once fragmented: pre-ingest orchestration, metadata lineage, observability, and real-time data readiness. When combined with Cortex, Snowflake’s AI and LLM services, Openflow enables fully integrated AI agents powered by live organizational data. This removes the friction between ingestion and insight.
Final Thoughts
Openflow, built on Apache NiFi, is a powerful tool for building flexible, cloud-native data workflows. Snowflake enhances this foundation with enterprise-grade security, control, and native platform integration. For modern data teams—especially those navigating AI readiness, unstructured data, and governance—Openflow offers a future-proof option that blends the best of visual ETL design with modern DevOps and AI-first architecture. As ETL reasserts its importance in the age of AI, tools like Openflow are poised to be essential ingredients in every data engineer’s toolkit. At evolv, we help organizations unlock the full potential of Openflow by integrating it seamlessly into their Snowflake environments—accelerating AI initiatives, modernizing data pipelines, and enabling scalable, governed workflows from day one.
