Snowflake OpenFlow: Managed Data Integration Built on Apache NiFi

Data integration has always been one of the most challenging aspects of building a modern data platform. Organizations need to connect dozens of disparate sources—databases, SaaS applications, streaming platforms, file systems—each with unique protocols, authentication methods, and data formats.

Traditional approaches require deploying and managing complex infrastructure, writing custom integration code, or paying high premiums for proprietary connectors. Snowflake OpenFlow offers an alternative: a fully managed integration platform built on Apache NiFi that handles both infrastructure complexity and connectivity breadth.

What Is Snowflake OpenFlow?#

OpenFlow is Snowflake’s managed data integration service based on Apache NiFi, a proven open-source data flow management platform. It enables organizations to build visual data pipelines that move and transform data between sources and destinations without writing code or managing servers.

OpenFlow handles structured and unstructured data—text, images, audio, video, sensor data—in both batch and streaming modes. It provides pre-built connectors for common data sources and the extensibility to create custom integrations when needed.

Key Architectural Components#

Control Plane: Manages and monitors all pipeline runtimes through the OpenFlow service API. Users interact via the visual canvas interface in Snowsight or programmatically through REST APIs.

Data Plane: The processing engine that executes data pipelines. Runs within customer infrastructure (BYOC) or Snowflake’s Snowpark Container Services, isolating computational workloads from the control plane.

Runtimes: Host individual data pipelines, providing isolated execution environments with dedicated security, scaling, and monitoring capabilities.

Deployments: Container units for runtimes, available in two variants:

• BYOC Deployments: Run in customer AWS environments while Snowflake manages control infrastructure
• Snowflake Deployments: Leverage Snowpark Container Services for fully managed compute

Processors: Individual data transformation and routing components within pipelines—filter records, transform formats, route based on content, aggregate data.

Controller Services: Shared services providing reusable functionality—database connection pools, authentication providers, schema registries.

Understanding Deployment Models#

OpenFlow’s flexibility comes from supporting two deployment architectures with different trade-offs:

Bring Your Own Cloud (BYOC)#

Processing occurs entirely within your AWS environment while Snowflake manages the control infrastructure.

Architecture:

Your AWS VPC
├── OpenFlow Runtime (containers)
│   ├── Data Pipeline Execution
│   ├── Processor Instances
│   └── Local Storage
├── Data Sources (databases, applications, files)
└── Network: PrivateLink to Snowflake

Snowflake Control Plane
└── Pipeline Management & Monitoring

When to Use BYOC:

• Data Sovereignty Requirements: Regulatory constraints require data processing within specific regions or accounts
• Data Sensitivity: Sensitive data must be preprocessed locally before moving to Snowflake
• Network Topology: Complex on-premises connectivity or private network requirements
• Cost Optimization: Leverage existing AWS commit or Reserved Instance capacity

Trade-Offs:

• You manage underlying AWS infrastructure (EKS clusters, networking, storage)
• Additional operational complexity compared to Snowflake deployments
• Greater control over compute resources and network configuration

Snowflake Deployments (Snowpark Container Services)#

Fully managed compute using Snowflake’s container orchestration platform.

Architecture:

Snowflake Environment
├── Snowpark Container Services
│   ├── Compute Pools
│   ├── OpenFlow Runtime Containers
│   └── Data Pipeline Execution
├── Native Integration with Snowflake Security
└── Automatic Scaling & Monitoring

When to Use Snowflake Deployments:

• Operational Simplicity: Minimize infrastructure management overhead
• Snowflake-Native Workflows: Primary use case is loading data into Snowflake
• Rapid Deployment: Get started quickly without AWS infrastructure setup
• Unified Billing: Consolidate costs under Snowflake consumption model

Trade-Offs:

• Less control over underlying compute infrastructure
• Currently limited to Snowflake’s supported regions
• Data processing occurs in Snowflake environment (may not meet data locality requirements)

Core Use Cases for OpenFlow#

1. Unstructured Data Ingestion for AI Workloads#

Load multimodal data from cloud storage into Snowflake for Cortex AI processing:

Scenario: Analyze customer support tickets including attachments (PDFs, images, audio recordings) for sentiment analysis and automated routing.

OpenFlow Pipeline:

Google Drive Connector
    ↓
Filter by File Type (PDF, PNG, MP3)
    ↓
Convert to Structured Format (extract text, metadata)
    ↓
Enrich with Business Context (customer_id, ticket_id)
    ↓
Write to Snowflake Table
    ↓
Trigger Cortex AI Analysis

Value: Eliminates manual ETL scripting for diverse file formats while preparing data for AI-ready analysis.

2. Change Data Capture (CDC) Replication#

Real-time synchronization from operational databases to Snowflake analytics:

Scenario: Replicate customer orders from PostgreSQL production database to Snowflake for real-time reporting.

OpenFlow Pipeline:

PostgreSQL CDC Connector (Debezium)
    ↓
Filter by Table (orders, order_items, customers)
    ↓
Transform CDC Events (insert, update, delete)
    ↓
Merge into Snowflake Tables (MERGE statements)
    ↓
Update Materialized Views

Implementation:

# OpenFlow CDC Processor Configuration
processor:
  type: CaptureChangePostgreSQL
  properties:
    database_host: prod-postgres.example.com
    database_name: ecommerce
    tables: orders, order_items, customers
    initial_load: true
    slot_name: snowflake_cdc_slot

destination:
  type: SnowflakeMerge
  properties:
    database: ANALYTICS
    schema: REPLICA
    merge_key: order_id
    warehouse: ETL_WH

Value: Near-real-time analytics on operational data without impacting production databases.

3. Streaming Event Processing#

Ingest and process real-time event streams from Kafka or similar platforms:

Scenario: Process clickstream events from website and mobile app for behavioral analytics.

OpenFlow Pipeline:

Kafka Consumer
    ↓
Parse JSON Events
    ↓
Enrich with Session Context
    ↓
Route by Event Type (page_view, click, purchase)
    ↓
Aggregate Metrics (hourly active users)
    ↓
Write to Snowflake

Processor Configuration:

kafka_consumer:
  bootstrap_servers: kafka.example.com:9092
  topic: clickstream-events
  group_id: openflow-analytics

json_parser:
  schema_registry: http://schema-registry:8081

routing:
  page_view: analytics.page_views
  click: analytics.click_events
  purchase: analytics.conversions

Value: Unified streaming and batch processing without separate stream processing infrastructure.

4. SaaS Data Extraction#

Extract data from marketing and sales platforms for centralized reporting:

Scenario: Consolidate advertising performance metrics from LinkedIn Ads, Meta Ads, and Google Ads into Snowflake.

OpenFlow Pipeline:

LinkedIn Ads API Connector
    ↓
Meta Ads API Connector
    ↓
Google Ads API Connector
    ↓
Standardize Schema (campaign, impressions, clicks, spend)
    ↓
Join with CRM Data (campaign_id → opportunity_id)
    ↓
Calculate ROI Metrics
    ↓
Write to Snowflake Marketing Analytics Schema

Value: Eliminates custom API integration code and provides unified view across marketing platforms.

5. Custom Data Flows with NiFi Processors#

Leverage Apache NiFi’s 300+ built-in processors for specialized workflows:

Scenario: Process IoT sensor data from manufacturing equipment, applying custom validation and transformation logic.

OpenFlow Pipeline:

MQTT Subscriber (IoT sensors)
    ↓
ValidateRecord (schema validation)
    ↓
QueryRecord (filter anomalies)
    ↓
CalculateRecordStats (rolling averages)
    ↓
RouteOnAttribute (critical alerts → separate flow)
    ↓
ConvertRecord (JSON → Parquet)
    ↓
PutSnowflake (batch insert)

Value: Reuse proven NiFi processors while benefiting from managed infrastructure and Snowflake integration.

Advantages Over Traditional ETL/ELT Tools#

1. Multimodal Data Support#

Traditional ETL tools focus on structured databases and APIs. OpenFlow natively handles:

• Structured: Relational databases, APIs, CSV files
• Semi-structured: JSON, XML, Avro, Parquet
• Unstructured: Images, PDFs, audio, video, sensor data

This breadth eliminates the need for separate tooling based on data type.

2. Managed Infrastructure Without Vendor Lock-In#

OpenFlow builds on Apache NiFi—an open-source standard with a large ecosystem. You’re not locked into proprietary data flow languages or connectors.

If you later decide to self-host NiFi, your pipeline definitions remain compatible. This contrasts with proprietary platforms where migration requires complete rebuilds.

3. Data Sovereignty and Privacy#

BYOC deployments keep sensitive data processing within your AWS environment. Data never transits through Snowflake infrastructure until you explicitly write it to Snowflake tables.

This architecture supports:

• GDPR Right to Erasure: Process and delete data locally before Snowflake storage
• Data Localization: Keep EU data in EU regions, US data in US regions
• Pre-Anonymization: Hash or mask PII before it reaches the data warehouse

4. Real-Time and Batch Unified#

Most ETL tools specialize in either batch or streaming. OpenFlow handles both within the same pipeline framework:

# Same pipeline processes batch historical load and streaming updates
pipeline:
  initial_load:
    source: S3 (historical Parquet files)
    mode: batch

  ongoing_updates:
    source: Kafka (streaming CDC events)
    mode: streaming

  destination: Snowflake (merged output)

5. Extensibility Through Custom Processors#

When pre-built connectors don’t meet requirements, develop custom NiFi processors in Java:

@Tags({"custom", "transform"})
public class CustomBusinessLogicProcessor extends AbstractProcessor {
    @Override
    public void onTrigger(ProcessContext context, ProcessSession session) {
        FlowFile flowFile = session.get();
        // Apply custom transformation logic
        session.transfer(flowFile, REL_SUCCESS);
    }
}

Deploy custom processors to OpenFlow runtimes alongside standard connectors.

Security and Enterprise Features#

Authentication and Authorization#

OAuth2 Integration: Authenticate to SaaS APIs using OAuth2 flows without managing credentials manually.

Fine-Grained RBAC: Control who can create, modify, and execute pipelines through Snowflake’s role-based access control:

-- Grant pipeline creation privileges
GRANT CREATE INTEGRATION ON ACCOUNT TO ROLE data_engineer;

-- Grant execution privileges on specific runtimes
GRANT USAGE ON INTEGRATION openflow_runtime_prod TO ROLE pipeline_operator;

Secrets Management: Integrate with AWS Secrets Manager or HashiCorp Vault for centralized credential storage:

controller_service:
  type: AWSSecretsManagerClientService
  properties:
    region: us-east-1
    secret_name: snowflake/db_credentials

Network Security#

PrivateLink Support: Securely transmit data to Snowflake using inbound AWS PrivateLink, keeping traffic off the public internet:

snowflake_connection:
  type: SnowflakeConnectionPool
  properties:
    account: mycompany.privatelink
    warehouse: ETL_WH
    use_privatelink: true

VPC Isolation: BYOC deployments run entirely within your VPC, applying your existing network security policies (security groups, NACLs, firewall rules).

Encryption#

TLS Encryption: All data in transit encrypted using TLS 1.2+

Tri-Secret Secure: Enhanced encryption for data written to Snowflake, combining Snowflake-managed, customer-managed, and Snowflake-managed composite keys:

-- Enable Tri-Secret Secure for OpenFlow target table
ALTER TABLE marketing_data.ad_performance
SET ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE' MASTER_KEY = 'customer_master_key');

Getting Started: Implementation Pathway#

Prerequisites#

For Snowflake Deployments:

1. Snowflake Enterprise Edition or higher
2. ACCOUNTADMIN or role with CREATE INTEGRATION privilege
3. Compute pool for Snowpark Container Services

For BYOC Deployments:

1. AWS account with EKS cluster provisioning capability
2. Network connectivity between AWS VPC and Snowflake (PrivateLink recommended)
3. AWS IAM roles for OpenFlow service access

Step 1: Create a Deployment#

Snowflake Deployment:

-- Create compute pool for OpenFlow
CREATE COMPUTE POOL openflow_pool
  MIN_NODES = 1
  MAX_NODES = 5
  INSTANCE_FAMILY = CPU_X64_MEDIUM;

-- Create OpenFlow deployment
CREATE INTEGRATION openflow_integration
  TYPE = OPENFLOW
  ENABLED = TRUE
  DEPLOYMENT_TYPE = SNOWFLAKE
  COMPUTE_POOL = openflow_pool;

BYOC Deployment:

# Use Snowflake CLI to provision BYOC infrastructure
snow openflow deployment create \
  --name prod-openflow \
  --type byoc \
  --aws-region us-east-1 \
  --vpc-id vpc-12345678 \
  --subnet-ids subnet-abc,subnet-def

Step 2: Configure a Runtime#

Runtimes host your data pipelines:

CREATE OPENFLOW RUNTIME marketing_etl_runtime
  DEPLOYMENT = openflow_integration
  SIZE = MEDIUM
  AUTO_SUSPEND = 600
  COMMENT = 'Runtime for marketing data pipelines';

Step 3: Build a Data Pipeline#

Use Snowsight’s visual canvas or define pipelines in code:

Visual Canvas:

1. Navigate to Data → Integrations → OpenFlow
2. Select runtime marketing_etl_runtime
3. Drag processors onto canvas (GetS3Object → ConvertCSVToJSON → PutSnowflake)
4. Configure processor properties
5. Connect processors with relationships
6. Deploy pipeline

Code-Based Definition:

# pipeline_definition.yaml
pipeline:
  name: linkedin_ads_ingestion

  processors:
    - id: fetch_linkedin_data
      type: InvokeHTTP
      properties:
        url: https://api.linkedin.com/v2/adAnalytics
        method: GET
        authentication: oauth2_service

    - id: parse_json
      type: EvaluateJsonPath
      properties:
        destination: flowfile-attribute

    - id: write_to_snowflake
      type: PutSnowflake
      properties:
        database: MARKETING
        schema: RAW_DATA
        table: linkedin_ads
        warehouse: ETL_WH

  connections:
    - from: fetch_linkedin_data
      to: parse_json
      relationship: success
    - from: parse_json
      to: write_to_snowflake
      relationship: matched

Deploy via CLI:

snow openflow pipeline deploy \
  --runtime marketing_etl_runtime \
  --definition pipeline_definition.yaml

Step 4: Monitor and Optimize#

Monitor pipeline execution through Snowsight dashboards:

-- Query OpenFlow execution metrics
SELECT
    pipeline_name,
    runtime_name,
    execution_start_time,
    records_processed,
    execution_duration_seconds,
    status
FROM SNOWFLAKE.ACCOUNT_USAGE.OPENFLOW_PIPELINE_HISTORY
WHERE execution_start_time >= DATEADD(day, -7, CURRENT_DATE())
ORDER BY execution_start_time DESC;

Optimize based on:

• Throughput: Records processed per second
• Error Rates: Failed processor executions
• Resource Usage: CPU and memory utilization

Cost Considerations#

Snowflake Deployments#

Charged via Snowflake compute credits based on:

• Compute pool size (X-Small to 6X-Large)
• Runtime hours
• Data processing volume

Optimization Strategies:

• Use AUTO_SUSPEND to stop idle runtimes
• Right-size compute pools based on throughput requirements
• Schedule batch pipelines during off-peak hours for lower credit costs

BYOC Deployments#

Costs include:

• AWS infrastructure (EKS, EC2, storage)
• Snowflake control plane usage (minimal)
• Data egress from AWS to Snowflake (if not using PrivateLink)

Optimization Strategies:

• Use Reserved Instances or Savings Plans for predictable workloads
• Leverage Spot Instances for non-critical pipelines
• Enable PrivateLink to avoid egress charges

Comparison: OpenFlow vs. Alternative Integration Tools#

OpenFlow uniquely combines managed infrastructure with open-source flexibility and deployment choice.

Limitations and Considerations#

Current Platform Maturity#

OpenFlow is a newer offering in Snowflake’s ecosystem. Some capabilities may evolve:

• Connector library smaller than mature ETL platforms
• Regional availability limited compared to Snowflake Data Cloud
• Less community content (templates, tutorials) than established tools

Operational Expertise#

Teams need familiarity with:

• Apache NiFi concepts (processors, controller services, flow files)
• Data pipeline design patterns
• Snowflake integration best practices

Plan for learning curve if team hasn’t used NiFi previously.

BYOC Operational Overhead#

BYOC deployments require managing:

• Kubernetes clusters (EKS)
• Network configuration (VPCs, subnets, routing)
• Security policies (IAM roles, security groups)
• Monitoring and logging infrastructure

Ensure sufficient AWS operational expertise before choosing BYOC.

The Future of Data Integration on Snowflake#

OpenFlow represents Snowflake’s vision for modern data integration: managed infrastructure, open standards, deployment flexibility, and native platform integration.

As the platform matures, expect:

• Expanded Connector Library: More pre-built integrations for common sources
• Enhanced AI Integration: Direct pipelines feeding Cortex AI workflows
• Deeper Snowflake Native Features: Integration with data sharing, marketplace, and governance features
• Multi-Cloud Support: BYOC deployments beyond AWS (Azure, GCP)

For organizations building comprehensive data platforms on Snowflake, OpenFlow provides a path to consolidate integration infrastructure while maintaining flexibility for diverse data sources and workflows.

Conclusion#

Snowflake OpenFlow solves the persistent challenge of data integration by combining the proven capabilities of Apache NiFi with fully managed infrastructure and native Snowflake integration. Its dual deployment model—Snowflake-managed and BYOC—allows organizations to balance operational simplicity with data sovereignty requirements.

Whether you’re ingesting unstructured data for AI workloads, replicating operational databases for analytics, processing real-time event streams, or extracting data from SaaS platforms, OpenFlow provides the connectors, processors, and orchestration capabilities needed without deploying separate integration infrastructure.

For teams already invested in Snowflake, OpenFlow offers a compelling alternative to standalone ETL/ELT tools—reducing infrastructure complexity, maintaining data security, and leveraging familiar Snowflake operational patterns.

Start with a focused use case that benefits from OpenFlow’s strengths—perhaps unstructured data ingestion or CDC replication—prove the value, then expand to additional integration workflows as your team builds expertise with the platform.

Ready to explore Snowflake OpenFlow? Review the official documentation ↗ for detailed setup guides and connector references, then identify a high-value integration use case to pilot the platform.