How to Configure a ServiceNow Snowflake Data Share

The optimal method for configuring a ServiceNow–Snowflake data share depends on your organization’s requirements. For low-volume, low-frequency, or low-impact tasks, manual methods may suffice—such as exporting data from ServiceNow reports and uploading it into Snowflake via CSV/XML files.

These approaches require minimal setup but carry risks: inconsistent processes, human error, limited auditability, and no automation. As data volumes grow and business reliance on insights increases, more robust, scalable, and automated solutions become essential.

This article explores the most common methods for sharing data between ServiceNow and Snowflake—comparing their strengths, limitations, and suitability for various use cases.

What Is a ServiceNow–Snowflake Data Share?

Data sharing broadly describes the process of making data available to another application, user, organization, or any other potential stakeholder. This includes both manual methods, and automated process such as data integration and replication solutions.

A ServiceNow–Snowflake data share describes data that is replicated between the ServiceNow platform and Snowflake.

Data sharing solutions refer to tools that allow users to scale up the movement of data between systems and applications, such as data integration and replication tools. 

Why Share Data Between ServiceNow and Snowflake?

Organizations increasingly rely on data from multiple platforms to drive insights, automation, and compliance.

ServiceNow stores critical operational data—such as incidents, change requests, and CMDB records. When ServiceNow data is accessible within Snowflake, it can be easily fed downstream to other solutions, retrieved by data stakeholders, and stored more cost effectively.

Solutions that enable ServiceNow-Snowflake data shares allow teams to:

• Analyze ITSM trends across business units or geographies
• Train machine learning models for incident classification or root cause prediction
• Correlate ServiceNow data with logs, metrics, or financial systems
• Generate executive dashboards without burdening the ServiceNow instance
• Retain ServiceNow data without running into ServiceNow’s fees for exceeding storage limits

Benefits of A ServiceNow–Snowflake Data Sharing Solution

When it comes to sharing data between ServiceNow and Snowflake, data sharing technologies such as integration platforms and data replicators unlock several key benefits that drive both operational efficiency and strategic insights:

1. Centralized Data Platform for Cross-System Insights

When connected to Snowflake, ServiceNow data can be combined with data from other sources. This allows for a unified view of the organization’s performance and operations—enabling analytics use cases like ITSM trend analysis, SLA compliance monitoring, and resource planning.

2. Reduced Operational Load on ServiceNow

Instead of running complex reports directly in ServiceNow—which can affect platform performance—data is offloaded to Snowflake, where it can be queried more efficiently. This reduces the risk of long-running queries and performance bottlenecks within the ITSM platform.

3. Real-Time or Near-Real-Time Analytics

With a solution capable of real-time data streaming, organizations can dynamically transfer ServiceNow data (e.g. incidents, change requests, and CMDB items) into Snowflake. This allows organizations to perform advanced analytics and reporting on ServiceNow data, without the latency of periodic data extracts. 

4. Data Security and Governance

Data sharing solutions promote better security and governance than manual methods, reducing the risk of human error. Different data sharing methods provide varying levels of security and governance.

For example, where broad accessibility and flexibility is needed, REST API ServiceNow integrations are best placed to provide it.

However, if more stringent security is required, SOAP integrations limit accessibility and flexibility in favour of enhanced protection and stricter policy enforcement.

If preventing data loss is a concern, then an integration solution with a message bus should be considered. 

Once data is in Snowflake, role-based access ensures data is available to authorized users, without having to buy additional ServiceNow licenses, or adopt unsecure practices like login sharing.

5. Availability for Machine Learning, AI and More

Once in Snowflake, ServiceNow data can be used to power solutions downstream, including AI and machine learning pipelines.

For example, incident ticket histories can be used to train AI models for automated ticket categorization, anomaly detection, or predictive maintenance—capabilities that are limited when purely reliant on the ServiceNow ecosystem.

6. Scalable, Cost-Effective Architecture

Snowflake charges based on compute and storage, not per-user access—so organizations avoid additional licensing fees when querying data.

By transferring ServiceNow data into Snowflake, organizations can significantly reduce licensing costs, and scale analytics without incurring ServiceNow’s additional storage fees.

Key Considerations for Selecting a ServiceNow–Snowflake Data Sharing Solution

Choosing the right ServiceNow data sharing solution is crucial for ensuring seamless data movement, system performance, and security. Not all integration methods are created equal—what works for a simple dashboard may not scale for enterprise-wide analytics or real-time automation.

To help evaluate your options effectively, here’s a Checklist for Success that outlines key factors every organization should assess before selecting a ServiceNow–Snowflake data sharing strategy:

1. Volume

• How much data needs to move?
• Do attachments, comments, or related records (such as change requests and impacted configuration items) need to be included for full context?

When moving large volumes of ServiceNow data, a specialized data replication platform may be necessary. Generic connectors, configured to retrieve data from various sources via API lack the tight coupling with ServiceNow required to maximise throughput.

A solution like Perspectium’s DataSync is installed directly within ServiceNow, allowing it to use push technology to more efficiently replicate large amounts of data.

2. Performance

• Is data extraction currently impacting your ServiceNow instance? 
• Will data extraction start to impact ServiceNow performance as your demand for data scales?

Retrieving large volumes of ServiceNow data, and making frequent requests for ServiceNow data via external API calls will impact the performance of ServiceNow. If performance is an issue, or you believe it may be an issue in future, then consider a specialized data replication, engineered for high throughput. 

Most API-based solutions will start impacting the performance of ServiceNow when extracting several million records from the platform on a daily basis.

3. Purpose

• Is the goal simple data replication, cross-system automation, or triggering specific events (like creating an incident from a chat transcript)?
• Is the data in the ServiceNow–Snowflake data share time-sensitive?

Clarify whether your use case requires just data replication or deeper integration capabilities such as real-time automation or bi-directional e-bonding. Solutions that support workflow triggers or event-based replication will be more appropriate for dynamic, multi-system use cases.

When large amounts of data are required in batches, ETL and specialised data replication solutions are better suited than API-based solutions.

4. Resilience

• How critical is the data in your ServiceNow–Snowflake data share?
• Does the solution offer failover, queueing, or “store and forward” capabilities if Snowflake is temporarily unavailable?

Where resilience is a primary concern, prioritise data replication solutions that provide greater continuity, even during outages. Features like message queuing or built-in retry mechanisms help maintain data integrity and prevent loss during unexpected disruption.

5. Distribution

• Is the data flow one-way or bidirectional?
• Do you need to send data to multiple endpoints (e.g., Snowflake, a machine learning platform, or a BI tool)?

Evaluate how the integration distributes data. While multiple API can be used to populate multiple destinations, they increase the risk of hitting ServiceNow’s API rate limits and degrading platform performance.

Some integration solutions mitigate this by replicating data to a message bus, allowing multiple downstream systems to subscribe without overloading ServiceNow.

For bidirectional data flows, purpose-built e-bonding solutions are recommended. While chaining multiple integrations can enable e-bonding, this approach is often complex to configure and maintain.

6. Data Manipulation

• Does the data in your ServiceNow–Snowflake data share need to be structured?

When moving data from ServiceNow to Snowflake—or vice versa—do you need to reformat, enrich, or redact the data before sending it? While Snowflake can ingest unstructured data, your internal requirements may call for structured, standardized formats.

Conversely, if ServiceNow is consuming data from Snowflake, it will almost always require transformation to align with ServiceNow’s schema and operational models.

7. Security

• Is the data in your ServiceNow–Snowflake data share sensitive?
• Sensitive modules such as HR and Security Incident Response may require compliance with GDPR, HIPAA, and other data regulations.

Different ServiceNow–Snowflake data sharing solutions offer varying levels of security. For example, SOAP APIs generally enforce stricter security standards than REST APIs. Some organizations may even prefer to avoid APIs altogether, as they are frequent targets for cyberattacks.

Carefully evaluate the sensitivity of your data and align your security requirements with the chosen integration approach to ensure it meets your compliance and risk management standards.

8. Maintenance

• Who will maintain the integration?

The cost to maintain an integration solution often increases over time, as growing technical debt, complexity and scaling usage consume more development hours. 

Don’t just consider the initial cost of implementation. Consider the long-term maintenance burden and whether you have the resources to address it. Look for tools that offer monitoring dashboards, self-healing features, or vendor-managed services to reduce ongoing effort.

Configuring A ServiceNow–Snowflake Data Share – Top 4 Methods

Choosing the right integration method depends on your organization’s technical capabilities, data volume, latency requirements, and long-term maintenance needs. Below are common approaches to consider:

Custom Integrations

Organizations with strong in-house development capabilities can build custom integrations using ServiceNow’s REST or SOAP APIs, or connect directly via JDBC or ODBC to Snowflake.

This approach offers maximum flexibility and can be tailored precisely to business needs. However, custom builds are resource-intensive to create and maintain, and require ongoing support as systems evolve.

Pre-Built, API-Based Connectors

For teams without the bandwidth or expertise to build and maintain custom integrations, pre-built ServiceNow connectors offer a faster path to deployment. These solutions are well-suited for:

• Moderate data volumes (typically under a few million records per day)
• Dynamic or ad hoc transfers
• Basic integration requirements

While easier to implement, the responsibility for maintenance and scaling typically falls on the customer. As business needs become more complex, these connectors may struggle with throughput, error handling, or operational monitoring.

Examples include the Snowflake Connector for ServiceNow–a native connector built to extract data from ServiceNow via API. Organizations can also leverage the ServiceNow Integration Hub’s Snowflake Spoke that allows organizations to interact with Snowflake tables via API.

ETL (Extract, Transform, Load) Solutions

ETL tools are another option, and are particularly suited to situations when:

• Data volumes are high
• Transfers are scheduled or batch-based
• Real-time access is not required

ETL platforms extract data from ServiceNow, apply necessary transformations (e.g., formatting, masking), and load it into Snowflake.

This approach works well for nightly or periodic data updates and can scale efficiently. However, they typically lack support for real-time or event-driven workflows, making them unsuitable for use cases like live dashboards or automated incident response.

Perspectium DataSync

Perspectium offers a purpose-built, high-performance integration solution designed specifically for ServiceNow environments. Unlike generic API or ETL tools, Perspectium’s DataSync application is installed directly within the ServiceNow platform, enabling:

• Push-based replication with minimal impact on instance performance
• High throughput, suitable for enterprise-scale data transfers
• Built-in resiliency, including message queuing, error recovery, and logging
• Support for complex data models, including attachments, journal fields, and related tables

Because it’s deeply embedded in ServiceNow’s architecture, Perspectium is ideal for organizations that require real-time replication, low latency, and minimal system disruption—especially at scale.

Final Thoughts

There is no one-size-fits-all solution for ServiceNow–Snowflake data sharing. The right method depends on your organization’s data volume, latency tolerance, security needs, and internal technical capabilities.

Here’s a simplified framework:

• Use manual exports for low-volume, ad hoc use cases
• Use pre-built connectors for quick setup and basic data integration needs
• Use ETL tools for high-volume, batch-oriented workflows
• Use embedded replication tools like Perspectium when performance, resilience, or real-time needs are critical

Start by mapping your use cases to these options, then evaluate the methods in more detail to validate performance and fit.

Whichever path you take, planning for scale, security, and maintainability from the start will help ensure long-term success.

Interested in highly scalable, high-throughput ServiceNow integrations? Try Perspectium.