Modernizing XML Processing for Financial Services with Snowflake

Despite the rise of new data formats such as JSON, Avro and Parquet, XML (eXtensible Markup Language) remains a foundational data standard in financial services. From core banking systems built in the 1990s-2000s to modern regulatory reporting, XML is deeply embedded in the industry's operational fabric. Standards like FpML (Financial Products Markup Language) for derivatives, XBRL (eXtensible Business Reporting Language) for regulatory reporting, ISO 20022 for payments and securities, and even some FIX Protocol implementations rely heavily on XML.

Financial institutions routinely generate, exchange and submit XML documents to support critical functions such as:

• Interbank communications via SWIFT
  

• Trade and settlement processes
  

• Regulatory submissions in preparation for the Financial Data Transparency Act (FDTA) and current entities such as the SEC, FINRA, ESMA, the Federal Reserve, OCC and FDIC
  

• Payment message exchanges
  

• Market data file formats
  

While XML’s strict schema enforcement and document structure offer clear advantages for complex, structured data, the challenge data engineers and analysts face lies in making this data easily accessible and usable for modern analytics, reporting and integration workflows. Historically, parsing XML has required dedicated infrastructure, specialized development resources or custom extraction, transformation and loading (ETL) pipelines — creating friction, costs and delays.

Unlocking legacy and modern value with Snowflake

With the recent introduction of native XML processing capabilities, Snowflake bridges the gap between legacy data formats and modern analytics needs — allowing financial institutions to unlock the full value of their XML data without sacrificing agility or scale.

Using Snowflake, organizations can now:

• Load XML directly into Snowflake without needing external preprocessing
  

• Query XML data with standard SQL, including leveraging powerful built-in functions for navigation, extraction and transformation
  

• Integrate XML seamlessly with JSON, relational data and semi-structured analytics
  

• Apply governance, security and lineage uniformly across structured and semi-structured data
  

• Enable data science and AI/ML workloads directly on XML-derived data sets

Snowflake’s native XML support transforms XML from a siloed archival format into an active, queryable asset — fully integrated with the broader Snowflake AI Data Cloud ecosystem.

Key financial services use cases for Snowflake's XML capabilities

Financial institutions can now reimagine their XML-driven workflows across a variety of mission-critical functions.

Regulatory compliance and reporting

Organizations can directly ingest XBRL filings, regulatory XML templates or SEC submissions into Snowflake. With SQL-based parsing and transformation, compliance teams can automate report generation, validate filings against internal data and accelerate submission cycles.

Trading and risk management integration

Trade confirmations, derivative lifecycle events (via FpML) and FIXML messages can be loaded, parsed and integrated into trading and risk analytics pipelines — reducing latency in reconciliation and reporting.

Payments and interbank messaging

ISO 20022 XML messages for payments, securities transactions and account servicing can be easily stored, parsed and analyzed in Snowflake. Banks and clearinghouses can enrich payment data, monitor transaction flows and identify anomalies without custom parsing infrastructure.

Snowflake advantages for XML-driven workflows

By modernizing XML processing within the Snowflake AI Data Cloud, financial services institutions gain:

• Faster time to insight: Parse and query XML quickly without waiting for external ETL.
  

• A unified data estate: Combine XML, JSON, Parquet and relational data in a single governed platform.
  

• Enterprise-grade security: Apply fine-grained, enterprise-level access, compliance and governance controls to XML workloads.
  

• Scalability: Automatically scale compute resources for parsing large volumes of XML files.
  

• Data sharing and collaboration: Share parsed XML data sets across teams or with external partners using secure data sharing capabilities.
  

Snowflake eliminates the complexity traditionally associated with XML workflows, helping financial services firms stay agile, compliant and insight-driven.

Solution architecture

Modernizing XML processing with Snowflake leverages the platform’s native capabilities for storing, parsing, querying and managing semi-structured XML data — all using familiar SQL and Snowflake-native features. Snowpark XML provides a programmatic experience for Python data engineers.

Snowflake treats XML as semi-structured data through the VARIANT data type, enabling seamless integration into your analytics workflows without requiring external transformations.

SQL XML functions include:

• XML parsing: PARSE_XML converts raw XML text into a VARIANT format for storage and querying.

• Element retrieval: XMLGET extracts specific XML elements from parsed XML structures.

• XML validation: CHECK_XML verifies that XML strings are well-formed.

• XML generation: TO_XML serializes Snowflake objects back into XML text format.

• XML ingest: COPY allows build copy of XML into Snowflake VARIANT data type.

Snowpark XML delivers three primary benefits:

• Scales to large files: Snowpark XML pre-chunks large XML files based on rowTag, allowing customers to selectively load only necessary rowTags into Snowflake tables, bypassing the VARIANT size limit.

• Easier querying via VARIANT: Each XML record is extracted as a separate row, and each field within that record becomes a separate column of type VARIANT. This structure allows customers to query using dot notation or FLATTEN, without chaining XML functions like XMLGET.

• Simple, one-step API: Ingestion is initiated through a single, intuitive API:

These improvements simplify onboarding for Spark users and allow for faster migration to Snowflake for XML-heavy workloads.

Beyond basic parsing, Snowflake’s platform features allow you to automate, govern and analyze XML data at scale:

• Automated processing and real-time ingestion: Use tasks, Streams, Snowpipe, Dynamic Tables and Time Travel to build event-driven pipelines, enable real-time data ingestion, automate refreshes and audit historical XML data changes, ensuring the most up-to-date information for analysis.

• Pipeline automation and management: Orchestrate complex workflows with external tables, stored procedures, user-defined functions (UDFs) and tasks — enabling flexible, maintainable XML data pipelines.

• Security and governance: Apply row access policies, dynamic data masking policies, tag-based governance and object dependencies to secure sensitive XML data and manage compliance with financial and data privacy regulations.

• Data engineering, advanced analytics and machine learning integration: Extend your XML-based data sets with Python/Java UDFs and Snowpark and connect to BI and ML tools for predictive analytics, anomaly detection and advanced visualizations for faster time to decision.

• Data sharing and API integration: Seamlessly share XML-derived data sets across Snowflake accounts or integrate with external APIs using external functions, database replication and multi-region deployments for global reach and resiliency.

For examples of use, try out the following quickstarts with SQL and Snowpark: 

• Modernize XML Processing for Financial Services with SQL XML Functions
• Modernize XML Processing for Financial Services with Snowpark XML