Unlocking Efficiency: Snowflake’s Impact on Finance Sector Operations

In this blog post, we explore Snowflake's significant advantages for the finance sector and provide a few simple code examples to illustrate how easy it can be to work with Snowflake.

Photo by Nick Chong on Unsplash

Why Your Finance Company Should Move Analytics Data to Snowflake

If you’re a data engineer at a bank dealing with slow reports or a data analyst at an investment company struggling with the time it takes to add a new report, this post is for you.

In the dynamic and data-driven world of finance, having a robust, scalable, and secure data management platform is crucial. Snowflake, a cloud-based data warehousing solution, offers a compelling suite of features tailored to meet the unique needs of the finance sector. Here are several reasons why moving your data to Snowflake could be a game-changer for your finance company:

1. Scalability and Performance

Snowflake’s architecture is designed to separate storage and compute resources, which offers several benefits for scalability, performance, and cost management. Here’s how it works:

Storage Layer

• Data Storage: Snowflake stores all your data in a centralized storage layer. This storage is highly scalable, secure, and designed to handle large volumes of data efficiently. The data is stored in your choice of cloud, AWS, Azure, or Google, benefiting from the cloud provider's durability, backup, compression, encryption, and security mechanisms.

Compute Layer

• Virtual Warehouses: Compute resources in Snowflake are provided by virtual warehouses. Each virtual warehouse is an independent compute cluster that can be scaled up or down based on your workload requirements.
• Independent Scaling: You can scale compute resources independently of storage. This means you can add more computing power for high-demand operations without adjusting your storage capacity and vice versa.
• Concurrency: Multiple virtual warehouses can access the same storage layer simultaneously, allowing multiple users or applications to perform operations without interfering with each other. This concurrency ensures that one workload does not affect the performance of another.

-- Create a new warehouse with auto-scaling enabled
CREATE WAREHOUSE finance_wh
WITH 
    WAREHOUSE_SIZE = 'MEDIUM' 
    AUTO_SUSPEND = 300 -- Auto suspend after 5 minutes of inactivity
    AUTO_RESUME = TRUE -- Auto resume when a query is submitted
    MIN_CLUSTER_COUNT = 1 -- Minimum number of clusters
    MAX_CLUSTER_COUNT = 10; -- Maximum number of clusters

2. Enhanced Data Security

Snowflake provides the infrastructure to keep your data safe, but like any solution, it’s up to you to follow best practices and use the framework wisely. If you write your password on a note and leave it on top of your safe or keep your mobile and computer unlocked, it’s your responsibility.

Data Encryption

• End-to-End Encryption: Data is encrypted in transit and at rest using strong encryption standards (AES-256). This ensures that data remains secure throughout its lifecycle.
• Automatic Key Rotation: Snowflake automatically rotates encryption keys to enhance security and reduce the risk of key compromise.

Access Control

• Role-Based Access Control (RBAC): Snowflake uses RBAC to manage access to data and resources. This allows administrators to assign users roles with specific permissions, ensuring that access is granted on a need-to-know basis.
• Multi-Factor Authentication (MFA): MFA adds an extra layer of security by requiring users to provide multiple verification forms before accessing Snowflake.
• Single Sign-On (SSO): Integration with SSO providers allows users to authenticate using their existing corporate credentials, simplifying access management and enhancing security.

-- Grant privileges to finance_analyst role
GRANT USAGE ON DATABASE finance_db TO ROLE finance_analyst;
GRANT USAGE ON SCHEMA finance_db.investments TO ROLE finance_analyst;
GRANT SELECT ON ALL TABLES IN SCHEMA finance_db.investments TO ROLE finance_analyst;

-- Grant privileges to finance_auditor role
GRANT USAGE ON DATABASE finance_db TO ROLE finance_auditor;
GRANT USAGE ON SCHEMA finance_db.investments TO ROLE finance_auditor;
GRANT SELECT ON ALL TABLES IN SCHEMA finance_db.investments TO ROLE finance_auditor;
GRANT SELECT ON FUTURE TABLES IN SCHEMA finance_db.investments TO ROLE finance_auditor;

-- Grant privileges to finance_data_scientist role
GRANT USAGE ON DATABASE finance_db TO ROLE finance_data_scientist;
GRANT USAGE ON SCHEMA finance_db.investments TO ROLE finance_data_scientist;
GRANT SELECT ON ALL TABLES IN SCHEMA finance_db.investments TO ROLE finance_data_scientist;
GRANT INSERT, UPDATE, DELETE ON ALL TABLES IN SCHEMA finance_db.investments TO ROLE finance_data_scientist;

-- Grant privileges to finance_manager role
GRANT USAGE ON DATABASE finance_db TO ROLE finance_manager;
GRANT USAGE ON SCHEMA finance_db.investments TO ROLE finance_manager;
GRANT SELECT, INSERT, UPDATE, DELETE ON ALL TABLES IN SCHEMA finance_db.investments TO ROLE finance_manager;
GRANT SELECT ON FUTURE TABLES IN SCHEMA finance_db.investments TO ROLE finance_manager;
GRANT INSERT, UPDATE, DELETE ON FUTURE TABLES IN SCHEMA finance_db.investments TO ROLE finance_manager;

Network Security

• Network Policies: Snowflake allows you to define network policies to control access based on IP address ranges, ensuring that only trusted networks can connect to your Snowflake account.
• PrivateLink/Private Connectivity: For AWS and Azure, Snowflake supports PrivateLink and Private Connectivity, allowing secure, private communication between Snowflake and your virtual network without traversing the public internet.

-- Create a network policy named 'finance_network_policy'
CREATE NETWORK POLICY finance_network_policy
  ALLOWED_IP_LIST = ('203.0.113.0/24', '192.0.2.1', '198.51.100.5/32')
  BLOCKED_IP_LIST = ('0.0.0.0/0'); -- Optionally, you can specify blocked IP addresses or ranges

Data Masking and Obfuscation

• Dynamic Data Masking: This feature allows you to mask sensitive data dynamically so that unauthorized users can only see obfuscated data rather than the actual sensitive information. With just a few simple lines of code, you can control who can access each piece of your PII data.
• External Tokenization: Snowflake integrates with third-party tokenization providers to tokenize sensitive data before it enters Snowflake, providing an additional layer of security.

-- Create a masking policy for the account_number column
CREATE MASKING POLICY mask_account_number AS
  (val STRING) RETURNS STRING ->
  CASE
    WHEN CURRENT_ROLE() IN ('FINANCE_AUDITOR') THEN val
    ELSE '****-****-****-****'
  END;

Compliance and Certifications

• Compliance Certifications: Snowflake complies with major industry standards and regulations, including:
• SOC 1 Type 2
• SOC 2 Type 2
• HITRUST
• PCI DSS
• GDPR
• FedRAMP Moderate
• CSA Star Level 1
• And more
• Continuous Monitoring and Auditing: Snowflake monitors its platform for security threats and regularly undergoes third-party audits to ensure compliance with industry standards.

Data Governance

• Time Travel and Fail-Safe: Snowflake provides features like Time Travel and Fail-Safe to ensure data protection and recovery. Time Travel allows you to query historical data, while Fail-Safe provides additional data recovery options.
• Data Classification: Snowflake enables you to classify and tag data based on its sensitivity and compliance requirements, helping you manage and protect sensitive information more effectively.

-- Query the table as of a specific timestamp
SELECT * FROM finance_db.investments.transactions 
  AT (TIMESTAMP => '2023-07-01 12:00:00');

-- Create a tag for sensitive data
CREATE TAG sensitive_data;

-- Create a tag for confidential data
CREATE TAG confidential_data;

-- Tag the account_number column as sensitive data
ALTER TABLE finance_db.investments.account_info
  MODIFY COLUMN account_number SET TAG sensitive_data = 'true';

-- Tag the balance column as confidential data
ALTER TABLE finance_db.investments.account_info
  MODIFY COLUMN balance SET TAG confidential_data = 'true';

Incident Response and Security Monitoring

• Security Monitoring: Snowflake uses advanced security monitoring tools to detect and respond to potential security threats in real time.
• Incident Response: Snowflake has a dedicated security team and established incident response procedures to promptly handle and mitigate security incidents.

3. Cost Efficiency

Traditional on-premises data warehouses often come with high maintenance and infrastructure costs. Snowflake’s pay-as-you-go model ensures you only pay for the storage and compute resources you use. This model reduces capital expenditures and allows for more predictable budgeting and cost management.

4. Real-Time Data Analytics

In finance, timely and accurate data is critical. Snowflake supports real-time data processing and analytics, enabling you to gain insights quickly and make informed decisions. Whether you’re conducting fraud detection, risk management, or customer analytics, Snowflake’s capabilities allow you to process and analyze data in real time, providing a competitive edge.

5. Seamless Data Integration

Snowflake provides seamless integration with various data sources and third-party tools. This includes traditional databases, cloud-based applications, and data lakes. For finance companies, you can easily consolidate data from disparate sources into a single platform, facilitating comprehensive and holistic data analysis. Snowflake supports different file types including CSV, JSON, AVRO, ORC, PARQUET, and XML.

With Document AI, which was announced at the last Summit, you can process documents like PDFs, Word, TXTs, and images.

SELECT object_col,
       XMLGET(object_col, 'level2'),
       XMLGET(XMLGET(object_col, 'level2'), 'level3', 1)
    FROM xml_demo;
+-------------------------+------------------------------+---------------------------------------------------+
| OBJECT_COL              | XMLGET(OBJECT_COL, 'LEVEL2') | XMLGET(XMLGET(OBJECT_COL, 'LEVEL2'), 'LEVEL3', 1) |
|-------------------------+------------------------------+---------------------------------------------------|
| <level1>                | <level2>                     | <level3>3B</level3>                               |
|   1                     |   2                          |                                                   |
|   <level2>              |   <level3>3A</level3>        |                                                   |
|     2                   |   <level3>3B</level3>        |                                                   |
|     <level3>3A</level3> | </level2>                    |                                                   |
|     <level3>3B</level3> |                              |                                                   |
|   </level2>             |                              |                                                   |
| </level1>               |                              |                                                   |
+-------------------------+------------------------------+---------------------------------------------------+

6. Collaboration and Data Sharing

Snowflake’s unique data sharing capabilities allow secure and efficient data sharing across different departments and with external partners. This can enhance collaboration within your organization and with stakeholders such as auditors, regulators, and business partners, streamlining operations and improving transparency.

-- Create a share named 'finance_share'
CREATE SHARE finance_share;

-- Add the database and schema to the share
GRANT USAGE ON DATABASE finance_db TO SHARE finance_share;
GRANT USAGE ON SCHEMA finance_db.investments TO SHARE finance_share;

-- Add the table to the share
GRANT SELECT ON TABLE finance_db.investments.transactions TO SHARE finance_share;

7. Disaster Recovery and Business Continuity

Snowflake’s cloud-based infrastructure provides built-in disaster recovery and business continuity features. Automated backups, cross-region data replication, and rapid recovery options ensure that your critical financial data remains accessible and protected, even during an outage or disaster.

8. Advanced Analytics and Machine Learning

Snowflake’s support for advanced analytics and machine learning can transform how finance companies leverage their data. With integrations with platforms like DataRobot, AWS SageMaker, and Azure Machine Learning, you can build, train, and deploy sophisticated models directly within Snowflake. This empowers you to uncover deeper insights, improve predictive accuracy, and drive innovation in algorithmic trading, credit scoring, and customer personalization.

# Query data from Snowflake
query = "SELECT * FROM credit_transactions"
df = conn.cursor().execute(query).fetch_pandas_all()

# Preprocess data (e.g., handle missing values, encode categorical variables)
# Assume X contains features and y contains target (credit risk)
X = df.drop(columns=['credit_risk'])
y = df['credit_risk']

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train a Random Forest classifier
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)

# Predict on test data
y_pred = clf.predict(X_test)

# Evaluate model performance
print(classification_report(y_test, y_pred))
print("Accuracy:", accuracy_score(y_test, y_pred))

Conclusion

Migrating your finance company’s Analytics data to Snowflake offers many benefits, from enhanced security and scalability to cost efficiency and real-time analytics. Snowflake’s robust, flexible, and secure platform can help you navigate the complexities of financial data management, ensuring you remain agile, compliant, and competitive in an ever-evolving industry.

Summary

This blog post explored the benefits of migrating to Snowflake for the finance sector.

To stay updated on more Snowflake-related posts, follow me at my Medium profile: Eylon’s Snowflake Articles.

I’m Eylon Steiner, Engineering Manager for Infostrux Solutions and a Snowflake Data Superhero. You can follow me on LinkedIn.

Subscribe to Infostrux Medium Blog at https://blog.infostrux.com for the most interesting Data Engineering and Snowflake news. Follow Infostrux’s open-source efforts through GitHub.