Snowflake Best Practices: A Comprehensive Guide

As the modern SaaS company tech stack continues to expand, data is increasingly collected at every level of organizations. This data can be leveraged for insights and optimization, but only if it’s stored and structured properly.

Usually this data is held in a repository, as part of a structured data warehouse or data lake for later analysis. This is where Snowflake comes in. 

To fully leverage Snowflake’s capabilities, it’s crucial to follow best practices in its usage. 

In this post, we’ll cover a brief introduction to best practices in Snowflake, the importance of adopting them, and factors to consider when using Snowflake. This guide will also cover best practices in various aspects of Snowflake, including architecture, security, performance, cost management, and data governance. 

What Is Snowflake?

Snowflake is a cloud-based data warehousing solution that offers high performance, scalability, and a host of features that cater to modern data analytics needs. 

It was founded in July 2012 and was publicly launched in October 2014 after two years in stealth mode. The firm offers a cloud-based data storage and analytics service, generally termed “data as a service.” It allows corporate users to store and analyze data using cloud-based hardware and software. 

Importance of Best Practices in Snowflake

Adopting best practices in Snowflake is essential for maximizing the platform’s capabilities while ensuring strong performance, cost efficiency, security, scalability, and data governance. By following these guidelines, organizations can create a robust, flexible, and efficient data warehousing environment that supports their analytics and business intelligence needs. 

Here are the key benefits of following best practices in Snowflake. 

Key Benefits

Enhanced Performance

• Optimized query execution—Best practices like using clustering keys, optimizing queries, and leveraging materialized views ensure that queries run efficiently, reducing execution times and improving overall performance.
• Efficient resource utilization—Properly sized and managed warehouses, along with the use of auto-scaling and auto-suspend features, ensure that computing resources are used optimally, providing the necessary power without unnecessary over-provisioning.

Improved Cost Management

• Cost efficiency—Following best practices helps in managing and optimizing costs. Efficient warehouse management, data retention policies, and resource monitoring prevent wasteful spending and help maintain budgetary control.
• Predictable billing—By monitoring usage and implementing cost controls, organizations can better predict and manage their Snowflake bills, avoiding unexpected expenses.

Robust Security

• Data protection—Implementing best practices in security, such as encryption, role-based access control, and network security measures, ensures that data is protected against unauthorized access and breaches.
• Compliance—Adhering to security best practices helps in meeting regulatory compliance requirements, safeguarding sensitive data, and maintaining trust with stakeholders.

Scalability and Flexibility

• Seamless scaling—Best practices in architecture, like separating compute and storage, enable organizations to scale their Snowflake environment seamlessly as their data and processing needs grow.
• Adaptability—A well-architected Snowflake environment can adapt to changing business requirements, whether it’s handling increased data volumes, adding new data sources, or adjusting workloads.

Data Governance and Quality

• Data integrity—Ensuring data quality through validation checks, consistent data formats, and naming conventions maintains the integrity and reliability of the data.
• Governance and auditing—Proper governance practices, including auditing and monitoring, ensure that data usage is tracked and managed effectively, preventing misuse and enabling accountability.

Operational Efficiency

• Streamlined processes—Implementing best practices in data loading, staging, and transformation streamlines data workflows, reducing the time and effort required to manage data.
• Reduced maintenance—A well-maintained Snowflake environment requires less manual intervention, freeing up resources for more strategic tasks and innovations.

Risk Mitigation

• Minimized downtime—Efficient resource management and monitoring reduce the likelihood of performance issues and downtime, ensuring that the Snowflake environment remains available and responsive.
• Proactive issue resolution—Regular performance analysis and monitoring enable proactive identification and resolution of potential issues before they impact operations.

Architecture Best Practices

Data Organization

• Use separate databases for different environments—Separate databases for development, testing, and production environments to prevent accidental changes and to ensure data integrity.
• Schema design—Use a star schema or snowflake schema depending on your data model. Normalize data where appropriate, but consider denormalization for performance optimization in certain scenarios.
• Logical structures—Organize your data into logical structures like schemas, which can help manage and query data more efficiently.
• Evaluate architecture—This involves data warehouses or data lakes.

Data Warehouses vs. Data Lakes

A data warehouse is a relational database that is designed for analytical rather than transactional work. It collects and aggregates data from one or many sources. It serves as a federated repository for all or certain datasets collected by a business’s operational systems. 

Data lakes typically contain a massive amount of data stored in its raw, native format. This data is made available on demand as needed. When a data lake is queried, a subset of data is selected based on the query’s criteria and presented for analysis. 

The choice of a warehouse or lake therefore depends on the business requirements and the analyses to be carried out. 

Criteria

• Sizing—Choose warehouse or lake sizes based on the workload. Start with smaller sizes and scale up as needed.
• Scaling—Use multicluster warehouses for concurrent workload management. Enable auto-scaling and auto-suspend to optimize performance and cost.
• Separation of compute and storage—Leverage Snowflake’s separation of compute and storage to scale them independently based on needs.

Snowflake Performance Best Practices

Query Performance

• Clustering keys—Use clustering keys for large tables to improve query performance. Monitor and adjust clustering as data evolves.
• Query optimization—Regularly analyze query performance using the Query Profile tool. Optimize slow-running queries by reviewing execution plans and adjusting SQL as necessary.
• Materialized views—Use materialized views to store the results of expensive queries for faster retrieval.

Data Loading

• Bulk loading—Use Snowpipe for continuous data loading and the COPY command for bulk loading. Use appropriate file sizes and compress files to improve loading performance.
• Staging data—Use staging tables for data loading and transformation before moving data to final tables.

Security Best Practices

Access Control

• Role-based access control (RBAC)—Implement RBAC to manage user permissions effectively. Use roles to grant privileges rather than assigning permissions directly to users.
• Least privilege principle—Follow the principle of least privilege by granting only the necessary permissions required for users to perform their job functions.

Data Encryption

Ensure data is encrypted both in transit and at rest. Snowflake provides built-in encryption, but review and configure encryption settings to meet compliance requirements. 

Network Security

• Network policies—Use network policies to restrict access to your Snowflake account based on IP addresses.
• PrivateLink—Consider using AWS PrivateLink or Azure Private Link for secure connections between your Snowflake account and your VPC.

Cost Management Best Practices

Resource Monitoring

• Usage tracking—Use the Account Usage schema to track and monitor resource usage. Regularly review and analyze usage patterns.
• Resource limits—Set resource limits on warehouses to prevent runaway costs. Use the auto-suspend feature to stop warehouses when not in use.

Cost Optimization

• Warehouse optimization—Scale warehouses up or down based on workload demands. Use auto-suspend to minimize costs when warehouses are idle.
• Data retention—Manage data retention policies to control storage costs. Archive or delete old data that is no longer needed.

Data Governance Best Practices

Data Quality

• Data validation—Implement data validation checks during the ETL process to ensure data quality.
• Consistent data format—Use consistent data formats and naming conventions across your databases and schemas.

Auditing and Monitoring

• Logging—Enable and review Snowflake’s native logging features for auditing access and changes.
• Alerting—Set up alerts for significant events or anomalies in data usage or performance.

Conclusion

By following these best practices, you can ensure that your Snowflake environment is secure, efficient, and cost-effective. Regularly review and update your practices to align with evolving business needs and technological advancements. 

Snowflake’s robust feature set combined with diligent management can significantly enhance your data warehousing and analytics capabilities. 

This post was written by Iain Robertson. Iain operates as a freelance IT specialist who provides onsite and remote global interim, contract and temporary support as a senior executive in ICT management. He usually operates as an ICT project manager or ICT leader in the Tertiary Education sector.