7 tips for managing Snowflake data warehouses

Data is at the center of the modern enterprise, and effectively managing that data is key to the ultimate success of a business. Many businesses are turning to Snowflake data warehouses to store and process large volumes of data, so the effective management of a Snowflake data warehouse is now an important component of business success. 

Organizations are working with data today in volumes that were unheard of not that long ago. Because of this, the need for efficient data warehouse management has never been more necessary. Without the right data warehouse management strategy a company risks rapidly escalating costs, poor performance and security vulnerabilities. With data as a whole, companies can't take a list of best practices and bolt it onto an existing data management plan—they have to build out a comprehensive approach that looks at all aspects of the operation. 

In this article, we'll explore seven key practices for data warehouse management and how tools like Capital One Slingshot can help you optimize your data warehouse while keeping costs in check and streamlining governance.

The efficiency of your data warehouse operations starts with how you load and organize your data. Snowflake has several different ways to load data, and understanding and choosing the best options for your company can have a significant impact on both cost and performance. 

1. Use the COPY command, which is designed for batch-loading operations in Snowflake, to improve loading performance. Setting the appropriate file size—between 100-250MB compressed—and using the optimal number of threads will enhance loading efficiency. 

2. Use Snowpipe, a tool that excels in real-time data ingestion, when continuous data loading is needed. Processing data as it arrives can result in cost reduction by eliminating the need for large batch windows. 

3. Use Parquet and ORC files to achieve compression ratios 2x to 4x that of row-based formats and enable column pruning capabilities which reduce I/O during queries. Parquet and ORC also provide better support for complex data types as well as enhanced schema evolution capabilities. 

4. Stage your data in cloud storage solutions like AWS S3 or Google Cloud to reduce network transfer costs, improve error handling and retry capabilities and enhance audit trails for your data. 

5. Compress files  before loading to reduce storage costs by as much as 60%-80%, significantly improving loading performance.

There are different warehouse sizes available in Snowflake, and choosing the correct one can balance overall performance and cost. However, sometimes query patterns and workload characteristics, as well as peak usage periods and seasonal variations, may cause you to need something bigger. This is where Snowflake's scaling capabilities come into play. 

Snowflake provides detailed analytics on warehouse utilization patterns, which companies can use to balance auto-scaling parameters for cost efficiency and performance. Auto-scaling can automatically add or remove clusters from the warehouse based on limits set by admins.

Warehouses are also not a “one size fits all” solution. You can create specialized warehouses for different workload types and tailor them to those workload types. Enterprises should put careful thought and consideration into how they are structuring their data and consider implementing different warehouses depending on the purpose of the data in that warehouse. For example:

• Extract, Transform, Load (ETL) warehouses are excellent for data transformation. 

• BI warehouses can be configured differently for more interactive queries. 

• Data science warehouses have more specific sizing requirements for their complex analytical workloads. 

• Ad-hoc query warehouses have their own custom resource limits that should be planned out well ahead of time.

Regardless of the type of workload, Slingshot helps monitor and optimize Snowflake warehouses by providing insights into their usage patterns and providing data-driven recommendations to optimize them.

Data clustering is not a simple “set it and forget it” proposition with Snowflake data warehouses. Effective data clustering can have improved query performance, but it requires careful planning and ongoing maintenance. There are three things an organization must consider: 

• Query patterns 

• Common filter conditions 

• Data distribution characteristics 

There are tradeoffs that must also be considered between storage costs, query performance and how frequently data is updated within a Snowflake data warehouse. Each of these things play a critical role in designing and implementing the most optimal clustering strategy. 

Optimization is an ongoing process—organizations must pay attention to KPIs and make regular changes to operate and scale efficiently. Capital One Slingshot can help by continuously monitoring auto-clustering costs. This allows organizations to make data-driven decisions about their clustering strategy.

There are a lot of regulatory requirements around data governance and security, and it's essential to maintain compliance to protect sensitive data. To help with this, Snowflake provides Role-Based Access Control (RBAC). Implementing RBAC requires careful planning and ongoing management. A company should:

• Have a clearly defined and documented role hierarchy that respects the principle of least privilege

• Regularly perform access reviews and cleanup procedures

• Integrate with enterprise identity management systems like Okta or EntraID

Data protection strategies should also be comprehensive, not relying solely on basic must-haves like encryption. Snowflake provides data security options like dynamic data masking for sensitive fields and secure views, both of which enable safe sharing of data across organizational boundaries. Row-level security policies ensure that users can only access the data that they’re allowed to access, and end-to-end audit logging provides full transparency and accountability for all data access within the Snowflake data warehouse. 

There are multiple techniques and tools that help organizations monitor query performance and optimize it based on metrics. Organizations should focus on join optimization and predicate pushdown wherever possible. Materialized views are excellent for improving performance for frequently accessed data, and result cache optimization reduces computation utilization. 

Resource usage patterns are a key point in warehouse optimization. Organizations should be monitoring utilization trends within the data warehouse and tracking spilling operations that can indicate larger performance problems. Looking at cache hit rates gives insight into whether or not your caching strategies are effective, and looking at resource contention can help prevent performance bottlenecks. Slingshot’s performance analytics and cost projections provides admins with the visibility needed to refine query performance continually, helping to reduce resource contention and improve cache hit rates.

Putting a cost management strategy in place for Snowflake data warehouse operations is key to maximizing value. Take advantage of Snowflake's separated pricing model—individual charges for compute resources and storage allow companies to pick and choose where the budget is going, instead of having to pay for more storage because it needs more compute resources. Consider these tips for optimizing your costs: 

• Clearly define recovery requirements and optimize time-travel and fail-safe period settings to those requirements.

• Take advantage of Slingshot's cost management features, including cost attribution and over-time usage analysis.

• Implement the use of clones, zero-copy clones, and storage compression.

• Ensure unnecessary data isn't consuming storage resources through data lifecycle management

• Schedule compute-intensive tasks during off-peak hours to leverage lower-cost warehouses. 

Automation is indeed crucial for scaling and maintaining efficiency in complex environments like a Snowflake data warehouse. Here’s a quick rundown of how automation and Slingshot fit into the picture.

1. Data Loading and Transformation: Automate ETL processes to ensure data is consistently and correctly transformed and loaded into the warehouse.

2. Performance Optimization: Implement automated performance tuning and optimization tasks to maintain optimal system performance.

3. Security and Governance: Use automation to enforce security and governance policies.

4. Cost Optimization: Automate cost monitoring and optimization processes to control and reduce expenses.

Built-In Task Scheduling: Schedule tasks like data loading, backups and cleaning.

• Workflow Templates: Provides customizable templates for workflows, streamlining provisioning and governance.

• Monitoring and Alerts: Tracks automated processes and alerts you to issues like cost spikes, enabling proactive analysis and remediation.

• Warehouse scheduling and recommendations: Allows you to set up dynamic warehouse schedules for automated provisioning and gives you data-driven recommendations to right-size warehouses for optimal cost and performance.

• Resource Optimization: Aids in optimizing resource usage by providing insights into performance and cost, making it easier to adjust configurations based on real-time data.

Implementing these best practices with tools like Slingshot can significantly enhance the efficiency of your Snowflake data warehouses. 

Effectively managing Snowflake data warehouses requires a comprehensive approach that balances cost, performance and well-managed governance. Slingshot builds on Snowflake's capabilities to enable even more granular analysis, ongoing optimization and proactive monitoring of warehouse performance. By consistently implementing best practices, offering actionable recommendations, monitoring costs, applying sound governance practices and automating routine tasks, Slingshot helps teams maximize their investment. Learn more about how you can simplify and streamline Snowflake warehouse management with Capital One Slingshot.