As more and more companies turn to cloud-based data warehousing solutions like Snowflake, the needs for cost and usage optimization become increasingly important. Snowflake is a powerful platform that offers a variety of features and benefits and using it efficiently can help ensure organizations the best bang for their buck. Although accessing usage and cost details within Snowflake can be tricky for anyone without technical skills, anyone can use the pre-built Snowflake Usage Analytics from DataLakeHouse.io to view various usage and cost details for your organization and determine whether your team is making the most of Snowflake. Below we explore some strategies for effectively allocating budget and optimizing usage when it comes to your organization’s Snowflake.
Use Clustering Keys to Improve Query Performance
One of the most effective ways to better allocate your Snowflake budget is by improving query performance. Clustering keys are a powerful tool for accomplishing this goal. By selecting the right clustering keys, you can reduce the amount of data scanned by queries, which in turn reduces the amount of data that needs to be stored in your Snowflake account. This can lead to significant cost savings over time, allowing your organization to do more in the data cloud.
You can also effectively allocate your organization’s Snowflake budget through data compression. By compressing data, you can reduce the amount of storage space required by up to 90%. Snowflake supports several compression techniques, including GZIP, LZ4, and Zstandard. You can also use the automatic compression feature of Snowflake, which compresses data as it is loaded into the data warehouse.
Data retention is another critical aspect of effective budget allocation within Snowflake. Snowflake charges based on storage and compute usage. By setting up a data retention policy, you can automatically delete data that is no longer required. This reduces storage costs and improves the performance of your data warehouse.
Use Auto-Suspend to Control Costs
Auto-suspend is a feature that allows you to automatically suspend your Snowflake account after a specified period of inactivity. By using auto-suspend, you can avoid paying for resources that are not being used. This can be particularly useful for accounts that are only used occasionally or for testing purposes.
Monitor Query Performance
Monitoring query performance is essential for identifying and addressing performance issues. By monitoring query performance, you can identify slow-running queries, optimize query performance, and reduce the amount of resources required for each query. This can help you avoid over-provisioning and better allocate your spend within Snowflake.
Query optimization is essential to effectively allocate budget within Snowflake and optimize its usage. Snowflake provides several query optimization features, including query profiling, query caching, and query optimization rules. By using these features, you can optimize queries to reduce query times and improve performance.
Use Resource Monitors to Control Costs
One way to optimize your Snowflake usage is to use resource monitors. Resource monitors allow you to control the amount of resources that are allocated to specific users or groups of users. By setting resource limits, you can prevent users from using too many resources, which can help you avoid over-provisioning and reduce costs.
Resource allocation is crucial to optimizing Snowflake usage and spend. Snowflake allows you to allocate resources such as compute (and most recently an option for increased memory) and storage to your data warehouse. You can adjust resource allocation based on the workload of your data warehouse. By allocating resources efficiently, you can properly allocate your organization’s spend within Snowflake and improve performance.
Data partitioning is another effective strategy to optimize Snowflake usage and spend. Snowflake supports both automatic and manual partitioning. By partitioning data, you can segregate large datasets into smaller, more manageable parts. This allows you to query only the necessary data, which reduces query times and improves performance.
Use Time-Travel for Data Recovery
Snowflake’s time-travel feature allows you to recover data from a specific point in time. This feature is particularly useful for recovering data that has been accidentally deleted or modified. By using time-travel, you can avoid the need to restore an entire database, which can be time-consuming and expensive.
Use Snowflake Data Sharing to Share Data
Snowflake Data Sharing allows you to share data with other Snowflake accounts. This feature can be useful for collaborating with partners, customers, or other stakeholders. By sharing data, you can avoid the need to duplicate data across multiple accounts, which can help you reduce storage costs and improve data consistency to maximize your Snowalke account as much as possible.
In conclusion, Snowflake is an excellent data warehousing solution for storing and analyzing data. However, if you’re not keeping track of usage and costs, it’s almost impossible to know if your organization is utilizing the data cloud to its ful potential. By using Snowflake Usage Analytics from DataLakeHouse.io and following the strategies outlined in this blog post, you can optimize both Snowflake usage and spend to take full advantage of your account and do more with your data.
Start your free trial today to gain visibility into your organization’s Snowflake usage and costs and implement the strategies mentioned above to improve the performance of your data warehouse while reducing costs. Or, if you don’t have the time or resources to make use of the strategies discussed, feel free to reach out to AICG, our favorite service partner.
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