Common mistakes and strategies to avoid those
With the advent of Cloud Computing and pay-per-use paradigm, a lot of enterprise customers are migrating or building their data platforms on cloud. Snowflake, one of the most popular Data Cloud platform, is becoming increasingly popular among enterprises due to its unique architecture, performance, flexibility, low barrier to entry and the most familiar SQL interface (for most part) to interact with. However, when enterprises migrate their traditional data platforms to cloud and also build cloud-native pipelines, they may accidentally encounter costs that were not anticipated. If that happens, the entire Data Strategy seemingly fails and the catch-up game starts. In this blog, we will discuss some of the examples which lead to unwarranted costs and how enterprises can avoid them. While these are general principles with cloud, the discussion in this blog is primarily with respect to Snowflake.
1. Not Understanding the pricing model — Snowflake (for that matter any cloud platform) pricing model is based on the amount of data stored and the amount computation required to process that data. Enterprises that do not understand the pricing model may end up paying more than necessary. Snowflake provides a pricing guide to estimate costs, based on storage and compute usage.
2. Not leveraging Snowflake’s built-in features — Snowflake offers a variety of built-in features such as data sharing, multi-cluster warehouses, and automatic scaling, which can help enterprises reduce costs. Enterprises which do not leverage these features may end up paying more than necessary. For example, data sharing allows enterprises to share data securely between Snowflake accounts, reducing the need for data replication. Multi-cluster warehouse allows enterprises to scale compute resources independently, reducing the need for over-provisioning. Automatic scaling allows enterprises to automatically adjust their compute resources based on demand, reducing the need for manual intervention.
3. Not optimizing Query Performance — Query performance is a critical factor in Snowflake pricing model. Enterprises that do not optimize their queries may end up paying more than necessary. Snowflake provides a variety of tools, such as query profiling, query history, and query acceleration, to help enterprises optimize their queries. Enterprises should use these tools to identify and optimize queries that are consuming excessive resources. This is particularly important since many of the analysts who want to just query the needed data from Snowflake, do not have cost consideration in mind. This is an organisational cultural shift where the cost is an imperative for everyone who works with Snowflake.
4. Not using appropriate Storage Types — Snowflake offers multiple storage types, cost and performance characteristics. Enterprises that do not use appropriate storage tiers may end up paying more than necessary.
5. Lack of a formal Data Governance Strategy — Enterprises which do not govern their data formally and diligently, may end up paying more than necessary due to data breaches and unauthorized data access. Snowflake provides a variety of features such as encryption, access controls and auditing to help with Data Governance.
To ensure that enterprises are taking advantage of these cost optimization features and avoiding unexpected costs, it’s important to have a Consumption Governance Model in place. A consumption governance model is a set of policies, procedures, and tools that govern an enterprise’s cloud usage. Here are some best practices for implementing a consumption governance model with Snowflake:
- Set clear cost budgets, goals and thresholds
- Implement usage monitoring and alerts
- Establish cost allocation and chargeback
- Use cost optimization features
- Continuously optimize usage
Conclusion
In conclusion, enterprises which are using Snowflake or any Data Cloud platform need to be aware of the potential costs that they may encounter. Understanding Snowflake’s pricing model, leveraging built-in features, optimizing query performance, using appropriate storage tiers, securing data properly, and taking advantage of cost optimization features are critical for cost optimization. Implementing a consumption governance model with clear cost optimization goals, usage monitoring and alerts, cost allocation and chargeback, and continuous optimization can help enterprises ensure that they are optimizing their Snowflake spend while still meeting their performance requirements.
