Given the present state of the economy, data teams must ensure that they get the most out of their Snowflake investment. The primary function of Snowflake is that of a data warehouse. Data teams can store and handle data with this cloud-based solution. A big worry for data teams is snowflake expenses. Discussions with data teams revealed that minimizing expenses was a top objective for the company. Data teams spend a lot of time looking for methods to save money every few months by hand. One surefire strategy to cut costs with Snowflake is to optimize queries and process less data. Nevertheless, these tasks yield low returns on investment due to the constant work and bandwidth required.
Meet Baselit, a platform for automated Snowflake optimization. Baselit optimizes Snowflake costs automatically, eliminating the need for human intervention. With Beselit, data teams may automate cost optimization in addition to their human work.
How does Baselit function?
In most cases, processing less data is your only option for reducing data processing costs (i.e., query optimization). However, by reducing the computing power required to process the same data, an additional dimension becomes available through Snowflake’s warehouse abstraction, allowing for optimization along this line. With Baselit, optimizing your Snowflake warehouse is a breeze.
Micro-partitions, which include active storage, time travel, fail-safe, and cloning bytes, are used to determine Snowflake’s storage costs. The storage provider’s rates, which are usually around $23 per terabyte (TB) per month, are applied to the average of the data use snapshots taken hourly and averaged over a month to arrive at the cost computation.
Baselit makes it simple to discover your potential savings. Your Snowflake’s savings can be determined by running the provided SQL query.
The two primary parts of Baselit are:
Automated agents: Warehouses with automated agents spend less time sitting idle. Cache optimization (determining when to suspend a warehouse rather than leaving it idle) and cluster optimization (selecting the appropriate spin-down of clusters) are the two main mechanisms by which this occurs.
Autoscaler: Scaler that automates creating SLA-based scaling strategies for multi-cluster warehouses. The Economy and Standard insurance that comes with Snowflake are only sometimes the most cost-effective, and they don’t provide much leeway either. By creating a unique scaling policy for each warehouse, Autoscaler helps you save money and boost performance.
To optimize Snowflake expenses, Baselit has developed additional functionalities as follows:
- dbt optimizer that selects the optimal size of the dbt model’s warehouse automatically via iterative testing
- A “cost lineage” that breaks down spending by teams, roles, and users.
- Recommendations are generated automatically by analyzing Snowflake metadata.
To Sum It Up
Today, optimizing Snowflake costs is essential, not optional, in our data-driven environment. Businesses can utilize Baselit to their advantage to fully utilize Snowflake while maintaining a good profit margin. Baselit lets data teams concentrate on their strengths—driving informed decision-making by collecting important insights from data—with its automated methodology and detailed cost insights.
Dhanshree Shenwai is a Computer Science Engineer and has a good experience in FinTech companies covering Financial, Cards & Payments and Banking domain with keen interest in applications of AI. She is enthusiastic about exploring new technologies and advancements in today’s evolving world making everyone’s life easy.
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