How AI-Driven Data Engineering Tools Can Simplify Snowflake Optimization

Why Optimizing Snowflake Is Needed

Snowflake provides a secure and scalable database engine with a unique architecture that decouples compute and storage resources, allowing you to pay only for the resources you use.

But sometimes, Snowflake users need help to optimize their Snowflake instances. Here are a few reasons why.

• Less than a decade old, Snowflake is on a journey to provide the myriad tuning tools in a typical Oracle data engineer’s toolbox.

• Snowflake users are typically a broad mix of business and technical users with varying levels of database proficiency. This user mix can result in poorly written queries, sub-optimal schemas, and inefficient data ingestion and consumption patterns.

• Snowflake’s ease of scaling up/out can be viewed as a performance shortcut, allowing users to bypass database optimization best practices.

• Usage-based costs often skyrocket as query workloads increase. Computing costs are the dominant factor, often an order of magnitude greater than storage and cloud services costs.

Getting Your Arms Around Snowflake Optimization

Ordinarily, the best way to improve performance and cut computing costs is to optimize queries – a win-win.

Unfortunately, optimizing individual queries alone may help your Snowflake performance but won’t necessarily reduce your total computing costs. The reason is that Snowflake charges you a certain number of credits for every second your warehouse runs, even if it is underutilized.

Plus, the data engineering efforts required can be huge. Consider an organization issuing 100K queries monthly (larger organizations might perform tens of millions). Optimizing just 1% of the slowest queries would require its data engineers to inspect and improve a thousand queries—a daunting manual effort, far more than most organizations can execute.

What Snowflake Users Need to Optimize

Snowflake optimizations fall into four domains, each with unique tools and best practices. These include:

• Ingestion Optimization

• Query Optimization

• Schema Optimization

• Warehouse Optimization

Rather than your data engineers doing this work manually, these optimizations can be done automatically using AI-driven, intelligent data engineering tools like those discussed in this recent Insight Jam Live panel discussion.

Intelligent data engineering tools save you time, money, and engineering resources; as such, automated optimization makes more sense.

How Does AI-driven Snowflake Optimization Work?

Explaining how AI-driven Snowflake optimization works requires more specifics than this short article allows.

Instead, check out this short video, Snowflake Optimization Guided Tour.

You can then drill down the details with this guide. This “must-read” for your Snowflake users and administrators helps your team understand Snowflake optimization challenges and solutions. It provides actionable ways to reduce costs by 30-50% while optimizing performance.