Posts

In this post, Jay does a deep dive into how you can identify unused dbt models in Snowflake by analyzing your DAG and the historical access patterns.

In this post Andrey of Deliveroo does a deep dive into the Merge statement in Snowflake, how to effectively use it, and how it compares to other alternatives.

In this post we provide a deep dive into all Snowflake Summit 2023 announcements, why they matter and which ones we're most excited about.

A 30-minute conversation about getting started with Snowflake cost optimization and the tools available today.

Ian and Niall share their learnings and best practices on Snowflake cost optimization in a 30-minute presentation at Data Council 2023.

In the podcast, Niall shares practical insights and actionable steps that organizations can take to effectively manage and reduce their Snowflake costs.

Removing unused tables in your Snowflake account can reduce spend, increase security and improve overall warehouse usability. In this post, we show how to identify tables that have not been accessed recently.

Query timeouts are an important tool for Snowflake users looking to control costs and prevent accidental cost spikes. In this post we’ll cover why they’re useful and how they can be configured.

A comprehensive guide to resource monitors and alerting to control spend in Snowflake.

CTEs are an extremely valuable tool for modularizing and reusing SQL logic. They're also a frequent focus of optimization discussions, as their usage has been associated with unexpected and sometimes inefficient query execution. In this post, we dig into the impact of CTEs on query plans, understand when they are safe to use, and when they may be best avoided.

In this post, we show how you can use query tags or comments to achieve better visibility & monitoring for your Snowflake dbt model costs and performance.

Snowflake is an incredibly powerful platform, easily scaling to handle ever-larger data volumes without compromising on performance. But, if not controlled, costs associated with this scaling quickly climb. Whether your goal is to reduce the price of an upcoming renewal, extend your existing contract's runway, or reduce on-demand costs, use the strategies in this post to make significant savings.

Snowflake query tags allow users to associate arbitrary metadata with each query. In this post, we show how you can use query tags to achieve better visibility & monitoring for your Snowflake query costs and performance.

In this guide, we share optimization techniques to maximize the performance and efficiency of Snowflake. Follow these best practices to make queries run faster while reducing costs.

The ability to use different warehouse sizes for different workloads in Snowflake provides enormous value for performance and cost optimization. dbt natively integrates with Snowflake to allow specific warehouses to be chosen down to the model level. In this post, we explain exactly how to use this feature and share some best practices.

A deep dive into how you can optimize queries involving a range join for up to a 300x performance improvement.

An overview of Snowflake's new SQL syntax which allows users to exclude and rename specific columns when running a SELECT * style query.

Ian gives a dive deep presentation into Snowflake’s architecture, the lifecycle of a query, optimal warehouse configuration, table clustering and micro-partition pruning. A detailed methodology for calculating cost per query is also shared.

The Snowflake Query Profile is the single best resource you have to understand how Snowflake is executing your query and learn how to improve it. In this post we cover important topics like how to interpret the Query Profile and the things you should look for when diagnosing poor query performance.

Snowflake users enjoy a lot of flexibility when it comes to compute configuration. In this post we cover the implications of virtual warehouse sizing on query speeds, and share some techniques to determine the right one.

How to effectively utilize Snowflake’s materialized views to allow your table to have multiple, separate cluster keys.

Pairing query design with effective clustering can dramatically improve pruning and query speeds. We'll explore how and when you should leverage this powerful Snowflake feature.

Independently scalable compute and storage is an architecture fundamental of Snowflake. In this post, we’ll be focusing on how Snowflake stores data, and how it can greatly accelerate query performance.

Snowflake's zero-copy cloning feature is extremely powerful for quickly creating production replica environments. But, anyone who has cloned a database or schema with a large number of tables has experienced that it can take over ten minutes to complete. In this post we explore a potential solution.

Understanding the cost of each query in your Snowflake virtual warehouses is critical for cost management. This post provides a detailed overview and working code to help you calculate cost per query.

Ian gives a dive deep presentation into Snowflake’s architecture, the lifecycle of a query, how to use the query profile, optimal warehouse configuration, table clustering and micro-partition pruning. We also get into query best practices & anti-patterns - including some fun things like how to optimize range joins.

An overview of Snowflake's unique elastic data warehouse architecture and its three subcomponents cloud: cloud services, compute and storage