dbt vs Snowflake for data transformations

Snowflake’s high usability had rocketed it to the top of popular cloud data warehouses. The same can be said about dbt. While dbt and Snowflake Dynamic Tables both handle data transformations, they serve different purposes: dbt manages transformation logic and workflow orchestration across warehouses, while Snowflake Dynamic Tables enable near real-time materialization within Snowflake itself.

This guide will cover everything there is to know about dbt vs Snowflake for data transformations so you can make an informed decision about which tool is best for your use case. This guide will walk you through: 

• What Snowflake dynamic tables are
• What dbt is
• Main differences between dbt transformations vs. Snowflake Dynamic Tables
• How to optimize your Snowflake performance

What Are Snowflake Dynamic Tables? 

Dynamic tables were introduced to Snowflake in 2023. This table type is created from existing upstream objects that update (i.e. re-rerun the query) when the parent tables update. These tables use a “target lag” setting that defines the maximum acceptable delay between source data changes and table updates. Snowflake’s scheduler automatically manages refreshes to keep tables current within this specified lag, which can involve multiple refresh cycles instead of fixed intervals. 

Here’s a simplified overview of how Dynamic Tables are created from Snowflake: 

Reference: https://docs.snowflake.com/en/user-guide/dynamic-tables-about#how-dynamic-tables-work 

This type of modeling is especially useful if you’re running any kind of modelings where you need to keep reusing the results. 

What Is dbt?

dbt stands for Data Build Tool and is an open-source data tool and one of the most popular modeling frameworks today. Its SQL-based modeling, centralized code repository, and history of documentation make it a highly accessible and collaborative tool. 

Additional features that make dbt stand out include: 

• Version control
• Analytics engineering
• Data pipeline automation 

dbt includes two key features: 

1. Model logic (your SQL files) that contain a collection of reusable models that you want to reuse.
2. Profile configuration file (in YAML) where you can specify connection parameters for your target environment. Database and schema selection usually takes place in your dbt_project.yml file or individual model configurations.

Note: Use of dbt is not constricted to Snowflake. Other supported integrations include: 

• Databricks
• BigQuery
• Postgres
• Amazon Redshift
• Microsoft Fabric
• Starburst
• Onehouse

dbt vs Snowflake 

Snowflake does not replace to dbt. The two carry many similarities, but many differences as well. Here’s the overall overlap of the two: 

NOTE: The main differences between Snowflake dynamic tables and dbt relate to approach and development workflow. dbt provides more flexible development capabilities with robust documentation and testing. Dynamic Tables gets you native Snowflake integration with automatic refresh abilities. 

Snowflake does offer a Task feature that schedules SQL statements to make data transformation automation easier for the user. Snowflake is designed to handle complex workflows, and has a wide suite of data loading and transformation tools to make the process easier for you. 

Snowflake vs dbt: Use Cases

Take a look at a few common use cases to get a better idea of which tool is best for your specific needs. 

Complex Data Transformation Workflow

When it comes to complex data transformation workflows, dbt stands out. Users can easily build, test, deploy, and document their data transformations in its version-controlled environment. It provides a wide range of functionalities, including: 

• Jinja templating
• Complex SQL generation 
• Data workflow dependency management

Snowflake, on the other hand, has scheduling and chaining task features to help you manage dependencies. But it falls short when it comes to integrated testing and documentation features. 

Winner: dbt

Version Control and Collaboration

dbt again stands when it comes to version control and collaboration, especially for teams looking to implement CI/CD thanks to the following features: 

• Seamless Git integrations
• Robust collaboration tools
• Version control
• Data codebases change tracking

Snowflake does not offer built-in version control features, leaving users to rely on external tools. 

Winner: dbt

Scalability and Performance

This is where Snowflake truly shines. Snowflake is designed to execute SQL scripts at scheduled intervals, no need for compiling scripts that impact performance for larger datasets or complex queries. 

dbt still holds its own here. It was built to efficiently leverage the computing power of the SQL engines it uses to run, and can compile SQL queries executed in the data warehouse. 

Winner: Snowflake

Real-Time Data Processing and Low Latency

If you need real-time data updates with minimal latency, Snowflake Dynamic Tables truly shines. Its Dynamic Tables automatically refresh based on target lag settings, automating data transformation and pipeline management with minimal delay from source changes. 

The end-to-end latency of chained dynamic tables can be kept within your specific target lag with Snowflake’s intelligent refresh scheduling, making Snowflake the ideal choice for: 

• Financial reporting that requires up-to-the-minute accuracy 
• Real-time data analytics dashboards
• Operational metrics that drive immediate business decisions

dbt’s batch-based approach means you’re limited to scheduled rules. This can create potential delays between data updates. 

Winner: Snowflake

Snowflake vs dbt for data transformation: Which is right for me

Both Snowflake Dynamic Tables and dbt are raising the bar for modeling automation. But both also have their limits when it comes to ensuring data quality. dbt tests are a great validation starting point, but aren’t the best with asynchronous deployment at scale across all of your tables. 

Ultimately, the choice between Snowflake Dynamic Tables and dbt depends on your specific needs and existing infrastructure. 

You should choose dbt if you: 

• Have complex transformation logic that requires extensive testing and documentation
• Need robust version control and collaborative development workflows
• Want flexibility to work across multiple data warehouses
• Need advanced templating and code reusability 

You should choose Snowflake Dynamic Tables if you: 

• Need real-time or near real-time data updates
• Want simplified maintenance with native Snowflake integration
• Prioritize performance and scalability for large datasets
• Prefer a managed solution with less operational overhead

You also don’t have to just pick one tool. Many organizations find success using both: leveraging dbt for complex transformations and developmental practices, and Dynamic tables for performance-critical, real-time issues. 

How to Optimize Snowflake Performance  

No matter if you’re using Snowflake with dbt or just dynamic tables, optimizing performance is critical for maintaining fast queries while keeping costs low. 

Key optimization strategies include: 

• Warehouse rightsizing: Automatically scale compute resources based on your warehouse needs to avoid over- or under-provisioning. 

• Query optimization: Identify and remove inefficient queries to prevent excessive resource consumption. 
• Storage management: Use proper lifecycle policies and compression strategies. 
• Monitoring and alerting: Use proactive monitoring to identify performance issues before they hurt your budget. 

The real secret to Snowflake optimization? Looking beyond the basic optimization toolset Snowflake provides to third-party solutions that can automate your optimizations for you, reducing spend by up to 30%. 

Curious to see how much you can save with the right optimization tool? 

Contact Yuki today to see how our automated, plug-and-play optimization platform can help you save, no extra dev work needed.