SQL Server to Snowflake Migration: Challenges, Best Practices, and Practical Guide

Embarking on a migration from SQL Server to Snowflake involves navigating technical intricacies and strategic planning. From understanding the fundamental differences in database architecture and SQL dialects to efficient data transfer strategies, we cover key areas to ensure a seamless migration. 

Designed for data teams, this guide offers a comprehensive roadmap, ensuring technical compatibility and alignment with business objectives, thereby streamlining the complex journey from Microsoft SQL Server to Snowflake.

Common Microsoft SQL Server to Snowflake migration challenges

Technical challenges

The transition from SQL Server to Snowflake presents data teams with a range of technical hurdles that demand careful planning and strategic approach. This section explores two major challenges: the differences in database architecture and the distinct SQL dialects of each platform. 

Architecture differences

SQL Server, a traditional relational database management system (RDBMS) often hosted on-premises, uses a shared-disk architecture. This means it relies on a single instance to manage the database, leading to potential bottlenecks in performance and scalability. 

In contrast, Snowflake employs a unique multi-cluster, shared-data architecture. This architecture separates storage and compute functions, allowing for independent scaling of each, resulting in highly efficient resource utilization and performance. Snowflake's design also offers near-infinite scalability and concurrency without compromising on performance. 

Understanding these fundamental differences is key in planning a migration strategy, as it influences data modeling, performance tuning, and resource allocation.

SQL Server’s architecture explained

At its core, SQL Server operates with a three-layered architecture consisting of the Protocol Layer, Relational Engine, and Storage Engine.

Source: Guru99

The protocol layer serves as the front-end interface for clients to communicate with the SQL Server. It ensures secure connections and manages how data requests are sent and received.

For compute, SQL Server relies on its relational engine, which is responsible for parsing, interpreting, and executing SQL queries. It serves as the “brain” of SQL server by taking SQL queries from clients, figuring out the best way to execute them, and then actually performing the requested actions on the data. 

The storage engine manages where and how data is stored on physical storage devices (like hard drives). It makes sure data is stored safely, handles multiple users accessing the same data at once, and keeps a log of changes for recovery. 

In modern SQL Server deployments, you can decouple compute and storage by utilizing technologies like Azure SQL Database, SQL Server on Azure Virtual Machines, or SQL Server Big Data Clusters. These solutions allow you to independently scale your compute resources (CPU and memory) and storage capacity as needed. This separation of resources enhances flexibility, scalability, and cost-efficiency.

Snowflake’s architecture explained

Snowflake's architecture represents a significant departure from traditional database systems like SQL Server, with its innovative design focusing on separating data storage from compute resources. This architecture allows for a highly scalable and flexible data management environment.

In Snowflake, data storage is handled independently from compute processing. The data is stored on cloud-based storage services (such as Amazon S3, Azure Blob Storage, or Google Cloud Storage), allowing for virtually unlimited data storage capabilities. This data is stored in a columnar format, which is optimized for fast retrieval and efficient query performance, especially for analytical workloads. Snowflake manages the organization, file size, structure, compression, and metadata of the data storage layer, abstracting these details from the users.

On the compute side, Snowflake utilizes what it calls "virtual warehouses" to perform data processing tasks. A virtual warehouse is essentially a cluster of compute resources that can be scaled up or down independently of the storage. This means that you can increase or decrease your compute resources based on your current needs without affecting the underlying data. Each virtual warehouse operates independently, allowing multiple queries or jobs to run concurrently without contention.

Source: Snowflake

Benefits of migration to Snowflake

Enhanced scalability and elasticity:

A key benefit of migrating from SQL Server to Snowflake is the substantial improvement in scalability and elasticity. Unlike SQL Server's shared-disk architecture, Snowflake's cloud architecture allows for dynamic scaling: you can adjust compute resources independently of storage, providing flexibility to handle workload variations. This means that during periods of increased demand, you can scale up resources quickly and efficiently, a stark contrast to SQL Server, where scaling often involves significant hardware investment and complex planning.

Cost-efficiency in resource utilization:

When comparing Snowflake with SQL Server, the cost-efficiency of Snowflake's pay-as-you-go model stands out. In SQL Server, organizations often have to invest in resources to manage peak loads, leading to underutilization during off-peak periods. Snowflake's model, which allows you to pay only for the storage and compute you use, presents a more economical approach, especially for businesses with fluctuating data processing needs. 

Superior concurrency and multi-tenancy:

Snowflake's ability to handle these diverse workloads without performance degradation is a considerable advantage over SQL Server. SQL Server, particularly in its standard configurations, can struggle with high concurrency levels, leading to performance bottlenecks. Snowflake's architecture, with isolated compute clusters, enables efficient handling of multiple, simultaneous queries and jobs. This is especially beneficial for organizations where different teams or departments need to access and analyze data concurrently. 

SQL dialect differences

There are several notable SQL dialect differences between SQL Server and Snowflake; these differences are crucial for data engineers and developers to understand when migrating between the two platforms.

SQL Server SQL

Transact-SQL (T-SQL) is SQL Server's extension of SQL, offering additional procedural programming elements. T-SQL includes variables, procedural logic, control flow, and local variable declaration, enhancing SQL's capabilities. It's particularly powerful for writing complex stored procedures and triggers, allowing for sophisticated data manipulation and transaction control within the database.

Snowflake SQL

Snowflake utilizes a dialect of SQL that closely adheres to ANSI SQL, making it familiar to most database professionals. Its SQL capabilities include robust support for JSON and semi-structured data, advanced analytical functions, and efficient data warehousing operations. Snowflake's SQL simplifies complex queries, offering high performance without the need for extensive optimizations or tuning.

Example of dialect differences between SQL Server and Snowflake: date-related operations

Let's consider a distinct example that highlights the differences in handling date-related operations in SQL Server and Snowflake, which often becomes evident when using a SQL translator.

Query in SQL Server’s T-SQL

In SQL Server, to get the name of the weekday for the current date, you might use the DATENAME function as follows:

Equivalent Query in Snowflake SQL

In Snowflake, TO_DAYNAME is used with CURRENT_DATE() instead:


These functions are not only syntactically different but also conceptually distinct, reflecting each system's unique approach to handling date and time operations. When translating from SQL Server’s T-SQL to Snowflake's SQL, these differences in date-time functions are significant and require careful consideration.

Other notable SQL syntax differences can be found in transactions handling, temporary tables, and user-defined functions. SQL Server uses T-SQL specific syntax for transactions and offers extensive support for complex stored procedures. In contrast, Snowflake's simpler, ANSI-compliant SQL lacks some T-SQL features but excels in handling JSON and semi-structured data.

Using a SQL translator

Utilizing automated tools to convert SQL code to modern ones greatly facilitates the migration of large codebases. This accelerates the migration while preserving the crucial business logic embedded in the original SQL scripts. Using a SQL translation tool also reduces the effort and potential errors that typically arise from manual code conversion.

Datafold Cloud's SQL Dialect Translator

Datafold's SQL Dialect Translator, integrated within its platform, eases the process of converting date computations and window functions from SQL Server to Snowflake. It efficiently handles the adaptation of SQL code to new contexts, offering significant savings in time and labor.

Business challenges 

Migrating from SQL Server to Snowflake presents several business challenges that organizations must navigate to ensure a smooth transition. Beyond technical aspects, this transition involves strategic planning and change management. We look at the top three concerns here. 

Cost management

The shift from a capital expenditure model (on-premises SQL Server) to an operational expenditure model (cloud-based Snowflake) requires a reevaluation of budgeting and cost management strategies. Snowflake's usage-based pricing model, while flexible, can lead to unexpected costs if not managed properly. Organizations need to develop a clear understanding of their usage patterns and align them with Snowflake's billing model to control expenses effectively.

Data governance

Migrating to a cloud-based platform like Snowflake raises concerns around data governance and security. Companies must adapt their policies to the cloud environment, addressing data privacy, compliance with regulatory standards, and ensuring secure data transfer and storage. This involves a thorough understanding of Snowflake's security features and potentially restructuring how data is managed and accessed.

Performance tuning

While Snowflake offers automatic scaling, understanding and fine-tuning the system for specific use cases is essential. This includes optimizing queries for Snowflake, managing warehouse sizes, and understanding the cost implications of various performance strategies.

4 best practices for SQL Server to Snowflake migration

Beginning the journey from SQL Server to Snowflake may seem daunting, yet with the right strategies, it can become a streamlined and manageable process. Our comprehensive guide presents best practices specifically designed for this migration. It serves as a strategic roadmap that balances both your technical requirements and business objectives. 

Let's take a quick glance at the core strategies outlined in our guide to optimize your migration:

  1. Plan and prioritize asset migration

Successful migration planning means using column-level lineage to focus on essential data assets while pinpointing those that may be phased out in Snowflake. Start by transferring data consumption points prior to data production pipelines. This simplifies the transition process and reduces the load on the SQL Server system.

Datafold Cloud's column-level lineage
  1. Lift and shift the data in its current state

Implementing a lift-and-shift approach, made easier through tools like Datafold’s SQL Dialect Translator, streamlines the migration process. This lessens the need for ad hoc code refactoring, thereby cutting down on complexity, resource demands, and accelerates the migration schedule.

  1. Document your strategy and action plan

Maintaining comprehensive documentation of your migration strategy and action plan is crucial. It helps in securing stakeholder approval, identifies potential challenges for prompt resolution, and guarantees alignment among all teams involved. This fosters transparency and unified collaboration throughout the migration process.

  1. Automate validation between legacy and new systems

Utilizing automated validation tools, such as Datafold Cloud’s cross-database data diffing, plays a key role in verifying data accuracy and consistency between SQL Server and Snowflake. This automated process expedites the validation process, maintains data quality, and bolsters stakeholder trust in the migration.

Datafold Cloud's cross-database diffing

Putting it all together: SQL Server to Snowflake migration guide

Successfully transitioning from SQL Server to Snowflake necessitates a combination of technical knowledge and efficient project management. By following the best practices outlined in our guide, your team can effectively manage this change. We share some tips on how to streamline this entire process:

  1. Plan the migration from SQL Server to Snowflake

Your migration strategy should include a well-defined timeline, resource allocation, and methods for engaging stakeholders. Assess your existing SQL Server setup, understand interdependencies within your data, and set specific objectives for the shift to Snowflake.

Conduct column-level lineage analysis to identify critical data assets for migration. Start by moving data endpoints such as BI tools and analytics dashboards to Snowflake, ensuring uninterrupted data access and minimizing operational disruptions.

  1. Prioritize data consumption endpoints first

Migrate user-facing data endpoints, like analytics queries and applications, to Snowflake ahead of data production pipelines. This step involves replicating data from SQL Server to Snowflake, allowing direct querying in Snowflake by data users and applications.

  1. Implementing lift-and-shift from SQL Server Snowflake

Adopt a lift-and-shift approach to ease the migration, and be mindful of any architectural and SQL dialect differences between SQL Server and Snowflake.

Use tools like Datafold’s built-in SQL Dialect Translator to automate the conversion of SQL code. Then, perform data diffs to ensure 1-to-1 table parity between the SQL Server and Snowflake databases.

  1. Secure stakeholder approval

After stabilizing the migration, seek approval from stakeholders. Use data diff tools to demonstrate complete parity between SQL Server and Snowflake, reinforcing the effectiveness of the migration.

  1. Deprecate old assets

Announce the phasing out of old SQL Server resources, and distribute comprehensive parity reports to stakeholders, supporting their move to the Snowflake environment.


If you're curious to learn more about how Datafold can help your data team make migrations like the one from SQL Server to Snowflake more efficient, there are several ways to start:

  • Talk to a data migrations expert and tell us about your migration, tech stack, scale, and concerns. We’re here to help you understand if data diffing is a solution to your migration concerns.
  • For those who are ready to start playing with cross-database diffing today, we have a free trial experience of Datafold Cloud, so you can start connecting your databases as soon as today.

Migrations are hard and often span years. Datafold streamlines this through automation, enabling your team to prioritize what’s important: delivering trusted, quality data to your organization.