A Comprehensive Guide to Snowflake Migration: Best Practices and Strategies

Snowflake is a cloud data warehousing solution that has become popular for companies with large data volumes.

However, moving databases from an existing data platform to Snowflake can be complicated. You may face challenges in adapting existing pipelines that require custom code or integrating data from legacy systems to Snowflake’s environment.

This article will provide you with a complete overview of the procedure, along with recommendations and strategies to ensure a seamless Snowflake migration.

An Overview of Snowflake Data Warehouse

• Snowflake is a popular data warehouse that provides cloud computing and storage functions. Its decoupled architecture allows the compute and storage layers to be scaled independently, giving you flexibility to manage your data. 
• Snowflake is a software as a service (SaaS) that may be hosted on Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). Snowflake runs entirely on public cloud infrastructure, offering exceptional resilience and minimal maintenance.

Why Migrate to Snowflake?

Let’s discuss the main reasons for Snowflake migration before moving on to the migration process:

• Specifically Designed for the Cloud: Snowflake is a cloud-native data platform with infinite scalability. It can manage any data volume without the need for on-premises infrastructure, eliminating capacity concerns and streamlining resource management.
• Getting Rid of Data Silos: Snowflake provides a single source of truth for all data, integrating data from multiple sources into a single platform. This lets you integrate data seamlessly, collaborate effectively across your company, and make data-driven decisions.
• Scalability: Snowflakes’ architecture allows dynamic resource allocation, adapting to changing workloads to maintain high performance. It ensures that resources scale efficiently to match organizational needs.
• Improved Efficiency: Snowflake’s advanced architecture supports real-time analytics and rapid query execution. Separate computing and storage layers enable swift data loading and parallel processing.

How to Migrate Data to Snowflake?

Let’s look at the data migration to Snowflake with step-by-step instructions. 

Understand the Data

Understanding your data is the first stage in the Snowflake migration process. It entails analyzing data patterns to identify inconsistencies, classifying data according to importance, assessing its caliber, and comprehending complex data relationships. Ensuring you have an accurate and usable dataset throughout the process is essential.

Select a Method for Migration

Selecting a Snowflake migration strategy is the next step after data analysis. Here are some methods you might want to think about:

Manual Migration Using Snowsight

Data can be manually migrated from your previous system to Snowflake using Snowsight, Snowflake’s integrated development environment (IDE). This method benefits smaller data sets, but it can be time-consuming.

Limitations of Manual Migration Using Snowsight 

• Data Volume: Query performance can significantly degrade when dealing with data volumes exceeding 1 million rows, making it unsuitable for big data analytics.
• Pricing: Depending on your Snowflake membership and usage, you may incur extra expenses when using the service.  

Migration with Snowflake’s Replication Command

You can use Snowflake’s replication command COPY INTO to transfer data from your existing system seamlessly and in real-time. 

Limitations of Using COPY INTO Command

• Schema Matching: The schema of your data files must exactly match the schema of the target in Snowflake. Any discrepancies will lead to errors during the loading process.
• Security Configuration: The COPY INTO command requires proper security configurations to access and stage data in external locations. Misconfigurations can lead to security vulnerabilities or access issues. 

Select & Split Data

After choosing your migration to the Snowflake technique, the next step is to split the data using a file splitter. This lowers the possibility of errors and streamlines the migration procedure.

Stage the Data

Next, you must migrate your data to the Snowflake staging area. For this to work, you’ll need the SnowSQL command line tool, which you can easily download from the Snowflake platform. You can employ the zqvq command to easily stage local files for loading onto a Snowflake stage. 

Auto-Compress Files

Remember to auto-compress local files in Snowflake to increase speed while staging them. For the best results, check to see if the files have been explicitly compressed using GZIP format.

Transfer the Data

During this step, cloud migration configures the Snowflake environment and initiates the migration procedure. After configuring databases, warehouses, and accounts according to your requirements, you can transfer your previous system’s data to Snowflake.

Verify Cloud Migration

Data migration testing is essential to ensure that your migrated data in Snowflake is accurate, accessible, and ready for use.

Even though these processes appear simple enough, your data migration process in Snowflake can fail. To ensure that your diverse dataset is properly extracted, transformed, and imported into Snowflake, you can turn to ELT tools like Hevo.

Efficiently Integrate Data to Snowflake Using Hevo Data

Efficient data integration to Snowflake is crucial for maintaining data quality and supporting real-time analytics. To save time and energy, you can use Hevo, a real-time ELT no-code data integration platform that has automated data pipelines adaptable to your needs.

It assists you in transforming and enriching your data in a cost-effective manner by integrating with 150+ data sources (40+ free sources). Here are some of Hevo’s top features:

• Data Transformation: Hevo offers drag-and-drop and Python-based data transformation tools that let you clean and modify data before importing it into the desired location. This capability is particularly advantageous for Snowflake migration, as it ensures that your data is not only seamlessly transferred but also optimized and analysis-ready upon arrival.
• Automated Schema Mapping: Hevo detects the incoming data format and replicates it to the destination schema, automating the schema management process. Based on your data replication needs, you can choose between full or incremental mappings. This feature helps create accurate data structures in your Snowflake migration process.
• Incremental Data Load: Hevo ensures effective bandwidth consumption by enabling near real-time transfers of updated data. Thus, your Snowflake database remains up-to-date with minimal latency, enhancing the overall performance.

Get Started with Hevo for Free

What are the Best Practices for Migrating Data to Snowflake? 

Let’s explore the Snowflake migration best practices that you can follow:

• Accelerate Snowflake throughput by obtaining data only from secondary or read-only instances. You can load backup files from production to a lower environment and extract datasets if a read-only instance isn’t available.
• Utilizing native extractors from the system and storing the extracted data on a staging server or NFS will speed up the extraction process.
• Use the appropriate text delimiter tool for your dataset to prevent data corruption during extraction.
• You must conduct a proof of concept during your analysis phase to determine the windows of maximum throughput during peak, non-peak, and weekend hours.
• You must choose the maximum accessible bandwidth rather than the total capacity because several projects sometimes share network bandwidth.
• Consider device-based data transfer if you have large data volumes, slow speed, and generally short timelines.
• For best throughput, utilize native Snowflake data loader utilities. The error-handling features of Snowflake native loaders can also help you quickly find issues in the data load.
• Employ different data warehouses to load data more affordably. Based on the volume of data you have, size the warehouse and set auto-scaling policies with the ability to pause and resume operations. On your Snowflake account, establish resource monitoring and set compute, credit limit, and notification triggers.

Conclusion

1. Migrating to Snowflake can significantly enhance your data management and analytics capabilities, but the process involves various challenges that need careful consideration.
2. Manual migration methods can be time-consuming, error-prone, and resource-intensive, often leading to disruptions and inefficiencies. In contrast, Hevo provides a streamlined, automated approach to Snowflake migration, effectively addressing these challenges.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite firsthand. Also checkout our unbeatable pricing to choose the best plan for your organization.

Share your experience of Snowflake migration in the comments section below!

Frequently Asked Questions

Q1. What is the best way to migrate data from on-premise database to Snowflake?

Follow the below steps to migrate data from on-premise database to Snowflake:

• Create the necessary Snowflake database and schemas you need.
• Finalize your basic loading strategy.
• Consider the incremental loading approach after you have set the initial load.
• After preparing the incremental load, the last step is to carry out the initial complete load one more.

Q2. What are some of the challenges of moving data to Snowflake?

Some of the challenges of moving data to Snowflake are: 

• Manual certification and validations take a lot of time and are prone to mistakes. Your team must invest more time and energy in evaluating the data quality.
• Large data volumes in each iteration can impact data transfer rates, particularly when dealing with huge files.