On a day-to-day basis, Snowflake teams identify opportunities and help customers implement recommended best practices that ease the migration process from on-premises to the cloud. They also monitor potential challenges and advise on proven patterns to help ensure a successful data migration.

This article highlights nine key areas to watch out for and plan around in order to accelerate a smooth transition to the cloud. Additionally, this blog will shed light on some of Snowflake’s proven features to help you optimize the value of your migration efforts.

Migrating enterprise data to the cloud can be a daunting task. However, when executed properly, it can be both efficient and far less challenging. Leveraging Snowflake’s built-in features can further alleviate some of the common pain points associated with the migration process.

The areas of focus in this article are:

1. Data compression

2. Initial data uploads 

3. Ongoing data uploads

4. Data set prioritization

5. Data lifecycle management

6. Data security and encryption

7. Data validation

8. Disaster recovery 

9. Multiple software environments

Data compression

Data compression is crucial for conserving bandwidth when transferring data from on-premises to the cloud. There are several ways to compress data before uploading it. For instance, gzip is a reliable compression method. When loading data into Snowflake from Amazon S3 buckets, data compression can optimize the process, improving efficiency and reducing transfer time.

How Snowflake can help: If files are compressed using gzip or another widely used format, Snowflake can directly ingest the compressed data without requiring manual decompression. Alternatively, if your files are uncompressed on a local drive, Snowflake will automatically compress them using gzip — unless compression is explicitly disabled or a different compression method is specified. This built-in feature further helps conserve bandwidth during file uploads, making the migration process more efficient.

Initial data uploads

Every enterprise manages vast amounts of data spread across different formats in on-premises systems. A hybrid approach, where some data sets remain on-premises and some are moved to the cloud, may seem appealing to ease the upfront burden, but that will likely be much more challenging to manage long-term. With a hybrid approach, you are tasked with managing two separate sets of infrastructure, potentially different formats, and a federated model is likely time-consuming and expensive to use.

Data size can range from a few gigabytes to multiple terabytes. Handling a few gigabytes (GBs) is relatively straightforward, but migrating data in the terabyte range can pose logistical challenges. To help ensure success of this massive undertaking, a one-time, tamper-proof transfer method is essential to promote data accuracy and maintain security controls throughout the process.

How Snowflake can help: Every major cloud service provider (CSP) offers solutions to assist with large-scale data transfers. AWS provides Snowball, Microsoft Azure offers Databox, and Google has the Transfer Appliance to facilitate one-time, massive data migrations. Since Snowflake is compatible with these CSPs, once the offline transfer is complete and the data is available in the cloud, ingesting it into Snowflake for further processing becomes a seamless process.

Ongoing data uploads

While one-time uploads can be managed using the solutions discussed above, customers must also consider how to handle new data generated on a daily basis. This process could continue indefinitely or for a fixed period until the on-premises architecture is fully retired and data is piped directly into your cloud platform. To meet these ongoing data load requirements, pipelines must be built to continuously ingest and upload newly generated data into your cloud platform, enabling a seamless and efficient flow of information during and after the migration.

How Snowflake can help: Snowflake offers a variety of options for data ingestion. For real-time, continuous loading, Snowpipe is ideal for trickle feeds. For batch loading, the powerful COPY command can be utilized. For low-latency streaming use cases, Snowpipe Streaming is ideal. Additionally, Snowflake’s robust data integration ecosystem tools enable secure and controlled incremental uploads without the need for complex infrastructure. This flexibility allows data ingestion to be efficient and reliable, with minimal disruptions during the migration process. You can learn more about data ingestion best practices with Snowflake in this three-part series: Part 1, Part 2, Part 3.

Data set prioritization

Enterprises often face the challenge of different teams competing to migrate their data to the cloud as quickly as possible. If not managed systematically, this can lead to multiple copies of the same data being stored in the cloud, creating inefficiencies. To avoid this, it’s crucial to prioritize data sets and migrate them in a structured sequence, starting with “master data sets” before moving on to others.

While Snowflake facilitates seamless data migration and prioritization, many of our customers have demonstrated that thorough planning and careful identification of data sets are key to ensuring the right data is moved first, preventing unnecessary duplication. It can be as simple as listing down the data sets in a central location like Sharepoint and assigning priority to help plan appropriately and reviewing the list on a periodic basis.

How Snowflake can help: While there are numerous methods for uploading data sets and we have discussed a couple of them already in this blog, the option to load files using Snowflake’s web interface stands out as one of the easiest and often the quickest way to ingest data. This user-friendly approach allows business users to swiftly transfer files into Snowflake, streamlining the data-ingestion process.

Data lifecycle management

Data lifecycle management is a critical area for effective cost management in the cloud. Maintaining data in the cloud incurs operating costs, so establishing a robust data retention policy should be a foundational aspect of a customer’s cloud strategy. While regulatory and compliance requirements may prevent complete data deletion, implementing an expiry model for data that doesn’t fall under these retention requirements is recommended. This approach helps optimize storage costs.

How Snowflake can help: Snowflake offers several features that ease data lifecycle management, including various data storage considerations. These, combined with our cost optimization tools like budgets, help reduce storage costs. Additionally, our product team is working on new policy-based capabilities to make the lifecycle of data seamless to manage.

Data security and encryption

Data security is an important area that organizations consider when moving their data to the cloud. The security team has to be brought on board with the idea that enterprise data will be leaving the four walls of the enterprise and moving to the cloud. Features like private connectivity, network policies and encryption are some of the widely adopted methods for securing data during movement to the cloud.

Some organizations have established security policies that require data to be encrypted before it leaves their data center. Encryption methodologies, such as RSA and AES, can be applied at the file level to enable data protection during this process. Once the data is in transit to your cloud platform, comprehensive data protection policies can be implemented to safeguard the data both in transit and at rest, providing an additional layer of security throughout the migration process.

How Snowflake can help: Snowflake offers end-to-end encryption to help organizations meet their compliance requirements, keeping the data secure throughout its lifecycle. Additionally, Snowflake provides robust key management solutions once the data is under its management, further enhancing security and control over sensitive information. In addition, Private Link and limiting HTTP request acceptance from certain IP addresses (also known as “IP whitelisting”) help to limit data access.

Data validation

Data validation is crucial for data quality and instilling confidence in business users as they utilize this information. Some key metrics that customers commonly use for validation include the number of unique values, number of null values, data set freshness and duplicate values. Regularly logging and reviewing these metrics at defined intervals helps maintain data quality and supports informed decision-making for the business groups.

How Snowflake can help: Snowflake offers a variety of data metric functions that can run in the background to help identify anomalies and support data validation. These functions continuously monitor the data, enabling proactive detection of issues and promoting the overall quality and reliability of the data.

Disaster recovery

The level of disaster recovery (DR) preparedness required for a cloud differs significantly from an on-prem system. By default the CSPs have established standards to help DR strategies for maintaining data copies. While on-premises solutions often necessitate extensive planning and resources for data redundancy and to adhere to RPO and RTO policies for recovery, CSPs typically offer built-in DR capabilities that streamline these processes and enhance data resilience. This allows organizations to leverage the CSP’s infrastructure for more efficient and effective disaster recovery. Focusing on application needs from a data-availability standpoint helps in mitigating business risks.

How Snowflake can help: One of the key strengths of Snowflake is its capability to provide  seamless business continuity across different clouds and regions using Snowgrid, which is very easy to implement without a lot of infrastructure plumbing in the backend. In addition, Snowflake provides several built-in features to support disaster recovery, including automatic replication, time travel, failover/failback and secure data sharing . 

Multiple software environments

In the cloud, the need for multiple environments (such as development, testing, staging and production) often persists, similar to on-premises setups. However, cloud platforms offer greater flexibility and scalability which can simplify management. One can save on costs as the cloud allows for on-demand allocation of resources, helping enterprises stand up and tear down environments as needed and paying only for what they use. In addition, automation tools for deployment and maintenance of the environments make it a breeze to manage all of the logistics. User testing, performance testing, regression testing, security testing and more become very easy due to the nature of the cloud.

How Snowflake can help: Snowflake helps enterprises to save time, effort and money by providing a centralized platform for easy access, zero copy cloning for instant copies without replication across environments, integration with CI/CD tools and instant access to resources to help with different types of testing without the added management of maintaining the infrastructure needed to support these capabilities.

Closing thoughts

While we have discussed the nine broad areas where we have seen customers struggle and the potential solutions, this is by no means an exhaustive list. With careful planning and the right tools, migrating enterprise data to the cloud can make a cumbersome task easy to plan and manage. Snowflake’s robust set of features, ranging from data compression, upload options, data lifecycle management and enhanced security, help accelerate that journey to the cloud while minimizing risks.

By focusing on the critical areas discussed in this article, organizations can optimize their cloud migration efforts, ensuring a smooth transition that aligns with both operational needs and long-term business goals. With Snowflake as a trusted partner by your side, the journey of your enterprise data to the cloud is smooth.  For further reading, please visit Snowflake’s dedicated migration page, Migrate to the Cloud, and learn more about our native code-conversion tooling, SnowConvert.