This blog was co-written by Ayush Kumar Singh and Mayank Singh
When it comes to data management, one question that often arises is whether to lift and shift or modernize. In this context, ‘lift and shift’ refers to the process of moving data from one platform to another without making any changes to the data or how it is structured.
Modernization, on the other hand, involves making changes to the data or how it is stored and accessed in order to improve efficiency, security, and performance.
For organizations that are considering moving their data to the Snowflake Data Cloud, the question of lift and shift vs. modernization is an important one to answer.
In this blog, we will explore the pros and cons of each approach and help you determine which option is the best fit for your organization.
Lift and Shift: Pros and Cons
The main advantage of lift and shift is that it is a relatively simple and quick process. All you have to do is export your data from the current platform and import it into Snowflake. There is no need to worry about restructuring the data or changing how it is stored or accessed.
This means that you can get your data into Snowflake and start using it almost immediately.
There are a few other advantages to lift and shift as well. For example, it can be a good option if you have a large volume of data that would be difficult to migrate manually. It can also be a good choice if you have a lot of custom code or applications that rely on your current data structure and want to avoid breaking them by making changes.
However, lift and shift also has some significant drawbacks. One of the main issues is that it doesn’t take advantage of the features and capabilities of Snowflake. For example, Snowflake’s flexible data warehousing structure and ability to handle semi-structured data can be incredibly useful, but you will only be able to take advantage of these capabilities if you lift and shift your data.
Another potential problem with lift and shift is that it can be inefficient. If you don’t make any changes to your data structure, you may end up with a lot of redundant or unnecessary data that take up valuable storage space and slows down queries. You may also miss out on opportunities to improve data security and compliance by leveraging Snowflake’s built-in security features.
Modernization: Pros and Cons
On the other hand, modernization involves making changes to your data structure and how it is stored and accessed to take advantage of all of the capabilities of Snowflake. This can be a more complex and time-consuming process, as it requires a thorough analysis of your data and how it is used in order to identify opportunities for improvement. However, the benefits of modernization can be significant.
By restructuring your data and leveraging Snowflake’s features, you can improve the performance and efficiency of your data management processes. You can also take advantage of Snowflake’s security and compliance features to better protect your data and meet regulatory requirements.
One of the main advantages of modernization is that it allows you to take a more strategic approach to data management. Rather than just moving your data from one platform to another, you can use the move to Snowflake as an opportunity to re-evaluate your data needs and develop a plan for managing and using your data more effectively.
One potential drawback of modernization is that it can be a more complex and time-consuming process. It may require significant investment in terms of time, resources, and expertise to identify opportunities for improvement and implement changes. It may also require major changes to your current data structure and processes, which can be disruptive to your organization.
Another risk of modernizing the data stack is that it involves changing not only the platform but also the business logic of the applications; this can be a daunting and complex task for many organizations as they have to ensure that the new logic is consistent, accurate and compatible with the existing data sources and processes.
Moreover, changing the business logic might affect the applications’ performance, security, and reliability, which may require additional testing and validation. Hence, modernizing the data stack can introduce additional risks and challenges for organizations migrating their data to the cloud.
Which Option Is Right for You?
The decision of whether to lift and shift or modernize your data in Snowflake will ultimately depend on your specific business needs and goals. If your current infrastructure serves you well and you’re happy with the performance and scalability, a lift and shift to Snowflake may be your best option.
However, if you’re looking to take advantage of Snowflake’s modern features and capabilities, such as its built-in machine learning capabilities and data sharing features, modernizing your data in Snowflake may be the way to go.
Ultimately, the key to deciding is to understand your data and business requirements fully and to work with a Snowflake expert to determine the best approach for your organization.
One possible strategy for migrating applications to the cloud is first to do a lift and shift and then modernize and refactor the applications in a second iteration. This can be a valid approach for several reasons.
- It can allow the organization to quickly move the applications to the cloud without spending too much time and resources on redesigning them.
- It can enable the organization to take advantage of some of the cloud benefits, such as scalability, availability, and cost savings.
- It can provide the organization with an opportunity to evaluate the performance and functionality of the applications in the cloud environment and identify the areas that need improvement.
- It can facilitate the modernization and refactoring process by allowing the organization to leverage the cloud-native tools and services that can help with code analysis, testing, deployment, and monitoring.
For example, one of our clients chose to initially migrate their data from an on-premises Exadata data warehouse and Quobol data lake to Snowflake. Once the data is completed, they intend to create a data vault in Snowflake to modernize further and optimize their data management.
What Does phData Recommend?
As a data engineering and machine learning consulting company, phData has literally helped hundreds of customers migrate their data using the lift & shift and modernization approach. In our experience, the answer to this question is not necessarily one or the other.
Many of our customers choose lift & shift and then modernize. Starting with lifting and shifting and then modernizing is our recommended approach since you get the best of both worlds.
Our number one engineering principle is to change one thing at a time. When you change two or more things at once, it makes it hard to know what broke things or attribute what made things better. This method supports that. This is a simplification of “Make It Work, Make It Right, Make it Fast.”
Deciding whether to lift and shift or modernize your data in Snowflake is a crucial decision that will have a significant impact on your organization’s data management processes. Ultimately, the best approach for your organization depends on your specific business needs and goals.
Consulting with Snowflake experts, like phData, can help you make an informed decision and ensure a successful migration to the Snowflake Data Cloud.