Table of Contents
Introduction
At Bacancy Technology, we’ve helped organizations migrate to Snowflake from Oracle, SQL Server, Teradata, Netezza, Hadoop, Amazon Redshift, and several legacy data warehouse platforms. While every Snowflake migration project is different, many of the assumptions we hear at the start of these projects are surprisingly similar.
Over the course of different migration projects, we’ve found that addressing these assumptions early leads to better planning, fewer surprises during execution, and smoother adoption after going live.
Based on the migration projects we’ve delivered across healthcare, banking, finance, and insurance clients, we’ve put together some of the most common Snowflake migration myths, or assumptions that we have seen across our clients and the broader market, and uncovered the reality behind each of them.
Top 8 Snowflake Migration Myths, And the Reality Behind Them
Discover the most common Snowflake migration myths, the reality behind them, and practical insights from Bacancy Technology’s data engineers based on client engagements.
Myth 1: Snowflake Migration Is Just a Lift and Shift Project
We’ve seen quite a few clients approach a Snowflake migration as a lift and shift project. The migration plan is built around moving the data and pipelines that are already there, with the expectation that modernization can happen after the platform is live.
The Reality:
That usually isn’t how the project unfolds. Moving the data is only one step of the Snowflake migration process. Once teams begin reviewing pipelines, reporting logic, security policies, downstream applications, and data models, the scope becomes much clearer.
Looking back at the Snowflake migrations we’ve delivered, moving the data has usually been the most predictable part of the project. The harder decisions come much earlier, around architecture, dependencies, governance, and how the platform should operate once the migration is complete. That’s what makes a Snowflake migration much more than a lift and shift exercise.
Myth 2: Everything Should Be Migrated at Once
We’ve seen organizations plan their Snowflake migration around a single rollout, with every workload expected to move to the new platform at the same time. The assumption is that one migration is simpler to manage than several smaller ones.
We’ve seen this assumption come up more often when organizations are working against licensing deadlines or infrastructure refresh cycles. The pressure to finish quickly often makes a phased migration feel like an unnecessary delay.
The Reality
The projects we’ve worked on usually move in smaller phases. Critical workloads, reporting, and data pipelines are validated first, followed by the rest of the platform. That gives engineering teams room to fix issues without affecting every business function at the same time.
For several of our banking and insurance engagements, that approach made business validation much more manageable. Finance teams could sign off on reconciliations, operations teams could validate critical reports, and engineering teams could address issues before expanding the rollout. That level of control is difficult to achieve when every workload moves at the same time.
Myth 3: Moving to Snowflake Automatically Reduces Costs
Snowflake’s pricing model has led many organizations to assume that migrating alone will lower their data platform costs. We hear this most often during the early evaluation stage, before workloads and usage patterns have been reviewed.
Cost is usually one of the reasons organizations consider Snowflake in the first place. The mistake is expecting the platform to deliver those savings regardless of how it’s implemented.
The Reality
A Snowflake migration changes the platform, not the workload. If the same queries, warehouse configurations, data retention policies, and usage patterns are carried into the new environment, the monthly costs often look very similar. The migration creates the opportunity to optimize costs, but it doesn’t do it automatically.
Our Snowflake consultants have worked with organizations to reduce their Snowflake spend within the first few months after migration, and others that saw costs increase before they stabilized. The difference wasn’t Snowflake. It was how compute was configured, how workloads were scheduled, and how the platform was managed after the migration.
Myth 4: Most of the Snowflake Migration Can Be Automated
We’ve worked with teams that expected a migration tool to handle most of the project. Once the database objects were converted, the remaining work was expected to be limited to testing and production rollout.
The Reality
The migration tools can significantly reduce the effort involved, but they don’t verify whether the migrated environment behaves the way the business expects it to.
Across the Snowflake migration projects we’ve delivered, the larger effort has usually gone into validating business logic, reconciling reports, reviewing downstream dependencies, and ensuring existing applications continue to work after the migration. Those are decisions that still require engineering expertise and business context, regardless of the migration tool being used.
Myth 5: Every Existing Workload Needs to Move to Snowflake
For companies planning migration to Snowflake, It’s common for migration plans to start with an inventory of everything running on the existing platform. The assumption is that if it’s part of the current environment, it should also exist after the migration.
We’ve seen organizations carry years of reports, scheduled jobs, and datasets that no longer serve an active business purpose simply because they’ve always been there.
The Reality
A migration creates one of the best opportunities to simplify a data platform. Not every workload needs to be recreated, and not every dataset needs to be retained in its current form.
Some of the most successful Snowflake migrations we’ve been part of weren’t the ones that moved the most workloads. They were the ones that reduced unnecessary complexity before the migration was complete.
Myth 6: Snowflake Migration Requires Business Downtime
Business teams often assume they’ll need to pause reporting or accept a period of downtime while systems are moved to Snowflake. That concern is understandable, especially for organizations supporting business-critical operations around the clock.
We’ve had this conversation with clients across banking, healthcare, and insurance, where even short interruptions can affect business operations.
The Reality
Most enterprise migrations are planned to minimize disruption. Parallel environments, staged validation, and controlled production rollouts make it possible to transition workloads without bringing the business to a halt.
The amount of downtime usually has less to do with Snowflake and more to do with the migration strategy. Planning the rollout carefully often has a much bigger impact than the platform itself.
Myth 7: Data Loss Is Unavoidable During a Snowflake Migration
The possibility of losing data is one of the biggest concerns organizations raise before a migration begins. That concern becomes even stronger when large historical datasets or regulated information are involved.
It’s also one of the reasons migration planning often receives so much attention before any technical work begins.
The Reality
The migrations we’ve worked on rely on repeated validation throughout the project instead of waiting until the end. Data reconciliation, parallel testing, and business validation all help confirm that information remains accurate before workloads move into production.
For regulated industries such as banking and healthcare, proving that the migrated data matches the source is often as important as completing the migration itself. That validation becomes part of the project from the beginning, not something left until go live.
Myth 8: Go Live Means the Snowflake Migration Is Complete
Many teams consider the migration complete once data has been moved to Snowflake, reports are available, and users can start accessing the new environment. The assumption is that the major challenges have already been addressed and the remaining work is limited to regular platform operations.
The Reality
Moving data to Snowflake is only one part of the transformation. After migration, teams often need to refine warehouse configurations, review query performance, adjust resource usage, and strengthen governance based on how the platform is being used.
Across the Snowflake migration projects we’ve delivered, we have seen that the organizations that continue improving their environment after migration are usually the ones that achieve better performance, cost control, and long-term value from Snowflake.
Final Thoughts
Most Snowflake migration myths come from assumptions that sound reasonable at the beginning of a project. Migrating everything at once feels faster, automation appears to reduce manual effort, and reaching the new platform can feel like the finish line.
But as we’ve seen across Snowflake migration engagements, these assumptions often overlook the decisions that determine whether a migration actually delivers the expected results.
If you’re planning a Snowflake migration, you can hire Snowflake developers to evaluate your current environment, avoid common migration pitfalls, and build a migration strategy that’s aligned with your technical and business objectives.
Author Bio
Chandresh Patel is a CEO, Agile coach, and founder of Bacancy Technology. His truly entrepreneurial spirit, skillful expertise, and extensive knowledge in Agile software development services have helped the organization to achieve new heights of success. Chandresh is leading the organization into global markets systematically, innovatively, and collaboratively to fulfill custom software development needs and provide optimum quality