Cloud Data Migration: Key Insights for Modern Data Leaders

Out of the average IT budget of $329 million in 2024, companies allocated $95 million to cloud products and services. Tech leaders only expect that percentage to increase, highlighting the growing realization that companies must continue to modernize to remain competitive. 

While cloud data migration can have a significant impact—such as 30% cost savings—missteps are common. Cost overruns and project delays are just two of the challenges that organizations often face. 

Designed for modern data teams, this practical framework outlines effective strategies to reduce complexity and risk, a step-by-step migration checklist, and guidance on embedding governance and data intelligence throughout the process. The goal is to transform how your organization delivers value through the cloud, not just how it moves data.

What is cloud data migration?

Cloud data migration is the strategic process of moving data, applications, and business functions from on-premises data centers and systems—or between cloud environments—into a new cloud platform. 

What enables secure and efficient operations across public, private, and multi-cloud architectures? Modern data teams rely on migration tools to extract, replicate, validate, and optimize data. Some of the most typical targets include cloud-native data warehouses, data lakes, and software-as-a-service (SaaS) analytics platforms. 

Most importantly, a successful cloud data migration process hinges on meticulous planning, rigorous validation, and continuous optimization to ensure strong performance, data security, and cost efficiency in the new environment.

Why is migration to the cloud important?

Cloud migration helps organizations stay agile and resilient. Below are eight core business benefits that data leaders can gain from these data migration projects:

• Elastic performance: Instantly scale resources for heavy workloads—like large SQL queries or AI training—then scale down to save costs.

• Unified data management: Centralize datasets to simplify governance, improve data quality, and streamline compliance.

• Enhanced security: Leverage built-in features, such as multi-factor authentication and role-based access, to protect sensitive data and meet regulatory standards.

• Automated disaster recovery: Benefit from geo-redundant backups and automated failovers to minimize downtime and ensure business continuity.

• Cost‑effective storage: Use tiered storage to archive cold data affordably while keeping active data accessible on demand.

• Seamless upgrades: Handle infrastructure updates automatically, reducing operational risk and freeing up IT resources.

• Accelerated analytics: Access integrated tools for real-time dashboards and machine learning, which enable faster, more data-driven decisions.

• Remote collaboration: Empower teams to securely access and analyze data from anywhere to boost productivity and responsiveness.

Together, these benefits show that cloud migration is a foundational step toward building a scalable, secure, and insight-driven data ecosystem.

What are the different strategies for cloud data migration?

In a 2024 study, 63% of IT leaders reported that their organizations had accelerated migrations to the cloud. Successfully completing such migrations requires a thoughtful strategy—or blend of strategies—based on business needs, data sensitivity, downtime tolerance, and budget. Consider seven different options for your migration plan:

1. Rehosting (lift-and-shift)

Rehosting involves moving data and applications to the cloud with minimal changes. It’s ideal for organizations that need a rapid exit from legacy data centers or are facing tight migration timelines. For example, a financial services firm might rehost its reporting database to minimize service disruption. 

While this method is quick and requires little upfront development, it can carry over technical debt and may not optimize cloud costs.

2. Refactoring

Refactoring requires minor modifications to applications or data pipelines to better leverage cloud-native services. This strategy strikes a balance between speed and optimization and often results in better performance and cost savings. A retail company, for instance, might refactor its extract, transform, and load (ETL) jobs to use managed cloud services, improving reliability and scalability. 

However, refactoring demands some development effort and a longer timeline than lift-and-shift.

3. Re-architecting

Re-architecting means redesigning applications and data flows for cloud-native capabilities, such as microservices and serverless architectures. 

This future-proofs critical workloads but increases project complexity and risk of scope creep. As a result, data leaders should reserve this strategy for high-impact, growth-oriented initiatives.

4. Rebuilding

When legacy code is incompatible with the cloud, rebuilding from scratch may be necessary. This allows teams to innovate and optimize fully—but it’s also resource-intensive and time-consuming. Enterprises often rely on this approach for core applications that provide competitive differentiation.

5. Replacing (SaaS adoption)

In some cases, organizations can replace legacy systems with SaaS solutions, such as customer relationship management or ETL platforms. This enables rapid deployment and reduces maintenance overhead, but may limit customization and introduce vendor lock-in.

6. Retiring

Migration is an opportunity to identify and decommission unused datasets or obsolete reports, which reduces costs and security risks. However, it’s essential that teams carefully audit dependencies to avoid disrupting business processes.

7. Retaining (hybrid cloud)

In some cases, critical workloads may have to remain on-premises due to regulatory requirements, legacy system dependencies, or risk tolerance. A hybrid cloud strategy allows organizations to preserve this stability while leveraging the flexibility of the cloud, but it also introduces added complexity in managing and integrating disparate environments.

Your cloud data migration checklist

Use this streamlined checklist to guide a smooth, secure cloud data migration process:

• Set clear objectives: Define what success looks like—for example, improved scalability, lower costs, or stronger security.

• Inventory and prioritize: Catalog all data sources and applications and focus on what’s most critical to migrate.

• Assess infrastructure and choose your provider: Evaluate your current setup and select a cloud provider that meets your needs for reliability, scalability, and cost.

• Assemble your team and plan: Assign roles and responsibilities and establish a realistic migration timeline, complete with key milestones.

• Prepare and map data: Cleanse, transform, and ensure the compatibility of data and integrations before moving them.

• Ensure security and compliance: Implement controls to protect your data and meet regulatory requirements throughout migration.

• Test and validate: Rigorously test functionality and performance in the cloud environment.

• Cut over and optimize: Synchronize final data, switch users to the new environment, and monitor performance for ongoing optimization.

These essentials apply to any migration strategy, allowing you to overcome any challenges during your transition to the cloud. Additionally, it’s wise to have your migration plan formally recorded, as pictured below. This will keep all stakeholders informed and will also enable you to track migration readiness and progress. 

Data intelligence helps craft your cloud migration strategy

Many organizations lack the necessary visibility into their business ambitions in the cloud. Often, they’re also uncertain about where their data resides across legacy silos or whether it’s even ready for migration. 

Hence, the importance of business, technical, relational, and operational metadata to provide transparency and understanding of data. This type of data intelligence plays a crucial role in ensuring that your cloud migration is efficient, secure, and aligned with business goals. Here’s how it can support your strategy:

Data strategy drives cloud strategy

Thinking about your own organization, what data is most popular? Which assets are most trusted? Which are deprecated?

Data intelligence platforms like Alation surface usage metrics, data lineage, and business context so you can prioritize which data and applications to migrate first. This ensures that you move your most valuable assets and avoid wasting resources on redundant or obsolete data.

Alation also provides deeper insights into cloud migration effects by offering visibility into how downstream processes consume data. Through interactive data lineage, anyone can explore how data moves and impacts processes and analytical applications. This transparency for data lineage reduces risk and ensures that the migration can be completed successfully.

Governance and data quality planning set the foundation

“Data quality is directly linked to the quality of decision making,” says Melody Chien, Senior Director Analyst, Gartner. Therefore, organizations must ensure that data is consistent, trustworthy, and doesn’t get misused. Effective data governance is necessary to uphold high data quality throughout its lifecycle—enabling businesses to meet regulatory requirements and drive effective decision-making. This is especially true since on-premises policies and practices might not directly translate to the target cloud environment. 

Alation data governance capabilities enforce policies consistently across both legacy silos and cloud data platforms. Additionally, its data stewardship, health, and profiling features surface and address quality issues before they become problematic in the migrated environment. 

As a result, you ensure compliance with regulatory requirements and boost the reliability of your cloud analytics. However, make sure your migration strategy incorporates on-premises security policies, and implement solutions that deliver comparable security and compliance in the cloud.

Cloud cost management demands close attention

The cost of cloud data migration depends on several factors, including data volumes and workload complexity. It’s also important to consider post-migration costs, such as employee training and potential fees for moving data. 

Real-time insights from data intelligence platforms such as Alation can help optimize workloads by identifying and prioritizing the most critical data for migration—and then rationalizing and streamlining assets once the migration is complete.

Successful migrations maintain continuous service

Consider the cost of business downtime upfront. For instance, Alation's real-time data lineage and impact analysis can help you identify potential disruptions through dependency analysis and support planning. You can then plan ways to minimize disruption or significant outages to reduce overall cloud migration costs. 

One approach is to break the migration down into smaller, manageable phases rather than a single large move. If possible, plan to complete the various phases during periods of low business activity, such as weekends or holidays. This can reduce risk and allow for testing and adjustments at each stage. 

Additionally, ensure that all critical data is backed up before starting the migration. Remember to regularly test these backups to ensure they can be restored quickly in case of an issue. Above all, keep all stakeholders informed about the migration plan, potential risks, and expected downtime. Clear communication helps to manage expectations and prepare teams for any disruptions.

User adoption signals ROI

The success and perceived value of a migration hinge largely on user adoption. Not everyone will understand new cloud technology immediately, so transparency and proper training are necessary to help avoid roadblocks. Beyond this, prioritize using an intuitive, user-friendly data intelligence platform to make it as easy as possible for people to find, understand, and trust new datasets. 

Cloud data migration success stories

Across industries, organizations that take a structured, governance-first approach to cloud migration consistently see faster outcomes, reduced risk, and stronger business value. These standout examples illustrate how thoughtful planning—combined with the right tools—can turn migration into measurable impact.

Spark NZ

Spark NZ, New Zealand’s largest digital services provider, faced siloed and scattered data that slowed reporting and limited innovation. Despite having a highly skilled team, analysts and engineers spent significant time searching for data rather than leveraging it for decision-making.

By cataloging and prioritizing data with Alation before migrating to Snowflake, Spark avoided moving obsolete assets and improved governance. As a result, the company accelerated its cloud migration, cut its Power BI dashboard delivery times by 30%, and supported rapid machine learning development. These changes helped Spark drive faster, more data-driven decisions and contributed to a 9% increase in mobile revenue.

Texas Mutual Insurance Company

Texas Mutual Insurance Company struggled with disconnected data warehouses and limited governance, which led to instinct-driven decisions and reporting delays. 

By adopting Alation Cloud Service to govern and document datasets ahead of migration, Texas Mutual established a trusted data foundation. As a result, they:

• Got essential business dashboards 80% quicker, supporting data-driven decision-making

• Reduced key processes from a week to 30 minutes,  significantly speeding up upgrades

• Allowed team members to focus their priorities—spending more time on adoption and user training than upkeep and maintenance 

Both companies' experiences demonstrate how a well-done data migration can contribute to faster decision-making, improved data trust, and enhanced business agility. Through their partnership with Alation, both companies are now well-positioned to thrive in the data-driven digital era.

What platforms are teams migrating from?

As organizations modernize their data infrastructure, three legacy platforms stand out for experiencing significant declines in usage: Teradata, Cloudera (Hadoop), and IBM DB2/Netezza. 

1. Teradata

Teradata has long been a leader in enterprise data warehousing, but high licensing fees, rigid hardware requirements, and limited elasticity for new workloads are driving many organizations to cloud-native alternatives. Industry reports and customer reviews frequently cite cost and lack of flexibility as top migration drivers. Cloud data solutions offer a more cost-effective, pay-as-you-go model that scales with the business.

2. Cloudera (Hadoop)

Cloudera, known for its Hadoop-based data management solutions, was an early leader in the big data analytics market. However, the complexity and cost of managing Hadoop clusters on-premises are common pain points among its users. As a result, more CIOs are considering cloud-based alternatives and modern data stack architectures that support a wider range of data workloads while reducing administrative overhead.

3. IBM DB2

Over the last few years, IBM DB2’s on-premises architecture has become a barrier for teams seeking more agile, cloud-native capabilities. The legacy system’s reliance on specialized skills, slower innovation cycles, and limitations around real-time analytics have led many organizations to transition to more modern options. In particular, they’re switching to alternatives that support advanced analytics, automation, and self-service access at scale.

Across industries, the common thread is clear: teams want flexible, cost-effective, and scalable cloud solutions that legacy platforms can no longer deliver.

Choosing a service provider for cloud data migration: Snowflake, Databricks, and AWS

Selecting the right cloud provider shapes your data migration’s success, cost, and future flexibility. Here’s a practical comparison of three leading options:

1. Snowflake

Pros:

• Separates compute and storage to allow for independent scaling of performance and cost management.

• Provides a fully managed service that comes with automated tuning and maintenance, reducing operational overhead.

• Offer strong multi-cloud support (AWS, Azure, GCP) that’s complete with seamless cross-cloud data sharing.

• Delivers a consistent experience across regions and clouds that has robust integrations with BI and governance tools.

Limitations:

• Proprietary SQL extensions can make switching providers or integrating with certain tools more complex.

• Data egress fees and regional feature rollouts may impact long-term costs and flexibility.

2. Databricks

Pros:

• Features a unified lakehouse architecture that combines data lake scalability with warehouse performance.

• Offers best-in-class for advanced analytics, AI, and machine learning, as well as built-in tools like MLflow and Delta Lake.

• Supports open data formats and supports real-time, unstructured, and large-scale workloads.

• Enables deep integration with cloud-native services and allows for flexible deployment across AWS, Azure, and GCP.

Limitations:

• Spark expertise is required if you want to maximize cost efficiency.

• Misconfigured clusters have the potential to inflate costs.

• The platform’s depth may overwhelm smaller teams without dedicated data engineers.

3. AWS

Pros:

• Possesses a large portfolio of cloud services (over 200), including migration tools like AWS DMS, Glue, and Redshift.

• Offers global reach and strong Service Level Agreements (SLAs), which are ideal for those seeking better reliability and compliance.

• Boasts a deep ecosystem that encompasses extensive partner and third-party integrations.

Limitations:

• Pricing models are complex, especially when you combine storage, compute, and data transfer needs.

• There’s vendor lock-in risk if users have already heavily invested in native AWS services.

Each provider has unique strengths—and potential tradeoffs—depending on your team’s skill sets, use cases, and priorities. Evaluating these options through the lens of your long-term data strategy can help ensure your migration lays the groundwork for both immediate wins and sustained innovation.

Are you planning your cloud data migration?

Cloud data migration promises agility, innovation, and scale—but only when it’s done right. Too often, teams struggle with low visibility into their data landscape, difficulty prioritizing what to migrate, and a lack of governance that leads to high costs and disrupted business operations. 

Alation helps data leaders overcome these challenges with a platform that’s built for clarity and control. Our platform provides a unified view of data across cloud and on-prem environments, plus built-in governance, impact analysis, and automated lineage that simplify decisions and reduce risk.

If you're planning a migration, Alation can help you do it faster, smarter, and with fewer surprises. Explore our cloud data migration solution for practical tools and expert guidance—or dive into our expert-led webinar to see how leading teams are using Alation to power their cloud strategies.