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Pitfalls of Cloud Data Migration

  • ETL Testing
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” 36% of data migration projects kept to the forecasted budget, and only 46% were delivered on time “
-Forbes

Cloud data migrations often fail on budget and timelines, with the majority of projects missing forecasted costs or delivery deadlines. This post outlines four common hurdles: dirty data (duplicates, inconsistent formats), choosing the wrong cloud storage option, mapping legacy data to new cloud application structures, and security/compliance gaps. It emphasizes that pre-migration validation, accurate data mapping, and thorough QA testing are essential to avoid data loss, corruption, and unexpected downtime during migration.

Key Takeaways

  • Dirty data must be fixed before migration, not after — duplicates, referential integrity issues, and mismatched data formats are far more expensive and difficult to resolve once data has already moved to the new cloud structure.
  • Storage selection directly impacts application performance — choosing the wrong cloud storage class (across providers like AWS, Azure, or Google Cloud) can hinder how effectively applications run post-migration.
  • Legacy-to-cloud data mapping requires careful documentation — differences in field counts, naming conventions, and new data types (e.g., social media, blobs, video) can create confusion if not planned and documented in advance.
  • QA and validation reduce major migration risks — data migrations commonly face missing/lost data, data corruption, and unexpected downtime, making pre-planned testing essential to confirm data was transferred completely and accurately.

This statistic shows a grim picture of wasted effort. But to replace CapEx with OpEx cloud data migration and cloud migration, in general, is a popular solution. Enterprises are looking for ways to scale data storage, due to AI and ML and given the volume of data being generated and collected.

Migrating terabytes or even petabytes of data from one location to another is a daunting task. But to understand this further, you need to look beyond the number of bytes. As pointed out, data migration is not without its risk. Being aware of the common hurdles like – data security, privacy, compliance, availability and performance – that could potentially derail your project will increase the likelihood of achieving a successful Cloud Data Migration.

Let’s check what common hurdles that lead to this dismal success rate are.

Common Hurdles of Cloud Data Migration
bad-data
Dealing With Dirty Data

Accept it! From the onset, almost every organization’s data has a variety of issues. The main culprits are duplicates and inconsistent/incomplete data. Things start to “get dirty” when you have more than 5% duplicates, numerous inconsistencies such as referential integrity, truncated data, and mismatched data formats. When moving data to the cloud data structures, traditional data types often do not match the new destination formats. To reduce the overall cost of the migration, data needs to be validated before movement. For your records to be accurately mapped, you need to address data inconsistencies and incompletions before migration, as it is far more expensive and challenging to fix in the new data structures.

Failure To Choose The Proper Storage

Selecting the wrong data storage option would hinder the effective operation of applications. The team must vet the cloud storage options suitable for its operations when an enterprise migrates its data and applications to the cloud. When deciding on a provider, enterprises are looking into simplifying data management, supporting new accelerated insights, or lowering costs. Enterprises can select some data class options from AWS, Azure, and Google Cloud, providing Infrastructure-as-a-service (IaaS) cloud storage.

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Cloud-Applications

Mapping Old Data with New Cloud Applications

Modern data structures offer multiple workload support such as Data Warehousing and AI/ML all supported by a single Cloud offering such as DataBricks or Snowflake. This is an entirely new way of thinking about where workloads need to be executed and on which data platform. To take advantage of these modern data platforms, companies need to know how to move to these architectures and what testing is required to ensure the process runs effectively. As an example, new data elements are available in these modern data stacks that take into account things like social media, blobs, video and other data types.

There are various possibilities of change of business workflows like you may have to add two fields two in the new database, and vice versa whereas you may have had one field in your legacy database, or you may change the field names which may create a huge confusion in the whole migration process. So, before it gets more complex, the best way out is to choose a destination for your data in your new database and transfer it from where it currently lives in your legacy system and keep it documented.

Replace completely with the differences between cloud and premise databases

Security and Compliance Adjustments
61% of companies listed security as a primary concern for not moving to the cloud.

Selecting the wrong data storage option would hinder the effective operation of applications. The team must vet the cloud storage options suitable for its operations when an enterprise migrates its data and applications to the cloud. When deciding on a provider, enterprises are looking into simplifying data management, supporting new accelerated insights, or lowering costs. Enterprises can select some data class options from AWS, Azure, and Google Cloud, providing Infrastructure-as-a-service (IaaS) cloud storage.

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Why is Quality Assurance playing a crucial role in Cloud Data Migration?

How do you verify whether all data is moved/loaded and matches all the rules or data accuracy? This has to be preplanned and implemented before data movement to the cloud. Think about what can go wrong and the cost of recovery when issues arise after moving the data.

Whether you are migrating your data from legacy systems to a new system, cloud, or from one vendor’s software to another’s, it has always been one of the most challenging initiatives for IT managers. Data Accuracy is a key aspect that should be validated through planned testing when loading data from one source to a target system.

Here are a few scary metrics of Data Migration like
Migrations have missing or lost data
30%
Have some form of data corruption
40%
Migration projects have unexpected outage/downtime and no one denies the typical cost of the downtime
64%

So, how to mitigate these?

  • Check whether all the required data was transferred according to the requirements.
  • Make sure destination tables are populated with accurate values.
  • Validate that the absence of data loss unless it is based on requirements.
  • Authenticate the performance of custom scripts.

And these are also the objectives of Data Migration Testing. Migrating data is the complex work of a QA team and it requires skill, expertise, tools, and resources. As it is not a simple transfer of information from one storage to another. You need to implement a thorough validation and testing strategy to reduce risk and ensure that the data has been migrated and transformed. The faster a QA team starts analyzing, the faster the issues can be revealed and removed.

Plan A Seamless Cloud Data Migration With Customized Testing Approach

Undoubtedly, with various challenges, both technical, economical, and personnel-related, the process of cloud migration is often fraught. An enterprise must engage with Datagaps to overcome these hurdles. At Datagap, we leverage the experience of having tested large-scale data warehousing and business intelligence applications, to help you perform comprehensive testing to check if your data remain functional, stable, scalable, and compatible in the target cloud environment.

Our differentiators are our products – Datagaps ETL Validator and DataOps Dataflow. Datagaps ETL Validator generates hundreds of test cases automatically using Data Migration wizards. On the other side using Apache spark as the engine, Datagaps Dataflow (The best testing tool for Cloud Big Data Testing) can compare billions of records. Datagaps ensures Data Accuracy and Reliability by strengthening your Cloud Data Migration Testing Strategy.

To achieve 100% data validation for data migration projects, talk to our experts

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FAQs: Cloud Data Migration Testing

1) What is “dirty data” and why is it a problem during cloud migration?

Dirty data includes issues such as duplicate records, referential integrity violations, incomplete values, and inconsistent data formats. If these problems are not resolved before migration, they become more difficult and expensive to correct after the data has been moved to the cloud.

2) Why does choosing the right cloud storage option matter for migration success?

Cloud providers offer multiple storage options designed for different workloads. Selecting an inappropriate storage class can negatively affect application performance, scalability, and overall system efficiency after the migration is complete.

3) What makes mapping legacy data to cloud applications challenging?

Legacy systems and modern cloud applications often differ in field structures, naming conventions, and supported data types. Careful data mapping and documentation help ensure that information is migrated accurately and consistently.

4) What are the biggest risks if a cloud migration isn’t properly tested?

Without comprehensive validation, organizations may experience missing or lost data, data corruption, application failures, and unexpected downtime. Thorough pre- and post-migration testing helps verify that data has been transferred completely, accurately, and without impacting business operations.

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Established in the year 2010 with the mission of building trust in enterprise data & reports. Datagaps provides software for ETL Data Automation, Data Synchronization, Data Quality, Data Transformation, Test Data Generation, & BI Test Automation. An innovative company focused on providing the highest customer satisfaction. We are passionate about data-driven test automation. Our flagship solutions, ETL Validator, Data Flow, and BI Validator are designed to help customers automate the testing of ETL, BI, Database, Data Lake, Flat File, & XML Data Sources. Our tools support Snowflake, Tableau, Amazon Redshift, Oracle Analytics, Salesforce, Microsoft Power BI, Azure Synapse, SAP BusinessObjects, IBM Cognos, etc., data warehousing projects, and BI platforms.  www.datagaps.com 

Queries: contact@datagaps.com

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