Moving data from one system to another can be surprisingly complicated. Whether you're replacing a legacy database, consolidating business applications after an acquisition, or moving your infrastructure to the cloud, the challenge is preserving the information your teams depend on while keeping business operations still running.
The best data migration tools help automate data extraction, transformation, transfer, and validation. However, different solutions address very different requirements. Some specialize in database replication, others build cloud data pipelines, while platforms like Getint support migrations between work management, ITSM, and DevOps applications.
In this guide, we'll compare 12 data migration tools, explain their key features and limitations, and explore how to choose the right solution for your migration project. We'll also cover common migration challenges, data security considerations, and best practices for transferring data accurately.
What Are Data Migration Tools?
Data migration tools are software solutions that help organizations transfer information from one database, application, storage environment, or platform to another. They automate different stages of the data migration process, including extracting source data, transforming it into compatible formats, loading it into target systems, and validating the results.
Depending on their purpose, migration tools may provide predefined connectors, automated data conversion, schema mapping, monitoring, and error handling. Some are designed to move large data volumes between databases and cloud platforms, while others focus on preserving application-specific information such as tasks, comments, attachments, and relationships.
It's also important to know the differences between data migration vs data integration.
- Data migration generally involves moving existing information into a new environment, often during a system replacement or modernization project.
- Data integration enables information to flow between connected systems on an ongoing basis. Some platforms support both, which can be particularly useful when organizations need to migrate historical records while maintaining synchronization during a gradual transition.
Why Do Companies Need Data Migration?
Organizations undertake data migration projects for several reasons, from replacing outdated infrastructure to bringing teams and information together after business changes.
Modernizing legacy systems. Older applications and databases can become difficult to maintain, integrate, or scale. Migration allows businesses to adopt newer platforms while retaining important historical information.
Moving to cloud platforms. Cloud migration can improve scalability, flexibility, and access to information. Businesses may transfer existing data from local infrastructure to cloud services or between different cloud environments.
Supporting mergers and acquisitions. When organizations merge, they often need to consolidate customer data, projects, IT service management records, and internal applications. Data migration helps bring information into shared systems and simplify collaboration.
Improving performance and governance. Migrating to modern data platforms can improve system responsiveness and create opportunities to strengthen data quality, access controls, and compliance practices.
Whatever the reason, successful data migration requires careful planning. Poor data quality, incompatible structures, and incomplete transfers can affect how reliably information works in the new environment.
Best Data Migration Tools at a Glance
The right data migration tool depends primarily on the systems you're connecting and the type of information you're transferring. A solution designed to move SQL Server databases to AWS may not be suitable for migrating Jira work items to Azure DevOps, even though both projects involve data movement.
Here's a quick comparison of the top data migration tools for different business requirements.
Note that these tools are grouped according to their strongest use cases rather than ranked by a universal performance score. The selection includes different categories of data migration software so organizations can identify solutions relevant to their infrastructure and workflows.
12 Best Data Migration Tools Compared
The tools below cover everything from transferring data between SaaS applications to migrating large databases and modernizing cloud infrastructure. Each review considers the platform's functionality, suitable use cases, and potential limitations.
1. Getint – Best for Work Management, ITSM, and DevOps Data Migration
Best for: Organizations migrating between Jira, Azure DevOps, ServiceNow, Asana, monday.com, and other supported business applications.
Getint is a data integration and migration platform designed to connect work management, ITSM, DevOps, and business systems. It supports organizations moving away from legacy systems, consolidating multiple application environments, or standardizing processes following mergers and acquisitions.
Application migrations present challenges that traditional database migration tools don't always address. For example, moving from Azure DevOps to Jira requires more than transferring records. Teams may need to map issue types to work item types, translate workflow statuses, preserve supported relationships, and transfer comments, attachments, and custom field values.
Getint provides configurable mappings and migration controls that help teams manage these differences. Users can define how source data corresponds to information in target systems and choose which records should be transferred.
Let's see how Veryon migrated all project data from Azure DevOps to Jira using Getint.
A particularly useful capability is combining historical data migration with continuous data synchronization. Instead of requiring every team to switch platforms simultaneously, organizations can migrate existing work items and maintain synchronization between supported systems during the transition.
This approach is relevant when different departments move at different speeds or when an acquired company continues using its existing platform until the migration is completed.
Key features:
- Configurable data mapping: Map work item types, fields, workflow statuses, and supported relationships between applications.
- Selective migration: Choose records using item IDs, date ranges, and JQL filters where supported.
- Historical data transfer: Migrate existing tasks, issues, and other supported records rather than synchronizing only newly created items.
- Migration controls: Configure how previously migrated items are handled during subsequent runs.
- Monitoring and logs: Review migration progress, synchronization activity, and errors.
- Continuous synchronization: Keep connected applications synchronized when a gradual transition is necessary.
- Deployment flexibility: Choose between Getint SaaS and customer-managed On-Premise deployment.
In our documentation we recommend auditing existing workflows, testing mappings, running pilot migrations, and validating the results before a full transfer. The migration licensing includes a three-month synchronization license, giving teams time to prepare and test their configuration.
Limitations to consider
Getint specializes in supported business applications rather than relational database migration or cloud data warehouse processing. It wouldn't be the appropriate choice for transferring large SQL databases into Amazon Redshift or building an enterprise analytics pipeline.
Migration capabilities also depend on the source and target applications, so teams should confirm which data types, permissions, relationships, and metadata can be transferred for their particular connection.
When to choose Getint: When migrating between supported business applications requires granular mapping, controlled data transfer, and the flexibility to keep both environments operational during a transition.
Learn more in the Getint migration documentation.
2. AWS Database Migration Service – Best for AWS Database Migrations
Best for: Organizations migrating databases to AWS or between supported database engines.
AWS Database Migration Service (AWS DMS) helps organizations migrate relational databases and other supported data stores into AWS environments. It is particularly relevant for businesses modernizing existing infrastructure, consolidating database systems, or replacing legacy database platforms.
One of its main advantages is support for Change Data Capture (CDC), which enables ongoing replication of changes made to the source database. This allows the source system to remain operational while the target database is being prepared, potentially reducing downtime during the final transition.
AWS DMS also supports database migrations involving different engines. For example, a company moving from SQL Server to PostgreSQL may need to convert the existing database schema before transferring data.
For supported migration paths, AWS DMS Schema Conversion can help translate database schemas and code objects into formats compatible with the target environment. Some objects still require manual review or conversion.
Key features:
- Full data loading and ongoing replication using CDC.
- Database migration between supported source and target engines.
- Schema conversion and migration assessment capabilities.
- Integration with AWS cloud infrastructure and services.
- Monitoring of migration tasks and replication activity.
Limitations to consider
AWS DMS requires knowledge of database architecture, networking, source permissions, and cloud infrastructure. Teams should also distinguish between transferring database records and converting the schema, since these are separate activities.
When to choose AWS DMS: When a migration involves supported databases and AWS is the primary destination, especially where minimizing database downtime is important.
3. Fivetran – Best for Automated Cloud Data Pipelines
Best for: Data teams transferring information from multiple applications and databases into cloud data warehouses.
Fivetran is a managed data movement platform focused on automating data extraction and loading. It connects supported SaaS applications, databases, and other data sources to destinations used for reporting, analytics, and business intelligence.
Rather than requiring engineers to build and maintain individual pipelines, Fivetran provides managed connectors that handle much of the ongoing synchronization process.
This is particularly useful for organizations consolidating customer data, financial records, and operational information from multiple systems into modern data platforms.
Fivetran also supports incremental data movement for compatible sources, reducing the need to repeatedly transfer entire datasets. Schema management capabilities help accommodate changes in source structures, although destination models and transformation requirements still need planning.
Key features:
- Managed connectors for SaaS applications and databases.
- Automated data extraction and loading.
- Incremental synchronization and supported CDC workflows.
- Schema management and pipeline monitoring.
- Integration with transformation workflows and cloud analytics platforms.
Fivetran's pricing is associated with consumption metrics that include monthly active rows for relevant products and plans. Organizations handling large data volumes should model expected costs before selecting the platform.
Limitations to consider
Fivetran is primarily designed for analytics-oriented data movement rather than preserving operational workflows between business applications. Costs can also vary with data activity and the selected services.
When to choose Fivetran: When the goal is to maintain reliable, automated data pipelines into a cloud warehouse with relatively limited infrastructure management.
4. Airbyte – Best for Customizable and Open-Source Data Movement
Best for: Technical teams seeking flexible connectors and greater control over their data integration infrastructure.
Airbyte offers data movement capabilities through an open-source ecosystem alongside managed deployment options. It is particularly attractive to organizations that want more control over connectors, data sources, and pipeline configuration.
Teams can use existing connectors or develop custom integrations for applications that are not covered by standard integrations.
This flexibility can be useful when an organization has proprietary systems, unusual data formats, or requirements that managed migration platforms cannot address directly.
Airbyte can support incremental loading where the source connector allows it, helping reduce unnecessary data transfers. Depending on the deployment and architecture, teams may also manage infrastructure, authentication, orchestration, and monitoring.
Key features:
- Open-source connector framework.
- Custom connector development.
- Data extraction from supported applications and databases.
- Incremental synchronization for compatible sources.
- Self-managed and managed deployment options.
Limitations to consider
Open-source software can reduce licensing costs, but it doesn't eliminate the engineering effort required for configuration, upgrades, connector maintenance, or troubleshooting.
The reliability and feature coverage of individual connectors may differ, particularly for community-developed integrations.
When to choose Airbyte: When customization, connector flexibility, or control over the data movement environment is more important than a completely managed setup.
5. Hevo Data – Best for No-Code Data Migration
Best for: Teams that want to transfer data into cloud data warehouses without building complex pipelines.
Hevo Data is a managed data pipeline platform focused on simplifying extraction, loading, and transformation for analytics workflows.
It connects supported databases, business applications, and other sources to cloud destinations. Users can configure data pipelines through a graphical interface, reducing the need for extensive programming.
Hevo is useful for companies building centralized reporting environments. For example, a business collecting data from marketing platforms, CRM systems, and transactional databases may want to combine this information in one warehouse to improve reporting consistency.
The platform provides monitoring, schema handling, and transformation capabilities, although the available functionality depends on the source, destination, and pipeline configuration.
Key features:
- No-code pipeline configuration.
- Connectors for supported databases and SaaS applications.
- Data transformation and schema management.
- Incremental loading for compatible sources.
- Pipeline monitoring and error notifications.
Limitations to consider
Hevo is mainly designed for moving data into analytics destinations. Organizations requiring application-specific metadata mapping, workflow preservation, or database infrastructure replication should assess whether it supports their exact scenario.
When to choose Hevo Data: When a business needs relatively straightforward data ingestion and ongoing synchronization into a supported cloud warehouse.
6. Matillion – Best for Cloud Data Transformation
Best for: Data engineering teams handling complex transformations within cloud data environments.
Matillion provides data integration and transformation capabilities for organizations working with modern cloud data platforms. It's relevant when data must be cleaned, standardized, enriched, or restructured before becoming useful in the destination environment.
For example, a business consolidating customer information from several applications may encounter inconsistent formats, duplicate records, and different naming conventions. Matillion helps teams build workflows that prepare this information for analytics and reporting.
Its visual pipeline-building capabilities can reduce repetitive coding, while technical teams can still apply more advanced transformation logic where supported.
Key features:
- Visual pipeline and transformation design.
- Integration with major cloud data platforms.
- Data transformation and orchestration capabilities.
- Automated execution of data processing workflows.
- Support for complex transformation requirements.
Limitations to consider
Matillion is better suited to analytics and cloud data engineering than migrating business application configurations or preserving operational relationships between work management systems. Although its visual interface simplifies many tasks, complex transformations can still require SQL knowledge and data engineering expertise.
When to choose Matillion: When migrating data involves substantial preparation and transformation within a cloud-based analytics environment.
7. Azure Data Factory – Best for Microsoft Data Ecosystems
Best for: Organizations moving data across Azure services, on-premises databases, and supported external systems.
Azure Data Factory is Microsoft's cloud-based data integration and orchestration service. It provides capabilities for extracting, transferring, and transforming information across different data environments.
Its Copy Activity allows users to move data between supported sources and destinations, while mapping capabilities help align source schemas and target structures. Azure Data Factory also provides orchestration features for scheduling and coordinating multi-step data pipelines. Teams can build workflows that combine data extraction, transformation, loading, and verification.
For organizations already using Microsoft Azure, the platform can integrate naturally with existing cloud infrastructure and governance practices.
Key features:
- Data movement between supported cloud and on-premises systems.
- Copy Activity for transferring data.
- Data mapping and transformation workflows.
- Pipeline scheduling and orchestration.
- Integration with Azure monitoring and management tools.
Limitations to consider
Configuring secure network connectivity, integration runtimes, pipeline dependencies, and performance settings may require technical expertise.
Azure Data Factory also focuses on data pipelines rather than application-level migration of project workflows and user collaboration context.
When to choose Azure Data Factory: When a company's migration and data integration requirements are closely connected to the Microsoft Azure ecosystem.
8. Qlik Talend Cloud – Best for Data Quality and Governance
Best for: Enterprises where data cleansing, consistency, and governance are major migration requirements.
Qlik Talend Cloud combines data integration with capabilities supporting data quality, transformation, and enterprise data management.
This becomes particularly important during migrations involving many business units or legacy systems. Different departments may store similar information using inconsistent structures, duplicate identifiers, or conflicting formats.
Moving these records without first addressing poor data quality can introduce the same problems into the new platform. Qlik Talend's capabilities can help organizations build data integration processes while applying validation and quality controls. Its broader data management functionality is relevant when companies need repeatable processes rather than a single database transfer.
Key features:
- Data integration and transformation capabilities.
- Data profiling and quality management.
- Support for complex enterprise data workflows.
- Governance and monitoring capabilities.
- Connectivity across supported enterprise systems.
Limitations to consider
Enterprise data management platforms can involve substantial implementation work. Organizations should assess which capabilities they actually need and avoid introducing unnecessary complexity for relatively small migration projects.
When to choose Qlik Talend Cloud: When a migration involves inconsistent data sources and requires a strong emphasis on data quality and governance.
9. SnapLogic – Best for Enterprise Integration Automation
Best for: Organizations building complex integration workflows across applications, databases, and cloud platforms.
SnapLogic is an integration and automation platform that helps organizations connect systems and create workflows for moving and transforming information. It's suitable for businesses managing diverse enterprise technology environments, where data must flow between SaaS applications, databases, APIs, and cloud services.
SnapLogic uses a visual approach to pipeline development and provides reusable integration components. Its automation capabilities can help reduce repetitive configuration tasks during complex migrations.
For instance, a company modernizing its operations may need to extract information from older applications, transform incompatible data formats, and load the results into several target systems.
SnapLogic can coordinate these processes when the necessary connectors and integration capabilities are available.
Key features:
- Visual integration pipeline development.
- Prebuilt connectors and reusable components.
- Data transformation and workflow orchestration.
- API and application integration capabilities.
- Monitoring and automation features.
Limitations to consider
Complex enterprise integrations still require careful architecture, mapping, and testing. SnapLogic's broader platform capabilities may also be more extensive than necessary for a straightforward, one-time migration.
When to choose SnapLogic: When migrating data is part of a broader integration and process automation initiative.
10. Informatica – Best for Large-Scale Enterprise Data Migration
Best for: Organizations dealing with complex migrations, extensive data governance requirements, and multiple enterprise systems.
Informatica provides data management and integration capabilities used across large enterprise environments. Its platform supports scenarios involving data integration, data quality, metadata management, and governance. These capabilities are relevant when companies consolidate large volumes of information from many systems.
For example, a global organization replacing several legacy data platforms may need to identify duplicated customer records, align information from different regions, apply transformation rules, and verify that the resulting datasets meet internal standards.
Informatica can support these requirements as part of a broader data management architecture. Its functionality is particularly relevant where compliance, auditability, and repeatable processes are essential.
Key features:
- Enterprise data integration and transformation.
- Data quality and profiling capabilities.
- Metadata management and governance tools.
- Support for complex data environments.
- Monitoring and orchestration capabilities.
Limitations to consider
The platform's breadth can introduce significant implementation and administration requirements. Costs and licensing arrangements should be evaluated against the actual scope of the migration.
When to choose Informatica: When migration is part of a large enterprise modernization project requiring extensive governance and data management capabilities.
11. Apache NiFi – Best for Custom Data Flows
Best for: Technical teams that need configurable, self-managed data routing and processing.
Apache NiFi is an open-source data flow automation platform. It allows teams to build workflows that move and process information between supported systems using a graphical interface.
Unlike managed cloud migration tools, NiFi gives organizations more control over how their processing infrastructure is deployed and operated. This can be valuable when data must remain within a company's local network or when integration workflows involve specialized protocols, file formats, and transformation rules.
NiFi supports configurable processors, routing conditions, and operational monitoring, making it useful for technical teams building customized data movement pipelines.
Key features:
- Open-source data flow automation.
- Visual configuration of processing workflows.
- Data routing and transformation.
- Self-managed deployment options.
- Operational monitoring and data provenance capabilities.
Limitations to consider
Apache NiFi requires infrastructure administration and technical expertise. Teams are responsible for designing workflows, maintaining the environment, and ensuring that data movement processes are reliable and secure.
It also doesn't provide a universal, ready-made migration process for every application or database.
When to choose Apache NiFi: When customization, local infrastructure control, and specialized data processing are primary requirements.
12. Oracle GoldenGate – Best for Database Replication and Minimal Downtime
Best for: Enterprise database migrations that require continuous replication and carefully managed cutovers.
Oracle GoldenGate is a data replication and integration technology used to capture and transfer database changes between supported systems. Its core strength is keeping data updated across database environments, which can be particularly useful during migrations involving business-critical applications.
Instead of taking the source database offline for the entire migration, organizations can transfer existing data and replicate subsequent changes while preparing the destination. This approach helps reduce the amount of disruption during the final cutover, although it still requires careful planning, consistency checks, and coordination.
Oracle GoldenGate can support heterogeneous replication scenarios, making it relevant to certain database modernization projects as well as migrations between compatible Oracle environments.
Key features:
- Change Data Capture and database replication.
- Support for selected heterogeneous database environments.
- Continuous data synchronization.
- Monitoring of replication processes.
- Capabilities relevant to low-downtime database transitions.
Limitations to consider
Database replication projects can be technically demanding, especially where source and destination schemas, transaction handling, or database versions differ.
Organizations should verify supported configurations and licensing requirements before choosing Oracle GoldenGate.
When to choose Oracle GoldenGate: When ongoing database replication is central to the migration strategy and downtime must be carefully controlled.
Types of Data Migration Tools
Data migration software can be classified by its primary purpose or deployment model. These categories often overlap, but understanding them makes it easier to narrow down the available options.
Cloud-Based vs. On-Premise Data Migration
- Cloud migration tools typically rely on hosted services that provide connectors, processing capabilities, and infrastructure for transferring data. They are often suitable for companies adopting cloud infrastructure or consolidating data in cloud warehouses.
- On-Premise data migration tools operate on infrastructure controlled by the organization. This approach can be preferable when internal policies require greater control over hosting, access, or network connectivity.
The important distinction is that deployment does not determine what a tool can migrate. A cloud-based integration platform and an On-Premise deployment may support similar data sources but have different operational and security responsibilities.
Some data migration platforms support both deployment models. Getint, for example, offers migration capabilities through both Getint SaaS and Getint On-Premise. The SaaS version is hosted by Getint, while the On-Premise version runs on the organization's own servers and supports migration runs using the same core platform. This allows teams to choose a deployment option that fits their infrastructure and security requirements while retaining access to Getint's migration functionality.
Open-Source and Self-Scripted Migration
- Open-source data migration tools provide flexibility for organizations with specific technical requirements. Developers can configure workflows, extend supported integrations, and adapt processing logic.
- Self-scripted migration relies on custom code, database utilities, APIs, and internal automation.
Both approaches can reduce dependency on commercial software, but they introduce maintenance responsibilities. Teams need to manage retries, permissions, performance, monitoring, and recovery procedures themselves.
Database vs. Application Migration
- Database migration tools primarily focus on records, tables, schemas, and replication between data storage systems.
- Application migration tools must account for business-specific structures.
For example, migrating a project management system may involve tasks, comments, attachments, custom fields, workflow statuses, and relationships between records. These elements require different transformation capabilities from those used when transferring raw data between database tables.
This is why selecting a tool based on the type of data being moved is one of the most important decisions in the entire migration process.
How to Choose the Right Data Migration Tool
The best data migration software for one organization may be unsuitable for another. Before comparing features or pricing, define what the migration needs to accomplish and how the destination environment should work.
1. Check Source and Target System Compatibility
Start with the applications, databases, or cloud services involved.
Does the platform provide native connectors for both systems? Can it access the required records and metadata? Does the target application support the same information structure as the source?
Compatibility becomes especially important during complex migrations involving multiple data sources. If a tool requires extensive custom development to connect the systems, implementation costs may outweigh the benefits of its other features.
2. Evaluate Data Mapping and Transformation
Source and target systems frequently store information differently. A database may use a different schema, while two project management tools may have different field types, workflow statuses, or relationship models.
Look for schema mapping, field transformation, data cleansing, and automated data conversion where appropriate.
For application migrations, granular mapping of permissions and metadata is also important. Not every tool can preserve every system-specific attribute, so the migration plan should distinguish supported data from information requiring additional handling.
The right transformation capabilities reduce manual adjustments and help preserve meaningful business context.
3. Consider Data Volume and Performance
Data volume directly affects migration planning.
Moving a few thousand application records is different from processing billions of database rows. Large data volumes can create performance bottlenecks, increase transfer costs, and place additional load on source systems.
Evaluate whether the tool supports parallel processing, incremental data loading, scheduling, and suitable performance monitoring. For database migrations, CDC can help minimize repeated transfers by replicating changes rather than constantly reloading the complete dataset.
Performance expectations should be tested using representative source data before committing to the full migration.
4. Review Security and Compliance
Migration often involves sensitive data, including customer records, employee information, and confidential business documents.
Verify what security controls the platform provides and how they apply to your selected deployment.
Important considerations include:
- Data encryption in transit and at rest.
- Appropriate access controls and credential management.
- Data masking when sensitive information should be hidden or transformed.
- Audit trails that record migration activity and relevant changes.
- Data retention policies and compliance with applicable regulations.
End-to-end encryption and detailed audit records can be particularly important in regulated environments, but their availability and scope must be verified for each solution.
Data security also depends on how the migration is configured, not only the software provider's certifications.
5. Examine Monitoring, Validation, and Error Handling
A migration should provide enough visibility to identify problems before they affect business operations.
- Useful capabilities include progress dashboards, execution logs, alerts, retries, and reconciliation tools.
Data validation should confirm more than whether the transfer completed. Teams need to verify data accuracy, completeness, and the integrity of important relationships. For example, record counts may match while individual field values, attachments, or references remain incorrect.
When evaluating a data migration platform, determine which checks are automated and which require additional scripts or manual review.
6. Plan for Downtime and Continuous Synchronization
Some migration projects can be completed during a scheduled maintenance window. Others involve systems that must remain available throughout the transition.
For database migration, incremental replication and Change Data Capture may help keep the destination updated while the source remains operational.
For application migrations, continuous synchronization can support coexistence between old and new systems as teams move in phases. Neither approach eliminates the need for final validation, ownership rules, and cutover planning.
7. Compare Technical Requirements and Total Costs
Licensing is only one part of migration costs. You should also take into account infrastructure, consulting services, the time spent by in-house engineers, data processing fees, training, and ongoing maintenance.
For instance, a platform priced according to monthly active rows behaves differently from software licensed per connection or charged according to cloud resource consumption.
A drag-and-drop interface can simplify configuration, but more complex workflows may still require technical expertise. Choose a tool that matches both your migration requirements and the people responsible for operating it.
Common Data Migration Challenges and How to Avoid Them
Even with reliable migration tools, unexpected problems can affect the outcome. The most common issues involve data quality, system compatibility, performance, and insufficient preparation.
Poor data quality is particularly problematic because automated migration can transfer existing inconsistencies very efficiently. A successful migration should therefore include data cleansing before the main transfer, not only corrections after problems appear.
Compatibility problems deserve similar attention. If source and target systems handle data differently, a technically completed migration may still produce information that teams cannot use as intended.
Data Migration Best Practices: Before, During, and After
Choosing appropriate migration tools is an important first step, but the entire migration process needs a clear plan. The following practices help organizations maintain data integrity, control risks, and avoid unnecessary disruption.
Before Migration: Audit and Prepare
Start with a comprehensive audit of the existing environment to identify the data sources involved, data volume, target systems, and information that must be preserved.
For database migration, this includes reviewing schemas, dependencies, data quality, and application connections, while work management or ITSM migrations require additional attention to projects, workflows, custom fields, automation rules, permissions, and external integrations. Before transferring data, define clear success criteria, such as acceptable downtime, required metadata preservation, and the successful migration of critical records and relationships. This is also the right time to perform data cleansing, remove obsolete information, and resolve inconsistencies that could affect the migration results.
During Migration: Test, Monitor, and Control Changes
Begin with a pilot migration using representative data, including important fields, relationships, and potential edge cases. Reviewing the initial results allows teams to identify mapping problems and adjust the configuration before transferring larger data volumes. For complex migrations, incremental migration techniques can help reduce operational risks by dividing the process into manageable stages. Throughout the transfer, monitor migration progress, errors, and performance to identify incomplete records, unexpected data transformations, or processing delays. Teams should also have documented and tested rollback procedures prepared in advance, ensuring they understand how to restore service or return to the previous environment if critical issues occur.
After Migration: Validate and Reconcile Data
Once the transfer is complete, perform thorough data validation and reconciliation to confirm that information has been transferred accurately and remains usable in the target system.
Database migrations may require comparing record counts, checksums, and query results, while application migrations should include verification of tasks, workflow statuses, comments, attachments, custom fields, and supported relationships. User acceptance testing is equally important, as employees need to confirm that the new environment supports their daily workflows. Any discrepancies should be documented and resolved, with continued monitoring of system performance after the migration. Where operationally and legally feasible, maintaining access to the previous environment for an appropriate period provides additional protection against issues discovered after the transition.
Final Thoughts: Which Data Migration Tool Should You Choose?
The best data migration tools address different problems, so the decision should begin with your source systems, destination platforms, data complexity, and operational requirements.
For database modernization, AWS Database Migration Service and Oracle GoldenGate are worth evaluating. Fivetran, Hevo Data, Airbyte, and Matillion focus on data pipelines and cloud analytics workflows. Informatica, Qlik Talend Cloud, and SnapLogic address broader enterprise integration requirements, while Azure Data Factory and Apache NiFi provide options for specific cloud and self-managed environments.
For organizations migrating between supported work management, ITSM, and DevOps applications, Getint offers a different set of capabilities, including configurable mappings, historical data migration, and synchronization during phased transitions.
Whatever platform you select, invest time in preparation, testing, and data validation. A carefully planned migration helps protect business information and makes the transition easier for the people who depend on it.
Or maybe you are already planning a Jira, Azure DevOps, ServiceNow, or other supported application migration?
Book a demo with Getint to discuss your migration requirements and explore how to transfer your data between systems.
