Cloud providers are racing to make data migration faster and smarter. In an effort to lower costs and downtime, AWS recently added Transform, an AI-powered Service that automates the Windows-to-the-Cloud migration, a full-stack process. Meanwhile, Microsoft released Migrate Agent, which uses artificial intelligence to scan on-prem workloads, suggest the best migration path, and deliver migrations across hybrid or multi-cloud settings. Furthermore, Google Cloud modernized the Database Migration Service with Gemini-powered conversion tools to simplify complex migrations, such as Oracle to PostgreSQL.Snowflake is adding schema conversion and real-time checking into the system through automation. These developments show that AI-led automation is transforming the migration process, which was once sluggish and manual, into a fast, smart process.
Hybrid or multi-cloud environments are adopted by an estimated 90% of businesses, which contributes to an intense demand for effective methods of data migration. As a matter of fact, the market of data migrations will be estimated at between $10.5 billion in 2025 and more than $30 billion in 2034. Moreover, by using AI-based mapping and verification technologies, the migration process error rate has already decreased by up to 40%, significantly increasing the success rate and making the transition to modern platforms much easier.
Reading further in this blog, you will learn how to best undertake data migration, the tools that make data migration easier, and what enterprises can do to assist in modernizing their systems in a safe and efficient manner.
Key Takeaways Modernizing systems through data migration helps to improve efficiency, analytics, compliance, and decision-making. Different approaches to migration: Big Bang, Trickle, ETL, phased fit. Different complexity and downtime needs Assessing current systems, data quality, and dependencies is the way to ensure the right migration strategy. Using advanced tools and platforms helps in migrating easily, reducing errors, and focusing on data integrity. Strong security and compliance measures are taken to secure sensitive data during the migration process. Kanerika’s developed approach with FLIP automation provides faster, accurate, and risk-free migration.
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Businesses today produce a large volume of data in various formats from various sources such as CRM systems, ERP systems, cloud applications, and operational tools. Managing this data in an efficient way is no longer a choice. Organizations require accurate, timely, and secure information to make strategic decisions, ensure operational efficiency, and stay competitive in the fast-moving markets. As a result, data migration, which is the process of transferring data from legacy to modern systems, has been identified as a high-priority business requirement.
Legacy systems create real business problems . Reports that should take minutes require hours or days to generate. Different departments report conflicting numbers because they’re pulling from different systems. As a result, IT teams use 60% of their budget to maintain old infrastructure rather than developing new capabilities. Security teams have a tough time enforcing consistent access controls in broken-up databases. These are not theoretical concerns. They have direct consequences on revenue, customer satisfaction, and competitive positioning.
The Main Causes as to Why Companies Focus on Data Migration Include: Migration to Modern Systems: It is the case that legacy systems are normally slow, obsolete, or incapable of connecting to new analytics and reporting systems. In this way, the transition to the modern systems improves the work, reduces the level of downtime, and leads to business evolution.Better Access to Data: Teams can now enjoy fast, easy access to information because it is combined in one place, and multiple sources are used to improve interdepartmental decision-making.Operation efficiency: Operation Automation reduces the number of hands and errors and simplifies finance, sales, and marketing operations.Regulatory Compliance and Security: Data migration is done in a proper manner, which allows businesses to stay in line with data privacy regulations (GDPR, HIPAA, and other local regulations) and handle sensitive data.Business Intelligence Support: The existing platforms can support advanced reporting, visualization, and analytics to enable organizations to determine insights and develop.
The industry research shows that over 70% of businesses regard a data migration as a strategic initiative that can improve data quality, lower costs, and provide an opportunity to act based on data. Whether we should migrate or not is not the issue; the question is how to go about it without disrupting operations.
A Breakdown of the Most Common Data Migration Techniques 1. Big Bang Migration Big bang migration is a one-time, full-scale transfer in which all data is moved from the source system to the target environment in a single event. It is fast, cost-efficient, and works best when businesses want a quick shift without maintaining two systems at the same time. This approach is often chosen for smaller datasets or when organizations have a narrow migration window.
The method requires extensive preparation. Teams must review all data mappings, thoroughly test the transformation logic, and prepare detailed rollback procedures. The cutover happens during a scheduled downtime window, usually over a weekend or during a maintenance period when business impact is minimal.
A key insight many teams overlook is that big-bang migrations require absolute clarity in cutover planning. Even small misalignments in checking or downtime readiness can slow operations. In particular, companies adopting this method usually set up command center teams that monitor the switch in real time, with dedicated personnel tracking data loads , application behavior, integration points, and user access. This intensive oversight reduces issues and enables rapid response when problems emerge.
Best for: Small to medium datasets, systems with few integrations, organizations with defined maintenance windows, and projects with tight deadlines.
Not recommended for: Mission-critical 24/7 systems, highly complex integrations, large enterprise datasets exceeding several terabytes, environments where testing reveals significant unknowns.
2. Trickle Migration Trickle migration transfers data gradually in phases. Both systems run in parallel, allowing teams to test, check, and improve as they move data. It is ideal for businesses that need continuous operations without interruptions and want more control over the migration stages.
Teams typically migrate by business unit, geography, data domain, or time period. They might move historical data first, then transition active records. Or they migrate one department at a time while others continue using legacy systems. Consequently, this phased approach provides flexibility to adjust strategy based on real-world performance.
A practical benefit here is the flexibility to refine each phase based on findings from previous stages. Teams often discover hidden data dependencies or quality issues only after the first batches move, and the trickle approach gives them room to correct these without pressure. In addition, data checking happens step by step, allowing thorough testing before committing to the next phase. If issues arise, only a subset of the data needs to be fixed rather than a complete rollback.
Best for: Large enterprises with complex data structures , mission-critical systems requiring high availability, organizations with limited downtime windows, and teams that need step-by-step verification.
Not recommended for: Projects with urgent deadlines, simple migrations with minimal dependencies, organizations lacking resources to maintain parallel systems, migrations where ongoing sync creates complexity.
3. ETL-Based Migration ETL-based migration is widely used when data needs significant transformation before moving to the target system. Data is pulled from source systems, cleaned and enriched through transformation logic, and then loaded into the new environment. This method is ideal for companies modernizing from legacy formats or bringing together multiple sources into a central platform.
The transformation stage provides an opportunity to improve data quality . Teams standardize naming rules, convert data types, apply business rules, enrich records with reference data , and remove duplicates or outdated entries. Therefore, this cleaning process creates a foundation for reliable analytics and reporting in the new system.
ETL migrations reveal the real state of enterprise data. Many organizations uncover inconsistencies, redundant fields, or outdated structures only during ETL processing. As a result, the migration stage is a valuable opportunity to improve data standards and governance. Issues that have persisted for years in legacy systems get addressed in a structured way, resulting in cleaner, more trustworthy data in the target environment.
Best for: Legacy system modernization requiring significant data restructuring; bringing together multiple data sources with different formats; projects prioritizing data quality improvements; migrations where business logic must be applied during transfer.
Not recommended for: Simple database upgrades with minimal schema changes, migrations requiring near-real-time sync, projects where transformation logic is unclear or changing.
4. Cloud Migration Cloud migration involves moving data from on-premises systems to cloud platforms like Azure, AWS, or Google Cloud. It offers scalability, cost improvement, better storage efficiency, and better access. Businesses choose this approach to modernize their data system and support analytics , automation, and real-time workloads.
The shift to cloud requires rethinking data setup. Organizations must choose between infrastructure-as-a-service, platform-as-a-service, or software-as-a-service models. Storage layers need to be set up for frequently accessed data and archives, and for proper backup strategies to support disaster recovery.
The wide variation in cloud-native tools matters. Choosing the right combination of storage layers, security controls, and backup features can significantly reduce long-term costs. Furthermore, teams often find that the planning phase is more critical than the migration itself. Decisions about region selection, network setup, identity management, and service tier choices have lasting impacts on costs and performance .
Best for: Organizations seeking operational flexibility and scalability, companies wanting to reduce data center costs, businesses requiring global data access, and enterprises building AI and analytics capabilities.
Not recommended for: Organizations with strict data location requirements that prevent cloud use, companies with extremely latency-sensitive applications, and environments where cloud costs would exceed on-premises expenses.
5. Application Migration Application migration involves shifting data along with the application that uses it. This requires coordination among database teams, development teams, and system builders, as data structures often change when the application migrates to a new version or platform.
The challenge lies in maintaining business continuity while upgrading both application logic and data schemas. APIs might change. User interface elements get redesigned. Integration endpoints require updating. In turn, testing becomes complex because teams must check that the new application produces the same business outcomes as the old system.
A noteworthy operational factor is version compatibility. Even when applications support migration, differences in plugins, integrations, or workflows often surface only during testing. Moreover, preparing a setup compatibility map helps teams avoid deployment delays. This map documents every integration point, third-party dependency, custom extension, and workflow automation that could break during migration.
Best for: Application platform upgrades, SaaS transitions, legacy application retirement, and modernization projects requiring both application and data changes.
Not recommended for: Migrations where application and data can be separated, projects with unclear application setup, and situations where multiple applications share the same database.
Comparing Migration Techniques: Which Approach Fits Your Needs? Technique Timeline Downtime Complexity Best For Risk Level Big Bang Short (days to weeks) High (hours to days) Medium Small datasets, clear requirements High Trickle Long (months) Minimal (minutes) High Large enterprises, 24/7 systems Low ETL-Based Medium (weeks to months) Variable High Data quality improvements Medium Cloud Variable Depends on method Medium to High Modernization, scalability Medium Application Medium to Long Depends on approach Very High Platform upgrades High
How to Identify the Right Migration Technique for Your Needs The first step to selecting an appropriate data migration technique is having a clear picture of your business requirements, your existing systems, data format, and future modernization requirements. Most migrations are rushed by many organizations, which can result in budget overruns, delays, and mixed results. Thus, a designed system will guarantee data accuracy, minimise disruption, and provide a basis for future expansion.
1. Assess Your Current Data Environment Start by mapping all systems that hold important business information. Examine the volume of data, format, interdependencies, and update frequency. Big or complex data sets might need to be migrated in stages or in parallel, but small or simple systems can be transferred in a one-time bulk. It is necessary to document the location of data.
For example, a manufacturing firm might have an old ERP system for production, Salesforce customer records, and SAP financial records. These sources, along with their relations, allow defining the points of integration that should remain functional during migration. As such, come up with an inventory of data volume, frequency of upgrade, downstream requirements, and business urgency, which will be used as your roadmap in the migration.
2. Understand Operational Tolerance for Downtime Both migration techniques have different effects on the downtime. Other processes, such as e-commerce, banking, or logistics, cannot sustain long outages and might take advantage of parallel or staged strategies. Cutover methods can be used in systems whose downtime periods can be predicted. Measure the downtime cost.
For example, an e-commerce site that loses 50,000 dollars an hour in the peak season could not afford to have a 24-hour cut-over, but a back-office payroll solution could have time to migrate during the off-peak. Correspondingly, coordination of migration approaches with business cycles avoids the inconvenience of doing business at crucial times.
Compatibility between source and target systems is crucial. Review data formats, storage structures, naming rules, and security models. When compatibility is low, a migration technique that includes a structured transformation becomes essential.
Legacy systems may require significant conversion. Mainframe data encoded in EBCDIC must be translated to ASCII or Unicode. Hierarchical databases may need to be flattened into relational formats. Additionally, testing sample conversions early helps identify gaps, reduces rework, and ensures accurate outcomes after deployment.
4. Align the Migration Method With Future Plans Migration should support long-term technology goals. Techniques that merely copy existing systems may solve short-term issues but limit future growth. Consider whether your organization plans to expand reporting, automate workflows, bring in cloud platforms, or adopt machine learning .
For instance, companies implementing ML need data in compatible formats, while organizations building real-time dashboards require streaming-capable setups. Moreover, migration is an opportunity to improve data models, establish governance policies, and build scalable, future-ready systems.
5. Check Security, Compliance, and Governance Requirements Data migration passes through multiple pipelines and storage areas, making security critical. Review encryption, retention policies, audit logs, access control, and regulatory requirements. The healthcare, BFSI, and telecom sectors often require strict monitoring and documented checks.
Ensure encryption in transit and at rest, maintain detailed access logs, verify security controls, and test access restrictions before completing the migration. Furthermore, compliance with HIPAA, GDPR, or industry-specific standards must be maintained throughout.
6. Review Resource Availability and Team Expertise Different migration techniques demand varying levels of preparation, testing, and monitoring. Teams with limited technical skills may benefit from simplified approaches with minimal manual intervention, while experienced teams can handle complex, customizable paths.
Assess familiarity with target platforms; teams proficient in AWS will manage cloud migrations efficiently compared to those new to the platform. Finally, bringing in external experts can fill gaps, speed up timelines, and prevent costly mistakes. The investment in knowledge is often lower than the cost of failed migrations.
Key Challenges You May Face During Data Migration Data migration impacts operations, reporting, customer experience , and business continuity. Even with careful planning, organizations often face challenges that can delay projects, increase costs, or compromise data integrity .
1. Data Quality and Structural Inconsistencies Poor data quality slows migration. Outdated entries, inconsistent formats, missing fields, or duplicates create errors during transfer. Departments may use different identifiers, such as email addresses, account numbers, or tax IDs, making it difficult to bring them together. Therefore, early profiling, cleaning bad data , and defining transformation rules are essential to maintain accuracy.
2. Legacy System Limitations Older applications may lack proper documentation, modern export options, or support current formats. Mainframes can restrict concurrent pulling, and custom applications may have embedded business rules. These limitations often require step-by-step pulling, temporary tools, or customized programs. Consequently, documenting fields, business rules, and dependencies ensures smoother migration and supports ongoing operations.
3. Complex Application and Workflow Dependencies Business data connects across multiple systems. CRMs link to marketing tools, financial databases work with ERPs, and inventory connects to procurement systems. Ignoring these dependencies can break workflows, dashboards, or reporting pipelines. In turn, mapping dependencies, testing chains, planning cutover sequences, and preparing rollback procedures maintain operational continuity.
4. Security and Governance Risks Data is vulnerable during transfer. Temporary accounts, multiple copies, and open network channels increase risk. Industries handling sensitive information must ensure continuous oversight. Moreover, encrypt data , monitor access, audit activities, remove temporary files, and check controls in the new environment to prevent breaches and ensure compliance.
Large transfers can overload networks, slow applications, and interrupt services. Backup plans, rollback procedures, and monitoring tools are critical. Furthermore, schedule heavy transfers during low-usage periods, implement bandwidth controls, monitor system resources, and test rollback procedures to minimize disruption.
6. Change Management and Hidden Costs New dashboards, interfaces, or reporting layouts may confuse employees and reduce productivity. Users resist changes when the benefits are unclear. Early communication, training, reference guides, and support resources improve adoption. Additionally, unexpected issues like corrupted files, incompatible formats, or undocumented systems can increase costs by 30–50% and extend timelines. Plan backup options, allocate resources, and track actual costs versus estimates.
In the modern context, data migration relies on the platforms that are able to move, transform, check, and even secure data without affecting the routine activities. Those tools assist organizations in moving out of the legacy environments to either cloud or hybrid environments without compromising consistency and reliability. Consequently, the appropriate selection of a tool minimizes the human input, accelerates the migration and makes sure that business data remains clean, reliable, and sound throughout the process.
Large migration programs are still supported by ETL platforms. They facilitate structured, semi-structured and more unstructured datasets on databases, CRMs, ERPs, and tailors. Informatica PowerCentre, Talend and Microsoft SSIS are some of the solutions that have powerful mapping, automatized pipeline, re-usable workflow, and powerful transformation capabilities.
These environments offer graphical development toolsets in which teams do not need to write much code to design data flows. Ready-to-use connectors are used to deal with typical source and target systems. Moreover, transformation libraries have cleansing functions, enrichment functions, aggregation functions, and checking functions.
These tools also aid the teams to modernize the old data models, fix inconsistencies and load clean data in the target environment with reduced manual steps. The operational basis of reliable migrations is given through job scheduling, error handling, logging and monitoring capabilities.
2. Cloud-Native Migration Services Migration services provided by cloud vendors are aimed at complexity reduction and large-scale transfer. AWS Database Migration Service, Azure Data Migration Service, and Google Cloud Database Migration Service enable a team to bring databases, files, and workloads into cloud-native configurations.
The services are compatible with uninterrupted copying, schema conversion, and automatic checking and are, therefore, suitable when the business is migrating to scalable cloud storage or managed database systems. In addition to that they support heterogeneous migrations, in which the source and target databases are dissimilar, and it automatically transforms the data types and adapts the schemas.
Cloud-native tools interact sufficiently with their systems. AWS DMS is linked to RDS, Aurora and Redshift easily. Azure Data Migration Service is native to Azure SQL, Cosmos DB, and Synapse. This is a tight integration that makes authentication, networking and performance improvement easier.
Organizations that require nearly zero downtime are required to use copying tools. Oracle GoldenGate, Qlik Replicate and HVR real-time replicate data that maintain the source and destination systems synchronized in the process of migration. Such platforms receive alterations when they transpire in source databases and implement them to targets with a low delay.
This is a change data capture model that preserves transactional integrity and allows it to proceed in migrating. This could minimize the chances of the production outages, and the apps keep running, even to the extent that data is transferred behind the scenes. Specifically, the banking, e-commerce, and manufacturing settings are some of the most common places where such platforms have been adopted, and continuous connectivity is paramount.
The transactions are done continuously in the financial institutions. Retailers have 24 hours e-commerce websites. Manufacturers have production lines that operate 24/7. These organizations require migration strategies that will not interfere with the business operations.
The data quality platforms facilitate the cleansing, check, deduplication and standardization of data before and after migration. Talend Data Quality, Ataccama as well as IBM InfoSphere are some of the products that aid the organizations in determining the completeness, conformity and accuracy.
Governance tools like Collibra and Alation document lineage, ownership, and rules, making mapping easier and supporting compliance requirements. Consequently, this system ensures the migrated data remains reliable and audit-ready. Data catalogs provide searchable inventories of data assets. Lineage tracking shows how data flows from source through transformations to final reports. Policy management enforces access controls and retention rules.
5. Bulk Transfer and Physical Migration Appliances Some projects have very large data sets that cannot be moved effectively on the normal networks. To cope with these situations, such services as AWS Snowball, Azure Data Box, or Google Transfer Appliance offer safe physical systems that may transfer multiple terabytes of information simultaneously.
Organizations ship empty appliances to their data centers , load them with data, and ship them to cloud providers for high-speed upload. This approach bypasses network bandwidth limits and speeds up migrations that would otherwise take weeks or months over internet connections.
These appliances help businesses speed up transfer times for historical archives, media libraries, or backup stores. Media companies migrate video libraries. Research institutions transfer scientific datasets. Financial services firms move decades of transaction archives.
Metadata platforms provide visibility into where data comes from, how it is structured, and how it is used. This helps teams identify dependencies, remove redundant datasets, and create accurate migration mappings.
Tools like Informatica Axon, Collibra Catalog, and Apache Atlas strengthen governance and support long-term data management after migration. Furthermore, they document business definitions, technical specifications, data quality rules, and usage patterns. This documentation helps new team members understand data structures and supports ongoing data management .
Best Practices to Ensure a Smooth and Secure Migration Process 1. Start With a Full Assessment of Your Data Environment Begin by checking source systems, data volumes, dependencies, formats, and quality issues. This baseline assessment helps identify gaps, inconsistencies, and potential risks early.
Create thorough documentation for each data source, including its business purpose, technical specifications, owner, users, update frequency, and downstream systems that depend on it. Consequently, this inventory becomes your migration blueprint.
Understanding what needs to be moved, archived, or transformed prevents delays and reduces hidden problems during execution. Teams can plan realistic timelines, allocate proper resources, and set achievable milestones.
2. Build a Clear Migration Strategy and Roadmap A detailed roadmap defines timelines, responsibilities, phases, and backup plans. It outlines how data will move, how business processes will be supported during the shift, and what success criteria look like.
Break the migration into separate phases with clear deliverables. Define what “done” means for each phase. Identify critical path activities that could delay the entire project. In addition, plan buffer time for unexpected issues.
A strong strategy keeps teams aligned and ensures the migration progresses without unexpected interruptions. Regular status reviews, risk assessments, and stakeholder communications maintain momentum and visibility.
3. Clean and Standardize Data Before Migration Data cleansing is one of the most critical steps. Standardizing formats, removing duplicates, resolving mismatched fields, and checking accuracy help ensure that the destination environment receives high-quality information.
Don’t migrate garbage into your new system. Invest time fixing source data. Establish data quality standards. Put in place checking rules. Remove obviously incorrect records. Standardize naming rules. Bring together duplicate entries.
Clean data improves reporting, forecasting, compliance, and long-term system performance. Therefore, it also reduces troubleshooting effort after migration. Problems identified and fixed before migration don’t become production incidents later.
4. Use Incremental or Phased Migration Approaches Instead of moving everything at once, phased methods help reduce risk and minimize downtime. Teams can migrate by module, department, dataset, or use case. This approach allows real-time checking, smoother cutover, and easier rollback if issues arise.
Start with non-critical data to test processes. Move historical archives first. Migrate reference data before transactional data. Bring over one department or business unit before expanding to others.
Step-by-step movement is especially useful for enterprises with complex or interconnected systems. Furthermore, each phase provides learning opportunities. Teams refine procedures. They identify and fix issues . They build confidence before tackling more complex datasets.
5. Ensure Strong Security and Compliance Controls Every stage of migration should include encryption, access controls, audit logs, and compliance checks. Protecting sensitive financial, healthcare, or customer data is essential when transferring across networks or cloud platforms.
Put in place encryption for data in transit and at rest. Use VPNs or dedicated network connections for sensitive data transfers . Maintain detailed audit logs showing who accessed what data when. In turn, check that security controls transfer correctly to the new environment.
Strong security practices help prevent breaches, maintain trust, and meet regulatory requirements. They also show due diligence if audits or compliance reviews occur.
6. Test Rigorously and Monitor After Go-Live Testing should check data accuracy, performance, system behavior, and integration points. Run parallel systems during testing to compare outputs. Check that reports match between the old and new environments. Test application workflows end-to-end.
After the migration, continuous monitoring ensures the new environment operates as expected. Track query performance. Monitor data quality metrics. Watch for integration failures. Gather user feedback.
This helps teams catch differences early, address performance issues, and stabilize operations quickly. Moreover, plan for a stabilization period where support teams are readily available to address issues. Don’t declare victory on day one. Monitor closely for at least two weeks after cutover.
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Kanerika Migration Services: Accelerating Modernization for Today’s Enterprises Organizations are under pressure to modernize their data , analytics, and digital platforms as business demands change. Legacy systems struggle with growing data volumes, slow reporting cycles, rising operational costs, and limited support for advanced analytics .
The challenges are consistent across industries . Finance teams wait days for reports that should be instant. IT budgets go toward maintaining aging infrastructure instead of innovation. Security teams struggle to enforce policies across broken-up systems. Business users work around limitations rather than getting the capabilities they need.
Kanerika helps enterprises make this shift smoothly through fast, secure, and well-structured migration services that protect business continuity at every step. Furthermore, our approach combines deep engineering knowledge with strong planning, rigorous testing, and clean cutovers. Every migration is supported by our proprietary FLIP platform, a smart tool that automates large parts of the process and ensures accuracy, consistency, and speed.
A Unified, End-to-End Migration Portfolio Kanerika supports a broad range of migration needs, enabling enterprises to modernize systems, reduce technical debt, and adopt cloud-native, AI-ready setups.
1. Application and Platform Migration We migrate applications from outdated systems to modern, cloud-native platforms to improve scalability, performance, and security. This includes replatforming, refactoring, and improving workloads for AWS, Azure, and GCP. Whether you’re moving from mainframes to microservices or upgrading legacy .NET applications to modern cloud setups, our team handles the technical complexity while maintaining business continuity.
2. Data Warehouse to Data Lake and Lakehouse Migration Enterprises shifting from rigid warehouses to flexible data lakes or lakehouse setups gain better support for structured, semi-structured, and unstructured data. We modernize pipelines, storage, and governance to help organizations adopt platforms like Databricks and Microsoft Fabric . Consequently, this transition enables advanced analytics, machine learning , and real-time processing that traditional warehouses cannot support.
3. Cloud Migration Kanerika transitions workloads to secure and scalable cloud environments, ensuring cost improvement, operational efficiency, and stronger disaster recovery. We handle identity integration, network setup, data transfer, and performance checking across Azure and AWS. In particular, our approach considers not just technical migration but also cloud economics, helping you choose the right service tiers and settings for the best cost-performance.
4. ETL, Integration, and Pipeline Migration We modernize ingestion and transformation workflows by migrating legacy ETL tools to cloud-first orchestration platforms. This includes transitions such as SSIS to Microsoft Fabric , Informatica to Databricks, and on-premise schedulers to cloud orchestration. Moreover, modern pipelines provide better monitoring, easier maintenance, and stronger integration with cloud-native analytics platforms.
5. BI and Reporting Migration Enterprises move to modern reporting platforms to gain interactive dashboards, real-time insights, and better governance. Kanerika supports migrations from Tableau, Cognos, SSRS, and Crystal Reports to Power BI , ensuring accurate visual redesign, semantic modeling, and secure workspace setup. We don’t just recreate old reports. We redesign them to take advantage of modern platform capabilities.
6. RPA and Workflow Automation Migration We help organizations streamline automation systems by shifting from legacy RPA tools to Microsoft Power Automate. This includes improving flows, better exception handling, and aligning automation with enterprise standards. As a result, modern automation platforms work better with existing systems and provide centralized governance.
FLIP: Accelerating Migrations with Smart Automation Kanerika’s FLIP platform brings speed, precision, and structure to complex migrations. It automates up to 80% of repetitive tasks such as data profiling , mapping, dependency detection, transformation checking, and lineage tracking. This significantly reduces manual effort, lowers risk, and ensures consistent results across large programs.
FLIP handles tasks that usually take weeks, such as parsing legacy code, generating transformation logic, and verifying outputs. It also documents all changes and the reasons behind them. FLIP supports complex enterprise transitions, including SSIS to Microsoft Fabric , Informatica to Databricks, Talend to Power BI, and legacy warehouses to cloud-native lakehouses.
With FLIP, organizations adopt modern setups in weeks instead of months, without putting data quality or business logic at risk. Furthermore, the platform has migrated thousands of ETL jobs, reports, and workflows across dozens of client implementations.
Security, Governance, and Compliance at the Core Kanerika operates with strict adherence to ISO 27001, ISO 27701, SOC 2, and GDPR standards. Every migration includes safeguards for data privacy, access control, audit readiness, and operational continuity. This gives enterprises the confidence to modernize without exposing themselves to security or compliance risks .
We encrypt data, enforce access controls, maintain audit logs, and verify security policies in the new environment. Consequently, we help organizations meet regulatory requirements without putting migration timelines at risk.
Our Phased Migration Approach Discovery and Mapping: Analyze systems, dependencies, user flows, and risks; create detailed documentation guiding all later work.Blueprint and Planning: Design the target setup, define the sequencing, select cutover windows, and establish rollback plans to form the migration roadmap.Pilot Migration: Migrate a representative workload of small scale to test the accuracy, performance, and user experience, and then migrate the full workload.Execution in Waves: Automate and validate with FLIP tools the workload of each wave by migrating to the next wave.Cutover and Stabilization: Transition users to the new system, test performance, troubleshoot, and train with intensive support during the first few weeks.
Kanerika delivers modernization that is fast, stable, and with quantifiable business value . With FLIP tools, deep platform knowledge, and a structured method, we help enterprises unlock the full advantages of cloud-native and AI-ready environments, without disruption or risk.
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FAQs 1. What are the most commonly used data migration techniques? The most common techniques include big bang migration, phased migration, incremental migration, and trickle migration. Each method is chosen based on data volume, system complexity, and downtime tolerance.
2. How do I decide which data migration technique is right for my project? You should consider factors like data size, source and target system compatibility, project timeline, business continuity needs , and acceptable downtime. A proper assessment helps match the right technique to your goals.
3. What risks are involved in data migration? Data loss, incomplete transfers, system downtime, and performance disruptions are common risks. Poor planning or lack of testing increases these risks, so organizations usually follow strict validation and backup steps.
4. How long does a typical data migration take? The timeline depends on the amount of data and the technique used. Small migrations may take hours, while large enterprise migrations can take weeks. Testing, validation, and fixing issues often add to the overall duration.
5. How can I ensure data quality during the migration process? You can maintain quality by cleaning data before migration, validating each stage, using automated tools to detect errors, and performing post migration checks. Continuous monitoring helps ensure accuracy and consistency.
