Expert Data Platform Migration Services

Data platform migrations carry five consistently high-impact risks:

Data loss or corruption: Legacy schema mismatches and format incompatibilities cause up to 45% of migration failures. Without automated validation and reconciliation, inconsistencies go undetected until post-go-live.

Business logic gaps: ETL workflows built over years embed undocumented transformation rules, exceptions, and dependencies. Manual rewrites routinely miss these — causing incorrect data outputs that are hard to trace.

Downtime and business disruption: Migration-related outages cost an average of $5,600 per minute. Complex migrations can cause 24-72 hours of disruption if not properly staged and rollback-tested.

Scope creep and timeline overruns: 61% of migration projects exceed planned timelines by 40-100%. Poor upfront discovery of data volumes, dependencies, and quality issues is the primary driver.

Security exposure: Sensitive data in transit is exposed during migration. 31% of enterprise migrations report compliance violations or data exposure incidents during the process.

Mitigation requires automated discovery before migration begins, phased rollout with parallel running periods, continuous validation checkpoints, and rollback procedures tested in advance.

Kanerika’s FLIP migration accelerators address the top two risks directly: automated extraction and conversion preserves business logic end-to-end, while built-in validation, schema reconciliation, and error handling catch data quality issues during — not after — migration.

Timeline depends heavily on the scope, complexity, and approach — manual versus automated:

Simple, single-tool migrations (e.g., one BI platform to another with limited dashboards): 4-8 weeks with automated tooling, 3-6 months manually.

Mid-complexity ETL platform migrations (e.g., Informatica to Microsoft Fabric with moderate workflow volume): 8-16 weeks automated, 6-12 months manually.

Enterprise-scale platform overhauls (e.g., full Azure stack to Microsoft Fabric including ADF, Synapse, and SSRS): 3-9 months depending on asset volume.

RPA migrations (e.g., large UiPath codebase to Power Automate): documented cases show 90 days for codebases that took 2 years to build, when automated migration tooling handles conversion.

The key variable is automation coverage. Organizations relying on manual migration consistently run 40-100% over their initial timeline estimates. The industry-wide failure rate for on-time, on-budget delivery stands at 80%, almost all attributable to manual approaches and insufficient pre-migration discovery.

These terms overlap but describe different scopes:

Data migration is the movement of data from one storage system, database, or application to another. It can occur within the same infrastructure or as part of a broader move to a new platform. The focus is on data transfer accuracy, completeness, and continuity.

Platform modernization is broader — it involves replacing or upgrading the underlying data infrastructure, analytics tools, and ETL workflows. This includes migrating the data and rebuilding or converting pipelines, reports, and business logic to work in the new environment. Informatica to Databricks is a modernization initiative, not just a migration.

Cloud migration refers specifically to moving workloads, applications, or infrastructure from on-premises systems to cloud environments. It may or may not involve changing the data platforms themselves.

In practice, most enterprise projects involve all three layers simultaneously — data moves, platforms change, and the target environment is cloud-native. Treating them as separate work streams is where underplanning typically occurs.

This is one of the most technically complex migration paths because Informatica PowerCenter workflows embed years of transformation logic, session configurations, parameter files, and exception handling that has rarely been fully documented.

The key steps for preserving business logic:

1. Automated extraction: Connect to the Informatica repository using pmrep protocols to extract mappings, workflows, transformations, and metadata — not just the surface-level logic but all dependencies.

2. Dependency mapping: Map all source-to-target relationships, lookup conditions, update strategies, and multi-transformation chains before writing a single line of Fabric code.

3. Intelligent conversion: Convert Informatica objects into Fabric-compatible Data Pipelines, Dataflows Gen2, and Notebooks. Complex Informatica transformations (like Expression, Router, Joiner, and Lookup transforms) have direct Fabric equivalents that preserve logic without manual rewriting.

4. Validation: Run parallel execution — Informatica outputs versus Fabric outputs — against representative production data sets. Schema reconciliation catches mismatches before cutover.

5. Exception handling migration: Informatica’s error tables and reject file logic must be explicitly mapped to Fabric’s pipeline monitoring and error routing capabilities.

Automated tooling can handle 70-80% of this conversion. The remaining 20-30% involves edge cases that require human review — typically custom Java transformations and highly complex mapplet logic.

Kanerika’s FLIP platform handles Informatica to Fabric migration through the FIRE connector, which securely connects to the Informatica repository, extracts complete workflow packages, and converts them into Fabric-ready pipelines. Business logic, data relationships, and transformation rules are preserved throughout the automated conversion process.

ROI from platform modernization comes from three sources — cost reduction, productivity gains, and strategic value:

Licensing and infrastructure cost savings: Informatica’s per-processor or per-core licensing is among the most expensive in enterprise software. Organizations migrating to Microsoft Fabric or Databricks typically see 40-70% reduction in annual licensing costs. Fabric’s capacity-based pricing, especially for organizations already on Microsoft 365 or Azure, is substantially cheaper. Cloud-native platforms also eliminate on-premises server maintenance costs.

Developer and operations productivity: Modern platforms with native automation, CI/CD integration, and low-code data flows reduce development effort by 30-50%. Teams spend less time maintaining fragile legacy pipelines and more time building analytical capabilities.

Data processing performance: Organizations migrating ETL workloads to cloud-native platforms report 20-40% improvements in processing speeds as distributed compute replaces single-node on-premises execution.

Strategic value: Fabric and Databricks architectures are AI-ready by design — they support vector storage, MLOps pipelines, and real-time analytics that legacy Informatica environments cannot deliver.

Most organizations see positive ROI within 12-18 months of migration completion, with the break-even point accelerating when automated migration tooling reduces the upfront implementation cost.

Data quality during migration requires controls at three stages, not just post-migration testing:

Pre-migration: Data profiling and quality assessment before the migration begins. Identify duplicate records, null values, referential integrity gaps, and outliers in source systems. Establish quality baselines so post-migration comparison is meaningful.

During migration: Row-count reconciliation at each transformation stage. Hash-based checksums verify that data arriving at the target matches source exactly. Automated exception logging captures and routes records that fail validation rules.

Post-migration: Parallel-run periods where source and target systems produce outputs simultaneously. Business user acceptance testing validates that reports, dashboards, and downstream processes produce expected results before source system decommission.

Poor data quality affects 84% of migrations (Cloudficient, 2025). The most common failure mode is discovering quality issues after cutover, which forces expensive rollback or remediation work in the new environment. Pre-migration profiling eliminates most of these surprises.

FLIP’s validation layer handles automated reconciliation throughout the migration process — not just at the end. Built-in schema reconciliation, data quality checks, and error handling run continuously during conversion, with migration reports providing full visibility into what was converted, what failed, and why.

UiPath-to-Power-Automate migration is driven primarily by cost and ecosystem consolidation — organizations running Microsoft 365 and Azure environments increasingly want automation on a platform they already pay for.

The technical complexity is significant: UiPath XAML workflows contain custom activities, exception handling logic, orchestration configurations, and attended-versus-unattended workflow separation that does not map directly to Power Automate’s connector-based architecture.

Key phases in RPA migration:

1. Workflow inventory and classification: Identify all UiPath workflows, assess their complexity, and classify them by automation type (attended, unattended, hybrid). Not all UiPath workflows have equivalent Power Automate counterparts.

2. Logic extraction: Extract business rules, exception handling, and workflow dependencies from XAML files.

3. Conversion: Convert XAML logic to Power Automate flows, mapping UiPath activities to equivalent Power Automate actions and connector calls. Complex UiPath selectors require rework for Power Automate’s browser/desktop capture model.

4. Testing: Replay production scenarios in Power Automate to validate functional equivalence before decommissioning UiPath.

The most frequent failure point is assuming 1:1 parity between platforms. UiPath’s orchestration capabilities are more mature than Power Automate’s; some complex scenarios require architectural redesign, not just conversion.

Kanerika’s FLIP RPA Migration Accelerator automates UiPath-to-Power-Automate conversion, translating XAML workflows into Power Automate flows while preserving all rules, logic, and exception handling. Published results include 90-day migrations of 2-year UiPath codebases, 50% reduction in migration effort, and 75% reduction in annual licensing costs post-migration.

Eight criteria matter most when evaluating a migration partner:

1. Platform-specific depth: General ‘cloud migration’ experience is not the same as documented expertise in the specific tools you are migrating from and to. Evaluate case studies for your exact migration path.

2. Automation tooling: Does the partner have purpose-built accelerators, or do they rely entirely on manual scripting? Automation directly determines timeline and cost.

3. Business logic preservation methodology: How do they handle undocumented transformation logic, custom functions, and exception handling? This is where migrations most commonly fail.

4. Data quality framework: What validation approach do they use during — not just after — migration?

5. Post-migration support: The go-live is not the end. Who handles performance optimization, pipeline monitoring, and issue resolution in the weeks after cutover?

6. Technology partnerships: Microsoft Solutions Partner, Databricks Consulting Partner, and similar certifications indicate platform-validated expertise, not just self-claimed specialization.

7. Security and compliance certifications: ISO 27001, SOC 2, and GDPR compliance matter when sensitive enterprise data is being moved.

8. Measurable outcomes: Ask for specific metrics from comparable migration projects — timelines, cost savings, error rates, and post-migration performance improvements.

Kanerika holds certifications across all eight criteria: Microsoft Solutions Partner for Data and AI, Databricks Consulting Partner, ISO 27001, ISO 27701, SOC 2, ISO 9001, and CMMI Level 3. FLIP migration accelerators are purpose-built for each supported platform path — not generic scripting.

FLIP is Kanerika’s proprietary AI-enabled, low-code/no-code intelligent automation platform. In the context of migration, FLIP operates as a set of purpose-built accelerators that automate the most time-consuming and error-prone phases of platform migration: discovery, asset extraction, logic mapping, format conversion, and validation.

Where traditional migration approaches require developers to manually read source system configurations and rewrite them in the target environment, FLIP automates 70-80% of this work. The platform connects directly to source repositories — Informatica PowerCenter, UiPath Orchestrator, Tableau Server, Cognos environments, Crystal Reports RPT files — extracts complete asset inventories, and converts them into target-platform-ready formats while preserving business logic, transformation rules, and metadata.

This automation shift has a direct impact on three migration outcomes: timeline (weeks instead of months), accuracy (automated conversion eliminates rewrite errors), and cost (60-70% reduction in labor compared to manual approaches). Post-migration, organizations receive detailed logs, migration reports, and source documentation for all converted assets.

FLIP supports twelve distinct platform migration paths across data and RPA categories:

Data Platform Migrations:

Azure Data Factory / Synapse to Microsoft Fabric

Informatica PowerCenter to Microsoft Fabric

Informatica PowerCenter to Databricks

Informatica PowerCenter to Alteryx

Informatica PowerCenter to Talend

SQL Server Services to Microsoft Fabric

SSIS to Microsoft Fabric

BI and Reporting Migrations:

Tableau to Microsoft Power BI

IBM Cognos to Microsoft Power BI

Crystal Reports to Microsoft Power BI

SSRS to Microsoft Power BI

RPA Migrations:

UiPath to Microsoft Power Automate

Each accelerator is purpose-built for its specific migration path — not a generic conversion tool applied across platforms. This means FLIP understands the native structures, metadata formats, and logic constructs of each source platform and produces output optimized for the target environment rather than a direct translation.

Published results from Kanerika’s FLIP migration deployments include:

Data Platform Migrations (FLIP Data Accelerators):

30% improvement in data processing speeds

40% reduction in operational costs

80% faster insight delivery

95% reduction in reporting time

Organizations save an average of 2,485 hours versus manual approaches

ROI typically achieved within 12-18 months

RPA Migration (FLIP RPA Accelerator — UiPath to Power Automate):

90-day migration of a 2-year UiPath codebase

50% reduction in migration effort through automation

75% reduction in annual RPA licensing costs post-migration

BI Migration:

Up to 80% automation in report conversion from Crystal Reports to Power BI

Consistent logic accuracy — FLIP preserved original report formulas throughout conversion

Zero data loss during migration with complete operational continuity

These outcomes are documented in production deployments, not benchmarks. Results reflect migrations where FLIP handled end-to-end conversion including validation — not just initial asset transfer.

Undocumented business logic is the single biggest risk factor in ETL platform migrations. Organizations that have run Informatica, SSIS, or similar platforms for 10-15 years typically have transformation rules, exception handling, and lookup logic that exist only inside the tool — never in documentation.

Kanerika’s approach to this problem has three components:

Automated deep extraction: FLIP connects to the source repository at the metadata level — not just the workflow surface — to extract all mappings, transformations, parameters, and dependencies. This includes logic embedded in reusable components, shared lookup tables, and workflow variables that manual review would miss.

Dependency graph analysis: Before conversion begins, FLIP builds a complete dependency map of source assets. This identifies circular dependencies, shared logic reused across multiple workflows, and transformation chains that must be migrated as units rather than individually.

Parallel validation: After conversion, Kanerika runs parallel execution periods where source and target environments process the same inputs simultaneously. Output comparison at the row and column level identifies any logic gaps before the source system is decommissioned.

The 20-30% of migration work that FLIP does not automate is human-reviewed precisely — it is the custom Java transformations, highly complex multi-hop mapplets, and proprietary function calls that require Kanerika’s data engineering team to redesign rather than convert.

Kanerika applies the IMPACT delivery methodology to all migration engagements. For platform migrations, the six phases work as follows:

Initiate (Assessment): Full inventory of source environment — all workflows, reports, pipelines, dependencies, and data volumes. Quality baseline assessment. Risk identification and prioritization by asset complexity.

Map (Architecture): Target architecture design for the new platform. Migration path definition including which assets can be automated versus manually redesigned. Dependency sequencing to determine migration waves.

Plan (Build): FLIP configuration for the specific migration path. Business logic documentation for complex edge cases. Test plan development with acceptance criteria defined by business stakeholders.

Activate (Integration): Automated migration execution using FLIP accelerators. Parallel running setup for critical workflows. Integration testing between migrated assets and downstream consumers.

Check (Test): Output reconciliation between source and target. Business user acceptance testing. Performance benchmarking on the new platform versus source system baselines.

Track (Deploy + Monitor): Production cutover with defined rollback triggers. Source system decommission planning. Post-migration performance monitoring and optimization.

The assessment phase is non-negotiable — migrations that skip it are the primary source of timeline overruns and business logic gaps post-cutover.

Kanerika delivers migration programs across eight industries, each with distinct migration priorities:

Banking and Financial Services: Informatica to Fabric for regulatory reporting pipelines; Crystal Reports to Power BI for risk dashboards; compliance data lineage preservation during platform transitions.

Insurance: Modernization of policy, claims, and underwriting reporting systems; migration of legacy ETL workflows to Fabric for faster actuarial analytics.

Healthcare: HIPAA-compliant migration of clinical and patient data pipelines; healthcare analytics platform modernization with data governance continuity.

Pharma: Migration of research and clinical trial data systems; FDA 21 CFR Part 11 compliance maintained throughout platform transitions.

Logistics and Supply Chain: ETL migration for fleet metrics, inventory, and supplier data; Power BI reporting modernization for operational visibility.

Manufacturing: Migration of production data pipelines to Fabric; SSRS to Power BI for shop floor and quality reporting.

Retail and FMCG: Tableau to Power BI migrations for customer and sales analytics; Informatica to Databricks for e-commerce data lakehouse modernization.

Automotive: Migration of production, supplier, and design data workflows; BI platform consolidation to reduce reporting latency.

The primary difference is automation depth and platform specialization. General systems integrators handle migration as a professional services engagement — they assess, plan, and manually convert assets using staff augmentation and project management. This approach works for small scopes but scales poorly: cost and timeline increase linearly with asset volume.

Kanerika’s approach differs in four ways:

Purpose-built automation: FLIP is not a consulting methodology — it is software that automates conversion work. Each accelerator is built for a specific platform pair, not a generic script applied across different tools.

Microsoft ecosystem depth: Kanerika is a Microsoft Solutions Partner for Data and AI and a Featured Microsoft Fabric Partner. This is not a marketing credential — it reflects validated technical capability and access to Microsoft engineering resources that general integrators do not have.

Databricks practice: Most migration partners that focus on Microsoft platforms do not have a concurrent Databricks practice. Kanerika is a Databricks Consulting Partner, which enables migrations that target lakehouse architectures — relevant for organizations whose analytics infrastructure spans both Microsoft and Databricks.

Integrated governance: Post-migration governance is built into Kanerika’s delivery through the KAN suite (KANGovern, KANGuard, KANComply). Data lineage, classification, and compliance monitoring activate immediately in the new environment — not as a separate engagement.

Certification baseline: ISO 27001, ISO 27701, SOC 2, ISO 9001, and CMMI Level 3 provide the security and process assurance that enterprise procurement and compliance teams require when sensitive data is being moved between platforms.