Informatica built its reputation on reliability. PowerCenter was the tool enterprises trusted for ETL at scale. But the economics have shifted. IPU-based pricing means costs climb as data volumes grow, and mid-to-large deployments regularly see five-year TCO land between $3.6M and $15M+.
Microsoft Fabric offers a different model: one platform for data engineering, warehousing, real-time analytics, AI, and Power BI, all built on OneLake . For organizations already on the Microsoft stack, the migration math is hard to ignore.
In this article, we’ll cover why enterprises are making this move, how Informatica capabilities map to Fabric, what the migration framework looks like, common challenges, and how Kanerika accelerates the process with FLIP.
Key Takeaways Informatica’s IPU pricing scales poorly as data volumes grow; Microsoft Fabric’s capacity-based model charges for compute consumed across all workloads under one SKU. Every major Informatica asset has a Fabric equivalent. PowerCenter mappings convert to Data Factory pipelines, ETL workflows become Dataflow Gen2, and governance moves to Microsoft Purview. A phased migration framework covering assessment, prioritization, architecture design, pipeline migration, and validation is what separates on-schedule projects from ones that run over. Common blockers such as complex ETL dependencies, legacy transformations, and data quality gaps are manageable with the right discovery process and automation tooling. Kanerika’s FLIP accelerator reduces manual migration effort by 75% and compresses most enterprise timelines from 12 to 18 months down to weeks.
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Why Organizations Are Moving Beyond Informatica Multiple pressures are converging at the same time, and most enterprises are already feeling more than one.
1. Rising Licensing and Infrastructure Costs Informatica’s IPU model ties billing directly to processing consumption. As data volumes grow, IPU spend follows, and the scaling is unpredictable. Mid-to-large enterprises typically see five-year TCO land between $3.6M and $15M+, before separate licenses for data quality, governance, and cloud editions are added.
The cost layers that compound over time:
IPU consumption spikes with data volume, creating budget variance that’s hard to forecast Axon, EDC, and cloud edition licenses add incremental overhead for every capability beyond core ETL Specialist Informatica talent commands a premium that grows as the certified developer pool shrinks
Microsoft Fabric uses capacity-based F-SKU pricing that covers all workloads under one SKU. Data Factory, Synapse, Power BI, real-time intelligence, and the Lakehouse are included. For any environment running significant Informatica volume, the pricing comparison alone justifies a formal evaluation.
2. Complex Multi-Tool Architectures In most Informatica environments, data integration, data quality, and governance run on separate licenses with separate configurations. Lineage tracking is partial, access controls are managed product-by-product, and compliance audit trails require manual assembly across tools.
The governance gaps that surface in regulated-industry audits:
No end-to-end lineage from source to consumption without manual documentation Role-based access defined separately across ETL, quality, and governance layers Compliance reporting assembled manually across disconnected product logs
Microsoft Fabric integrates directly with Microsoft Purview for unified lineage tracking and access policy management across the entire data estate. Teams already using Purview extend that framework to Fabric workloads from day one.
3. Growing Demand for Real-Time Analytics PowerCenter was engineered for on-premises batch processing. Modern data environments need real-time pipelines, cloud-native Spark compute, and distributed processing at scale. Informatica delivers these only through separate products or bolt-on configurations.
What batch-first architecture can’t support at modern volumes:
Real-time streaming pipelines without a separate streaming product Distributed Spark compute that scales without infrastructure overhead Event-driven pipeline triggers for time-sensitive business workflows
Microsoft Fabric supports real-time intelligence workloads natively, connecting directly to Azure ML and Fabric Data Agents without additional integration overhead. Teams that previously needed multiple Informatica products can consolidate onto one platform.
4. AI and Copilot Readiness Requirements Informatica’s AI capabilities require separate licensing and external integration. Running ML models against data processed by PowerCenter means moving data out, processing it elsewhere, and piping results back. Every handoff adds latency and creates a lineage gap.
The friction points that block AI adoption in Informatica environments:
No native Copilot or language-model interface for data querying AI workloads require data movement out of the ETL layer before processing Separate orchestration needed to connect PowerCenter outputs to ML pipelines
Fabric Data Agents (generally available after FabCon 2026 ) let teams query data in natural language, grounded in their own semantic models. For organizations building toward AI-driven analytics, Informatica’s architecture creates more friction than it resolves.
Mapping Informatica Capabilities to Microsoft Fabric Every major Informatica capability has a direct equivalent in Microsoft Fabric. Understanding the mapping is what makes the migration scope predictable rather than open-ended.
1. Informatica PowerCenter to Fabric Data Factory PowerCenter’s core ETL mappings, sessions, and workflows map to Data Factory Pipelines in Fabric. Source qualifiers become Fabric data sources, and transformation logic (filters, expressions, aggregators, and lookups) moves to Dataflow Gen2 transformation components. Scheduling configurations convert to Data Factory triggers, both time-based and event-based.
Standard mappings convert with high automation rates. Mappings with proprietary Informatica functions are flagged during discovery for targeted manual handling, rather than discovered mid-migration.
2. Informatica Cloud Data Integration to Fabric Data Pipelines IICS operates on a cloud-native, REST API-based asset model that differs from PowerCenter’s repository XML. The extraction and conversion path differs, but the end output is the same Fabric-native pipeline. IICS migrations require REST API extraction and a separate conversion module. FLIP handles both PowerCenter and IICS paths within the same project.
3. ETL Workflows to Dataflow Gen2 PowerCenter workflow orchestration (sequencing, dependency management, and error handling across mapping runs) moves to Dataflow Gen2 and Data Factory Pipeline activities. Linear workflows convert cleanly. Workflows with complex branching or unusual error paths need engineering review during discovery, planned upfront rather than mid-project.
4. Data Warehouses to Fabric Warehouse Existing data warehouse structures migrate to Fabric Warehouse, which supports full T-SQL compatibility. Schema objects, table structures, and stored procedures carry over with minimal rework for most standard warehouses. Performance optimization for Fabric’s distributed compute model typically runs during the validation phase.
5. Data Lakes to OneLake Data lake assets migrate to OneLake, Fabric’s unified storage layer in Delta Parquet format. OneLake supports shortcuts, meaning existing Azure Data Lake Storage Gen2 data can be referenced without physical data movement during a phased migration. The Microsoft Fabric OneLake architecture gives teams a single storage layer across all Fabric workloads.
6. Data Governance to Microsoft Purview Governance metadata tracked in Informatica Axon and EDC must be rebuilt in Microsoft Purview and the OneLake Catalog. This step is consistently the most underestimated part of enterprise migrations.
Cross-pipeline lineage, source-to-target documentation, and business logic annotations all need to be extracted from the Informatica environment before decommissioning. FLIP’s discovery module automates this extraction before conversion starts.
Informatica vs. Microsoft Fabric: A Side-by-Side Comparison Capability Informatica PowerCenter Microsoft Fabric Processing Model On-premises batch, proprietary engine Cloud-native Synapse Spark, distributed Deployment On-premises or Informatica Cloud SaaS, Azure-native Licensing IPU-based, scales with data volume Capacity-based F-SKUs, all workloads included ETL / Pipeline Mappings, workflows, sessions Data Factory Pipelines, Dataflow Gen2 Data Storage External warehouses and RDBMS OneLake, unified Delta Parquet Scheduling Session-based, PowerCenter Scheduler Data Factory triggers, time and event-based Governance Separate Axon and EDC licenses Microsoft Purview, built in AI / Copilot Requires external integration Native Copilot, Fabric Data Agents (GA) Real-Time Batch-first, limited streaming Real-Time Intelligence, native streaming Version Control Manual file management Native Git and Azure DevOps Talent Pool Specialist Informatica certifications Broad Azure and Microsoft platform skills Cost Visibility Unpredictable as volumes scale Consumption-based, consistent model
A Step-by-Step Informatica to Microsoft Fabric Migration Framework A structured migration framework is what separates on-schedule projects from ones that stretch into 18-month ordeals. Each phase gates the next, so problems surface early when they’re cheap to fix, rather than during cutover when they’re expensive.
1. Assess Existing Informatica Assets The first step is a complete inventory of everything in the Informatica environment before a single conversion decision is made. Teams that skip this step consistently run into scope surprises mid-migration.
What the assessment covers:
Full inventory of PowerCenter mappings, workflows, sessions, dependencies, and scheduling configurations Asset classification by tier: standard transformations (Tier 1), complex logic and parameterized mappings (Tier 2), proprietary functions or custom scripting (Tier 3) Data source audit confirming which connections have native Fabric connector support and which need OneLake shortcuts or custom connector design Governance metadata extraction: cross-pipeline lineage, source-to-target documentation, and business logic annotations FLIP’s FIRE module automates this assessment, connecting to the PowerCenter repository and producing a scored inventory with dependency chains fully mapped. Teams know their scope, complexity distribution, and estimated effort before planning begins.
2. Prioritize Migration Workloads With a complete inventory in hand, the next step is sequencing: deciding what migrates first and in what order. Migrating everything at once is a risk. Migrating in waves, with each wave validated before the next starts, is what keeps the project predictable.
Prioritization criteria that work in practice:
Business criticality: pipelines that feed revenue-critical reporting move last, after the team has proven the process on lower-stakes workloadsDependency structure: upstream pipelines must be migrated and validated before downstream consumers are convertedComplexity tier: Tier 1 standard mappings move first, Tier 3 proprietary function mappings move last with targeted manual handling planned in advance
Use Kanerika’s Migration ROI Calculator to model cost and time savings across different sequencing scenarios before committing to a plan.
3. Design the Target Fabric Architecture Before pipelines are migrated, the target Fabric environment needs to be fully designed. Migrating into an unplanned workspace structure creates technical debt that compounds over time.
Target architecture decisions that must be made upfront:
Fabric workspace structure, capacity tier, and region alignment OneLake folder hierarchy and Delta Parquet table naming conventions Purview lineage framework: how data flows will be documented and monitored post-migration Role-based access policy design for Fabric workspaces and OneLake Git integration and deployment pipeline structure for CI/CD
Teams that define this architecture before migration starts avoid the most common post-migration rework: restructuring workspaces and governance after pipelines are already live.
4. Migrate Pipelines and Workflows With the inventory complete and the target architecture defined, pipeline conversion can begin. This is where automated migration tooling delivers the most value: converting hundreds of mappings manually is the primary reason migrations stretch to 18 months.
What the conversion phase covers:
PowerCenter mappings converted to Dataflow Gen2 or Data Factory pipelines based on transformation complexity Workflow orchestration recreated in Data Factory pipeline activity chains Session scheduling converted to Data Factory triggers, time-based and event-based Proprietary function gaps addressed through targeted Power Query M or Spark notebook equivalents Connection strings and credential configurations updated for the Fabric environment
FLIP automates the conversion of standard mapping components and flags proprietary function gaps with specific descriptions, so engineering time is focused on complex edge cases rather than routine conversion work.
5. Validate Data and Business Logic Every converted pipeline runs in parallel against the live Informatica environment before any cutover. Output parity validation confirms that Fabric pipelines produce the same results as their Informatica equivalents.
The validation process covers:
Row count verification across source and target datasets Transformation output comparison for calculated fields, aggregations, and filtered results Edge-case scenario testing for boundary conditions in complex expressions Schedule and trigger behavior validation for time-based and event-based pipeline runs
Fabric pipelines are decommissioned from Informatica only after the business team has formally signed off on output parity. Different workflow groups can validate and cut over at different speeds, and there is no forced hard cutover.
6. Optimize for Performance and Cost Once pipelines are live in Fabric, the final phase focuses on performance tuning and cost optimization. Migrated pipelines that convert correctly from Informatica often have additional optimization potential in Fabric’s distributed compute model.
Post-migration optimization covers:
Spark partition tuning for high-volume pipelines running on Synapse compute Fabric capacity tier right-sizing based on observed compute usage patterns Pipeline scheduling consolidation to reduce unnecessary compute triggers OneLake storage optimization: Delta table maintenance, Z-ordering for query performance
This phase typically delivers incremental performance gains on top of the baseline improvements from moving off PowerCenter’s in-memory batch model.
Key Benefits of Migrating from Informatica to Microsoft Fabric 1. Unified Data and Analytics Platform Microsoft Fabric consolidates data engineering, warehousing, real-time analytics, AI, and Power BI onto a single platform with shared OneLake storage. Teams that previously managed separate Informatica, warehouse, and BI tool stacks can operate from one environment with unified lineage and governance.
2. Reduced Total Cost of Ownership Moving from IPU-based billing to Fabric’s capacity model eliminates the cost unpredictability that makes Informatica budgeting difficult at scale. Organizations running comparable workloads on Fabric typically see infrastructure cost reductions of 40 to 60%, with additional savings from consolidated tooling and reduced specialist talent dependency.
3. Faster Data Processing Fabric’s distributed Synapse Spark compute handles large-scale data volumes without the in-memory processing ceiling that limits PowerCenter at scale. Processing speed improvements of 60 to 70% are common for workloads that had been hitting Informatica’s volume limits. The data pipeline automation patterns available in Fabric also reduce manual pipeline maintenance overhead.
4. Simplified Data Architecture Replacing multiple Informatica products with a single Fabric platform removes the integration overhead that comes with managing separate ETL, quality, governance, and BI tools. OneLake’s unified storage means data does not need to move between tools. A Fabric data flow writes directly to the Lakehouse, and Power BI reads from it natively.
5. Built-In AI and Analytics Capabilities Fabric Data Agents let business teams query their own data in natural language without SQL or engineering involvement. Microsoft Copilot is embedded across Fabric workloads. AI-powered insights with Karl can be layered on top of Fabric’s semantic models to deliver business-facing analytics without custom development.
6. Improved Scalability and Governance Fabric’s capacity-based compute scales automatically with workload demand, removing the infrastructure management overhead of Informatica Server scaling. Microsoft Purview provides end-to-end lineage, role-based access policy management, and compliance coverage in one place, replacing the manual governance assembly that multi-tool Informatica environments require.
Common Migration Challenges and How to Overcome Them Every Informatica to Fabric migration runs into a predictable set of obstacles. Knowing what they are and how to handle them is what keeps the project on track.
1. Complex ETL Dependencies Large PowerCenter environments accumulate dependency chains that span dozens of mappings and workflows. A missed dependency means a broken downstream pipeline after cutover, which is the most common post-migration incident.
How to handle it:
Automate dependency discovery before planning starts; manual dependency mapping in large environments misses cross-team pipeline relationships Build the migration sequencing directly from the dependency map, so upstream pipelines are always validated before downstream ones convert Run FLIP’s FIRE module against the full PowerCenter repository to produce a complete dependency graph before a single conversion begins
2. Legacy Transformations Informatica PowerCenter includes proprietary transformation functions (custom expressions, plug-in transformations, and vendor-specific connectors) that have no direct equivalent in Fabric’s Data Factory or Dataflow Gen2.
How to handle it:
Catalog every proprietary function during discovery with a specific description of its behavior Assign Power Query M or Spark notebook equivalents to each flagged function before conversion starts Batch proprietary function handling into a dedicated engineering sprint rather than handling them ad hoc during conversion
3. Data Quality Validation Migrated pipelines can sometimes produce output that differs from the Informatica equivalent. The cause is often how Fabric processes certain transformations differently at scale, rather than a conversion error. Catching these differences in parallel validation is far less costly than discovering them post-cutover.
How to handle it:
Run parallel validation for a minimum of two full business cycles before decommissioning Informatica workflows Set automated row count and output comparison checks, flagging any discrepancy above defined thresholds Involve business data owners in sign-off alongside engineering; business owners catch logic gaps that technical validation misses
4. Performance Optimization Some pipelines that ran within acceptable time windows on Informatica can run slower in Fabric during initial migration, particularly if the target architecture was designed without Fabric’s distributed compute model in mind.
How to handle it:
Benchmark migrated pipeline performance against Informatica baselines during validation, before committing to cutover Apply Spark partition tuning and Delta table optimization to high-volume pipelines that fall below baseline performance Right-size the Fabric capacity tier based on actual observed compute patterns from the first two to four weeks of parallel running
5. Change Management and User Adoption The technical migration is one part of the project. Getting data engineering teams and business users productive in Fabric is the other. Teams that skip structured enablement end up with a governed Fabric environment that nobody uses confidently.
How to handle it:
Run Fabric training for data engineering teams during the parallel validation phase, so engineers are productive before Informatica is retired Deliver role-specific training for business users: Power BI consumption, natural language querying through Fabric Data Agents, and workspace navigation Establish a Fabric Center of Excellence post-migration to own ongoing governance, performance, and capability development
Alteryx to Microsoft Fabric Migration: The Enterprise Path to AI-Ready Data Learn how Alteryx to Microsoft Fabric Migration simplifies data integration, analytics, governance, and AI readiness on a unified platform.
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How to Choose the Right Migration Partner Choosing a migration partner is as consequential as choosing the target platform. Informatica environments carry years of business logic, custom transformations, and governance metadata. A partner that treats migration as a lift-and-shift exercise delivers a technically working system that misses the context that makes it valuable.
Four criteria that separate execution-ready partners from those that will cost you months:
1. Microsoft Fabric Expertise The partner should hold current Microsoft Fabric certifications and partnership status. Look for Microsoft Solutions Partner designation for Data and AI with Analytics Specialization, and specifically the Advanced Specialization for Data Warehouse Migration to Microsoft Azure. This credential signals direct experience with Fabric migration projects, beyond general Fabric deployments.
2. Migration Methodology A credible partner has a documented migration methodology covering assessment, dependency mapping, parallel validation, and phased cutover, and they can show you how it has been applied on comparable Informatica environments.
Generic agile delivery frameworks are different from migration methodologies. Ask to see the specific process for handling proprietary transformation functions and IICS vs. PowerCenter path differences.
3. Data Governance Experience The governance rebuild (Purview lineage, access policies, OneLake Catalog configuration) is where most migration projects either set up the new environment for long-term success or create a technical debt backlog that takes months to clear. A partner with dedicated data governance experience treats this as a first-class deliverable, not an afterthought.
4. Post-Migration Support Migration completion is the beginning of the optimization phase. Fabric capacity right-sizing, pipeline performance tuning, and user enablement all happen after the first pipelines go live. A partner that offers structured post-migration support (including optimization reviews and Center of Excellence setup) delivers more durable value than one that hands off at cutover.
How Kanerika Accelerates Informatica to Microsoft Fabric Migration Kanerika is a Microsoft Fabric Featured Partner, Microsoft Solutions Partner for Data and AI with Analytics Specialization, and holds the Microsoft Advanced Specialization for Data Warehouse Migration to Microsoft Azure. We have delivered data platform migrations across financial services, manufacturing, retail, and logistics, with direct product consulting history with Microsoft Fabric from its development and testing phases.
Our Informatica to Microsoft Fabric migration service is accelerated by FLIP, our IP-led DataOps platform, available on the Azure Marketplace and eligible for Azure Committed Spend (MACC). FLIP reduces manual migration engineering effort by 75% and compresses most enterprise timelines from 12 to 18 months down to weeks.
What FLIP automates across the migration lifecycle: Asset discovery: full PowerCenter repository scan producing a tiered inventory with dependency chains mapped and proprietary function gaps flagged before any conversion begins Pipeline conversion: PowerCenter mappings converted to Dataflow Gen2 or Data Factory pipelines, workflow orchestration recreated in Data Factory activity chains, scheduling migrated to Fabric triggers Parallel validation: automated parity testing comparing Fabric pipeline outputs against Informatica baselines across row counts, transformations, and scheduling behavior Governance setup: Purview lineage configuration, access policy definition, and OneLake Catalog setup delivered at deployment, not as a post-migration backfill
FLIP effort benchmarks across Informatica environment sizes: Scenario Manual Hours FLIP Hours Effort Reduction Simple migration (standard mappings) 720 hours 120 hours 83% Mid-scale (complex logic, parameterized) 2,400 hours 480 hours 80% Complex enterprise (proprietary functions, IICS) 3,365 hours 940 hours 72%
We hold ISO 27001, SOC II Type II, ISO 9001:2015, and CMMI Level 3 certifications, meeting the security and compliance requirements of regulated industries. FLIP qualifies for Azure Committed Spend, meaning eligible organizations fund the migration through existing Microsoft commitments rather than a separate budget.
For post-migration analytics, our Karl AI agent connects to the new Fabric environment and delivers natural language data insights to business teams, cutting time from question to answer without requiring SQL or engineering involvement.
A global consumer electronics organization ran production data operations on Informatica PowerCenter across multiple regions. Rising product and order volume was exposing the limits of the existing architecture.
Challenges Rising product and order volume slowed daily refresh cycles, holding back planning and sales reporting for regional teams Batch-heavy Informatica flows limited the frequency of data updates across time zones, slowing access to sales and stock data Inconsistent transformation rules across regions made it difficult to maintain a unified view of core business metrics
Solution Kanerika migrated high-volume Informatica flows to Microsoft Fabric using FLIP, keeping refresh cycle stability as data volumes continued to grow. Faster data flow paths in Fabric gave sales and supply chain teams more frequent access to changing business data. A shared transformation rule set was established across regions, reducing rework and giving all teams a consistent view of core metrics.
Results 60% improvement in data processing speed 42% reduction in infrastructure costs 76% faster time-to-insights for regional business teams Migration completed with zero disruption to ongoing production operations
Wrapping Up The decision to migrate from Informatica to Microsoft Fabric is a platform decision, one that goes beyond swapping one ETL tool for another. Organizations that treat it as a lift-and-shift exercise end up with Fabric pipelines that replicate the same multi-tool complexity they started with. The ones that get the most value treat it as an opportunity to redesign the data architecture: unified storage, end-to-end lineage, and AI-ready pipelines built from day one.
Kanerika’s combination of FLIP automation, Fabric expertise, and structured migration methodology means the project delivers a working, governed Fabric environment with every pipeline validated before Informatica is retired. Pre-migration assessment and governance planning from the start are what make that outcome consistent.
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FAQs 1. Why are organizations considering Informatica to Microsoft Fabric Migration? Many organizations are looking to simplify their data ecosystems by moving from fragmented data integration environments to a unified platform. Microsoft Fabric combines data integration, storage, analytics, governance, and AI capabilities within a single ecosystem. By migrating from Informatica, businesses can reduce architectural complexity, improve collaboration across teams, and accelerate access to insights while supporting future analytics and AI initiatives.
2. Can Microsoft Fabric replace Informatica? Microsoft Fabric can replace many of the data integration and orchestration capabilities organizations currently use Informatica for. Features such as Data Factory, Dataflows Gen2, OneLake, and Fabric Warehouse provide a modern foundation for data movement, transformation, and analytics. However, the migration approach depends on the complexity of existing workflows, data quality processes, and enterprise requirements.
3. What are the key benefits of Informatica to Microsoft Fabric Migration? Migrating to Microsoft Fabric helps organizations consolidate multiple tools into a single platform while improving scalability and reducing operational overhead. Businesses also benefit from unified data access through OneLake, integrated analytics, built-in governance, and support for AI-driven workloads. The result is a more streamlined data architecture that is easier to manage and optimize.
4. What challenges are common during Informatica to Microsoft Fabric Migration? Organizations often encounter challenges related to complex ETL workflows, custom transformations, data dependencies, and validation requirements. Existing Informatica assets may need to be redesigned or optimized to align with Fabric’s architecture. A structured migration strategy, thorough testing, and phased implementation approach can help minimize disruption and ensure a smooth transition.
5. How does Microsoft Fabric handle ETL and data integration? Microsoft Fabric provides several tools for data integration, including Data Factory, Data Pipelines, Dataflows Gen2, and Spark-based processing. These capabilities allow organizations to ingest, transform, orchestrate, and manage data across multiple sources. Fabric also supports both low-code and code-based development approaches, giving teams flexibility based on their technical requirements.
6. How long does an Informatica to Microsoft Fabric Migration typically take? The migration timeline varies depending on the number of workflows, data sources, integrations, and business processes involved. Smaller environments can often be migrated within a few weeks, while large enterprise implementations may require several months. A detailed assessment phase helps organizations understand scope, prioritize workloads, and develop a realistic migration roadmap.
7. How can organizations ensure data quality during migration? Data quality should be validated throughout the migration process rather than only after completion. Organizations typically compare source and target datasets, verify transformation logic, test business rules, and conduct user acceptance testing. Establishing clear validation frameworks helps ensure that data remains accurate, consistent, and reliable after the move to Microsoft Fabric.
8. How do you choose the right partner for Informatica to Microsoft Fabric Migration? The right migration partner should have expertise in both Informatica and Microsoft Fabric, along with experience managing enterprise-scale data modernization projects. Organizations should look for proven migration methodologies, governance expertise, data validation capabilities, and post-migration support services. A knowledgeable partner can significantly reduce migration risks while accelerating time to value.
