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SAP Data Migration: 12 Steps, Tools, and Paths to Fabric and Snowflake

September 21, 2026

TL;DR SAP data migration is the work of moving master, transactional, and historical data out of SAP systems into a new target, either SAP S/4HANA or a modern analytics platform. Most enterprises now run both tracks at once. The ERP track moves operational data into S/4HANA before mainstream maintenance for SAP Business Suite 7 core applications, including SAP ERP 6.0,

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Ontology vs Semantic Layer: Differences, Use Cases, and Why AI Needs Both

September 21, 2026

TL;DR A semantic layer defines how business metrics are calculated, so every report and query returns the same number. An ontology defines what business things are and how they relate, such as customers, orders, plants, and suppliers. The semantic layer answers “what is our revenue?” while the ontology answers “which suppliers affect this customer’s late orders?” AI agents need both,

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Data classification best practices guide by Kanerika, enterprise data governance leaders reviewing classification policy documents
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  • Gaurav Verma

Data Classification Best Practices: The Operating Playbook for 2026

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  • Gaurav Verma

10 Best Data Access Governance Tools for Modern Data Stacks in 2026

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  • Gaurav Verma

Databricks CREATE TABLE: 7 Ways to Build a Table and How to Pick

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  • Gaurav Verma

Staff Augmentation Process: 7 Steps From Skill Gap to Offboarding

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  • Gaurav Verma

Databricks SQL Warehouse: Types, Sizing, and Cost Optimization

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  • Gaurav Verma

AI Governance, Risk, and Compliance: The 2026 Operating Model

Our Informative Video Library

A business ontology on Microsoft Fabric gave a global pharmaceutical enterprise one governed definition for every KPI, shared across dashboards, business units and AI copilots.The problem was a familiar one. The same KPI meant different things in different dashboards. Leaders had plenty of data and very little confidence in the answers.What was going wrong:• KPI definitions and business rules were embedded inside individual Power BI dashboards, so the same metric returned inconsistent results depending on where you looked • Cross-domain questions took days of manual reconciliation while teams traced logic across reports and systems • AI and Copilot experiences could not deliver reliable answers, because there was no governed business layer behind themWhat Kanerika built:• A governed business ontology on Microsoft Fabric, acting as one trusted layer for KPI definitions, relationships and reporting logic across business users, Power BI reports and AI copilots • More than 85 KPI definitions and business rules centralized into that ontology, so every report and query runs on the same logic • More than 40 entities and relationships modeled across key business domains, making cross-functional analysis faster and better connected • The ontology linked to governed data in OneLake, cutting the dependency on dashboard-level logic and manual reconciliation • The same semantic layer extended to Fabric AI and Copilot, enabling trusted conversational analyticsThe result:• 70% reduction in investigation and reconciliation time • 100% consistent KPI logic across both reporting and Copilot-driven analyticsIf your teams spend more time reconciling metrics than acting on insights, it is time to govern KPIs at the business layer rather than inside individual dashboards.Talk to us about building a trusted ontology on Microsoft Fabric: https://kanerika.com/contact-us/🔔 Subscribe for more enterprise data and AI insights: https://www.youtube.com/@kanerikainc#MicrosoftFabric #BusinessOntology #SemanticLayer #DataGovernance #PowerBI #OneLake #Copilot #PharmaAnalytics 2:22Business Ontology on Microsoft Fabric: Global Pharma Case Study

Business Ontology on Microsoft Fabric: Global Pharma Case Study

Kanerika Inc. September 3rd

Order lead time analytics on Microsoft Fabric gave a global HVAC manufacturer its first reliable end-to-end view of the order journey, from lead through to delivery.The company builds thermal management and heat transfer solutions. Its order process ran across three disconnected SAP systems, so quotes, approvals and deliveries never came together in one record anyone could trust.What was going wrong:• Three disconnected SAP systems, with no single reliable record of the order journey • Lead time tracked manually across six stages, making it hard to see where orders were slowing down or getting stuck • Data pulled and reconciled every cycle, which delayed reporting and left teams working from mismatched numbersWhat Kanerika built:• A governed Microsoft Fabric pipeline bringing data from all three SAP systems into one structured foundation • A dedicated bridge connecting opportunities, quotes and orders, so the business could follow one continuous lead-to-delivery journey • A self-updating Power BI dashboard in place of manual data pulls, giving teams more than 50 standardized KPIs and one consistent view of performanceThe impact:• 60% drop in reporting effort • More than 35 source tables connected across the order life cycle • More than 50 governed KPIs supporting consistent order performance tracking • Full visibility of the order journey from lead to delivery, with delays spotted earlier and decisions made from one trusted viewWant the same visibility across your order life cycle? Talk to us about Microsoft Fabric analytics: https://kanerika.com/contact-us/🔔 Subscribe for more enterprise data and AI transformation stories: https://www.youtube.com/@kanerikainc#MicrosoftFabric #SAP #OrderManagement #ManufacturingAnalytics #PowerBI #LeadTime #SupplyChain #DataEngineering 2:04Microsoft Fabric Case Study: Order Lead Time Analytics for Manufacturing

Microsoft Fabric Case Study: Order Lead Time Analytics for Manufacturing

Kanerika Inc. September 1st

How can global enterprises scale invoice processing without increasing accounts payable costs?This invoice processing automation case study shows how Kanerika helped a global travel management company automate thousands of monthly invoices across hotels, transfers, and car rentals.The company was facing high invoice volumes, manual reconciliation, delayed approvals, fragmented document tracking, and limited visibility into invoice status and audit readiness.You’ll learn:- How automated invoice intake captures invoices directly from vendor emails - How OCR and AI extract key invoice information with less manual intervention - How rule-based validation checks invoice data before downstream processing - How automated workflows improve invoice reconciliation and approvals - How audit trails provide complete invoice lifecycle traceability - How the solution supports higher invoice volumes without increasing costs - How the company achieved an 80% reduction in manual invoice processing - How invoice lifecycle traceability reached 100% - How invoice handling capacity increased 4x without added costBy automating invoice processing from intake through downstream financial systems, the company improved operational efficiency, strengthened controls, and scaled its accounts payable operations.Ready to improve your accounts payable process? Talk to Kanerika about intelligent invoice automation: https://kanerika.com/contact-us/Subscribe for more case studies on AI automation, intelligent document processing, accounts payable automation, and enterprise data solutions.🔔 Subscribe for more enterprise data and AI insights: https://www.youtube.com/@kanerikainc#InvoiceAutomation #AccountsPayable #AIAutomation #InvoiceProcessing #IntelligentAutomation #CaseStudy #TravelIndustry #Kanerika 1:47Invoice Processing Automation Case Study: 80% Less Manual Work

Invoice Processing Automation Case Study: 80% Less Manual Work

Kanerika Inc. August 20th

How do you scale product engineering quickly while navigating private equity ownership, a major merger, and rising modernization demands?This case study shows how Kanerika helped a global logistics spend management and freight audit leader expand engineering capacity while maintaining platform stability and customer commitments through an M&A transition.Key takeaways: - How the client faced modernization demands under new private equity ownership - The engineering challenges created by a merger with a similarly sized competitor - Why limited engineering capacity was slowing modernization initiatives - How Kanerika built a 35-member engineering team within the first 90 days - How the team owned product engineering delivery and platform stability from day one - How two acquired businesses were integrated without disrupting customer commitments - How the engineering team scaled from 35 to 140 members over time - How a flexible cost model helped increase capacity without adding long-term fixed overhead - How the engagement delivered 40% lower development costs - How the client achieved 25% faster time to market - How platform reliability was maintained through critical M&A transitionsKanerika provides dedicated product engineering teams that help enterprises scale engineering capacity, accelerate modernization, and maintain delivery momentum through complex business transitions.If your engineering team is stretched thin, let's talk: https://kanerika.com/contact-us/Subscribe for more enterprise technology case studies and insights.🔔 Subscribe for more enterprise data and AI insights: https://www.youtube.com/@kanerikainc#ProductEngineering #DedicatedEngineeringTeam #EngineeringTeam #FreightAudit #MergerIntegration #MergersAndAcquisitions #DigitalTransformation #CaseStudy #Kanerika 1:39Dedicated Engineering Team Case Study: Scaling Through a PE-Backed Merger

Dedicated Engineering Team Case Study: Scaling Through a PE-Backed Merger

Kanerika Inc. August 6th

Slow reporting was costing FoodPharma speed, agility, and growth. Partnering with Kanerika, this leading US contract manufacturer of functional food products modernized its data foundation on Microsoft Fabric — cutting reporting time from 2 business days to about 90 minutes and saving the BI team nearly 15 hours every week. The results were significant enough to be featured in an official Microsoft customer success story.🔔 Subscribe: https://www.youtube.com/@kanerikainc📞 Talk to Kanerika about modernizing enterprise reporting on Microsoft Fabric: https://kanerika.com/contact-us/#MicrosoftFabric #DataModernization #BusinessIntelligence #DataAnalytics #CustomerSuccessStory 1:02Microsoft Fabric Case Study: FoodPharma Cuts Reporting to 90 Minutes

Microsoft Fabric Case Study: FoodPharma Cuts Reporting to 90 Minutes

Kanerika Inc. June 15th

A global leader in interconnect solutions was stuck on legacy QlikView — heavy manual scripting, days-old distributor data, and multi-day waits for every report change. See how Kanerika migrated them to real-time Power BI analytics on Microsoft Fabric, cutting manual reporting maintenance by 70%, speeding up data refresh cycles by 80%, and lowering infrastructure and licensing costs by 40%.This migration and the new real-time pipelines were built on FLIP, Kanerika's AI-powered DataOps platform: https://kanerika.com/product/flip/If your analytics stack is holding your team back, let's talk: https://kanerika.com/contact-us/🔔 Subscribe: https://www.youtube.com/@kanerikainc#PowerBI #QlikView #MicrosoftFabric #DataAnalytics #DataOps 1:41QlikView to Power BI Migration: 70% Less Manual Reporting

QlikView to Power BI Migration: 70% Less Manual Reporting

Kanerika Inc. June 3rd

A leading payment technology company was managing brand guidelines across dozens of partners, agencies, and markets — scattered systems, manual searches, and every routine question routed to in-house experts. Kanerika built a conversational AI assistant with a unified brand knowledge base for instant, self-service answers. The result: brand compliance up 35%, approval turnaround down 60%, and manual effort per query cut by 70%.🔔 Subscribe: https://www.youtube.com/@kanerikainc?sub_confirmation=1#ConversationalAI #BrandCompliance #ApprovalWorkflow #AIAutomation #EnterpriseAI 1:47Enhancing Brand Compliance and Approval Workflows with Conversational AI

Enhancing Brand Compliance and Approval Workflows with Conversational AI

Kanerika Inc. May 18th

A regulatory compliance firm serving financial institutions was drowning in fragmented workflows, manual controls mapping, and zero visibility across jurisdictions. See how Kanerika built an AI-powered, centralized compliance platform with AI-driven regulatory tracking, automated requirement mapping, and real-time dashboards — delivering 40% faster regulatory response, 60% less manual compliance work, and 5x better audit traceability.🔔 Subscribe: https://www.youtube.com/@kanerikainc?sub_confirmation=1#RegTech #ComplianceAutomation #AIinFinance #FinancialCompliance #DataAI 1:37Building an AI-Powered Compliance Platform That Scales Across Jurisdictions

Building an AI-Powered Compliance Platform That Scales Across Jurisdictions

Kanerika Inc. May 13th

See how Kanerika helped Fortegra, a leading insurance provider managing thousands of policies across multiple banking partners, fix slow, error-prone premium reconciliation. Using an intelligent automation framework built on UiPath and Python, manual bank statement downloads, unstructured data extraction, and manual premium updates were replaced with automated email monitoring, Python-based extraction and validation, and automated reconciliation — delivering an 85% reduction in reconciliation time, 99% accuracy in data extraction, and 100% elimination of manual updates.Facing similar bottlenecks in your finance operations? Let's talk: https://kanerika.com/contact-us/🔔 Subscribe: https://www.youtube.com/@kanerikainc?sub_confirmation=1#PremiumReconciliation #InsuranceAutomation #IntelligentAutomation #RPA #FortegraCaseStudy 1:39Data Accuracy in Premium Reconciliation with Automation for Fortegra | Kanerika's case study

Data Accuracy in Premium Reconciliation with Automation for Fortegra | Kanerika's case study

Kanerika Inc. April 14th

Microsoft Fabric migration webinar

Migrate to Microsoft Fabric 5X Faster with FLIP

Limited seats available! Register Now

Microsoft Fabric migration webinar

Migrate to Microsoft Fabric 5X Faster with FLIP

Limited seats available! Register Now

$1.2M

Average Annual Cost Savings in Logistics Operations

50%

Faster Time-to-market for Fintech and Healthtech products

28%

Boost in Customer Retention in Retail and E-commerce

30%

Reduction in Project Timelines for Pharmaceutical Firms
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