Data teams at large enterprises are spending months trying to get simple reports because their Oracle systems can’t keep up with modern analytics demands. A 2024 Forrester Consulting study found that companies using Microsoft Fabric achieved 379% ROI over three years, with data engineering productivity jumping by 25%. The research also revealed that organizations reduced time spent searching for data by 90% after moving to the platform.
The challenge is clear. Oracle databases still power critical operations at thousands of companies, but they’re showing their age. High licensing costs, complex maintenance, and limited integration with modern tools are pushing more organizations to migrate from Oracle to Microsoft Fabric. This shift isn’t just about swapping platforms. It’s about accessing unified data storage, real-time analytics , and AI capabilities without the usual infrastructure headaches. For companies dealing with slow insights, rising costs, and disconnected data systems, the migration offers a path to faster decision-making and better business outcomes.
TLDR Enterprises are moving from Oracle databases to Microsoft Fabric to cut costs, improve performance, and access real-time analytics. Oracle’s high licensing fees and complex scalability create barriers, while Fabric offers unified analytics with pay-as-you-go pricing. The migration involves challenges like PL/SQL conversion, schema mapping, and ETL redesign, but tools like Azure Data Factory and SSMA simplify the process. Companies achieve 379% ROI with faster insights and reduced maintenance. Kanerika’s FLIP accelerators automate migrations , reducing timelines from months to weeks while ensuring accuracy and compliance throughout the transition.
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Why Enterprises Are Moving Away From Oracle Databases 1. High Licensing Costs Oracle’s pricing model puts serious pressure on IT budgets. Companies pay per processor or per user, which adds up fast as your business grows. Many organizations find themselves spending hundreds of thousands just on licenses before counting maintenance fees.
Annual support costs typically run at 22% of the original license price Scaling up requires purchasing additional licenses for new processors or users Hidden costs appear during audits when Oracle identifies unlicensed usage 2. Complex Scalability Requirements Scaling Oracle databases takes significant planning and hardware investment. When your data grows, you can’t just flip a switch. You need to buy more servers, configure them properly, and often bring in specialized consultants to make it work.
Manual scaling processes require weeks or months of planning Hardware purchases involve large upfront capital expenses Database administrators need deep expertise to manage performance at scale 3. Vendor Lock-In Problems Once you build on Oracle, switching becomes extremely difficult. Your team writes stored procedures in PL/SQL, which only works with Oracle systems. Applications get tightly coupled to Oracle-specific features. Moving to another platform means rewriting years of business logic.
PL/SQL code doesn’t transfer to other database systems Custom integrations rely on Oracle proprietary tools Migration projects can take 12 to 24 months without automation 4. Limited Real-Time Analytics Oracle was built for transactional workloads, not modern analytics needs. Getting real-time insights requires complex workarounds or buying additional Oracle products. Batch processing creates delays that don’t work when businesses need instant answers.
Traditional batch windows mean waiting hours or days for updated reports Real-time streaming requires separate Oracle products at extra cost Integration with modern BI tools often needs middleware layers Connecting Oracle to cloud services and modern analytics platforms takes extra work. The system wasn’t designed for today’s data ecosystem . Teams struggle to bring in data from APIs, streaming sources, or cloud applications without building custom connectors.
Limited native support for modern data formats like JSON or Parquet Cloud integration requires additional middleware or custom development AI and machine learning tools need complex data pipelines to access Oracle data 6. Heavy Maintenance Overhead Keeping Oracle running smoothly demands constant attention from skilled administrators. Updates, patches, and performance tuning eat up IT resources. Companies find themselves spending more time maintaining the database than using it to drive business value.
Performance tuning needs ongoing monitoring and adjustment Routine maintenance requires specialized database administrator skills Version upgrades involve testing every stored procedure and application Cognos vs Power BI: A Complete Comparison and Migration Roadmap A comprehensive guide comparing Cognos and Power BI, highlighting key differences, benefits, and a step-by-step migration roadmap for enterprises looking to modernize their analytics.
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Oracle to Microsoft Fabric Migration Challenges 1. Schema and Data Type Mapping Oracle and Microsoft Fabric use different data structures and types. What works perfectly in Oracle might not have a direct equivalent in Fabric. This means you need to carefully remap every table, column, and data type to prevent data loss or corruption.
Oracle data types like NUMBER or VARCHAR2 need conversion to Fabric equivalents Date and timestamp formats differ between the two platforms Precision and scale mismatches can cause data truncation or errors 2. PL/SQL Code Conversion Oracle’s PL/SQL stored procedures don’t run on Microsoft Fabric . Every piece of business logic written in PL/SQL needs to be rewritten. Organizations with decades of accumulated code face months of conversion work.
Thousands of lines of custom PL/SQL require manual review and rewriting Cursors and loops need refactoring into set-based operations for better performance Triggers and packages must be recreated using Fabric-compatible alternatives like Spark or SQL 3. ETL Pipeline Redesign Existing Oracle ETL processes use tools and utilities that don’t work with Fabric. Teams using Oracle SQL*Loader, Data Pump, or custom scripts must rebuild everything. This means learning new tools and redesigning workflows from scratch.
Legacy tools like Informatica or custom Oracle utilities need replacement Data transformation logic must move to Azure Data Factory or Synapse pipelinesTesting rebuilt pipelines takes significant time to ensure accuracy 4. Large Historical Data Migration Moving years or decades of data takes serious planning. Network bandwidth limitations slow down transfers. Companies worry about downtime during the move, especially when dealing with terabytes of information.
Data transfer speeds depend on network capacity and can take weeks Business operations may need pausing during critical migration windows Staging large datasets requires temporary storage in Azure Blob or OneLake 5. Performance Optimization Differences What runs fast in Oracle might crawl in Fabric without proper tuning. The two platforms optimize queries differently. Indexes, execution plans, and performance patterns all change, requiring fresh expertise.
Oracle optimizer hints don’t translate to Fabric’s distributed compute model Indexing strategies need complete redesign for lakehouse architecture Query patterns optimized for row-by-row processing must shift to set-based operations 6. Dashboard and Report Recreation Power BI reports need rebuilding when moving from Oracle-connected dashboards . Business users rely on specific visualizations and metrics. Recreating everything while maintaining the same functionality takes careful attention.
Existing reports built on Oracle connections require manual recreation in Power BI Semantic models need rebuilding to work with Fabric data sources KPIs and business calculations must be validated for accuracy after migration 7. Security and Access Control Translation Oracle’s security model works differently than Microsoft Fabric. Row-level security, role-based access, and audit trails all need recreation. Compliance requirements make this especially tricky for regulated industries.
Oracle roles and privileges don’t map directly to Fabric security groups Audit logs and compliance tracking require new configuration in Purview Data encryption methods differ between on-premises Oracle and cloud-based Fabric8. Team Training and Skill Gaps IT teams know Oracle inside and out but face a learning curve with Fabric. The shift from managing physical databases to working with cloud-native platforms requires new thinking. People who’ve used Oracle tools for years must learn completely different interfaces.
Database administrators need training on cloud concepts and Fabric workloads Developers must learn Spark, Python, or newer SQL variants instead of PL/SQL Business analysts require time to adapt to Power BI and Fabric’s analytics tools9. Application Dependency Management Business applications often connect tightly to Oracle databases. APIs, connection strings, and database calls all need updating. Testing every application to ensure it works with Fabric takes considerable effort.
Connection strings and database drivers require updates across all applications Third-party software may lack native Fabric connectors Legacy applications might need code changes to work with new data access patterns 10. Downtime and Business Continuity Risks Migration creates risk of service interruptions. Critical business processes depend on database availability. Planning cutover windows without disrupting operations becomes a major logistical challenge, especially for global organizations.
Production systems may need temporary downtime during final data synchronization Rollback plans must exist in case migration issues arise Parallel systems running during transition double infrastructure costs temporarily Key Differences: Oracle Systems vs Microsoft Fabric Lakehouses Feature / Aspect Oracle Systems Microsoft Fabric Lakehouses Architecture & Storage Traditional relational databases; structured storage Combines data lakes and warehouses; supports structured & unstructured data Scalability & Performance Manual scaling; separate infra for large datasets Elastic compute & storage; auto-scaling for real-time analytics Analytics & AI Reporting/analytics built-in; advanced AI via third-party AI-ready; integrated with Power BI, Azure, and Microsoft 365 for ML and automation Data Integration Often requires ETL pipelines for disconnected systems Unified data platform; supports batch, streaming, and real-time ingestion Cost & Maintenance Higher upfront and ongoing maintenance costs Cloud-native; pay-as-you-go; reduced management overhead Flexibility Limited for unstructured or semi-structured data Handles structured, semi-structured, and unstructured data seamlessly Cloud Readiness On-premises focused; cloud migration can be complex Cloud-first design; hybrid and multi-cloud support
Oracle to Microsoft Fabric Migration Tools and Technologies 1. Azure Data Factory (ADF) Azure Data Factory handles the heavy lifting of moving data from Oracle to Fabric. It offers over 150 built-in connectors that let you pull data from Oracle and push it into OneLake or Fabric warehouses. The tool supports parallel loading, which speeds up migration for large datasets.
The visual interface makes it easier to design data flows without writing tons of code. You can map transformations, schedule pipeline runs, and monitor everything from one dashboard. This works well whether your Oracle database sits on-premises or in the cloud.
2. Microsoft Fabric Data Factory This is the native version of Data Factory built directly into Microsoft Fabric . It provides the same pipeline capabilities but integrates more tightly with other Fabric workloads. You can move data between Oracle and Fabric without leaving the platform.
Fabric Data Factory includes Dataflow Gen2, which offers a low-code way to transform data as it moves. The tool handles scheduling, error handling, and monitoring automatically. For teams already working in Fabric, this becomes the natural choice over standalone Azure Data Factory.
3. SQL Server Migration Assistant (SSMA) for Oracle SSMA automates the conversion of Oracle database objects to formats that work with Microsoft platforms. The tool analyzes your Oracle schemas, stored procedures, and functions, then converts them automatically. It also helps identify potential compatibility issues before you start the actual migration.
After conversion, SSMA can move your data and run tests to verify everything transferred correctly. The tool is free from Microsoft and handles schema mapping, SQL statement conversion, and initial data migration . However, it works better for smaller databases since it doesn’t support large-scale parallel operations.
4. Azure Database Migration Service (DMS) DMS specializes in moving databases with minimal downtime. It supports both online and offline migration modes depending on your business needs. The service continuously syncs data from Oracle to Fabric during online migrations , letting you cut over when ready.
The tool automatically checks for compatibility issues and provides recommendations. It monitors the migration process and alerts you to any problems. For organizations that can’t afford long maintenance windows, DMS helps keep systems running while data transfers happen in the background.
5. Synapse Notebooks Synapse notebooks provide a code-first approach for complex data transformations . They run on Apache Spark, which handles large-scale data processing efficiently. Teams use notebooks to convert PL/SQL logic into Python or Spark code that performs better in Fabric’s distributed environment.
The collaborative environment lets multiple developers work together on transformation logic. You can test code interactively, visualize results, and version control everything. Notebooks work particularly well for custom business rules that don’t fit standard ETL patterns.
6. Microsoft Purview Purview manages data governance throughout the migration process. It tracks where data comes from, how it transforms, and where it goes. This lineage tracking becomes critical for compliance and troubleshooting after migration.
The tool catalogs all your data assets, making them searchable across the organization. Purview also enforces security policies, sensitivity labels, and access controls. For regulated industries, having this governance layer built in from the start saves headaches later.
7. Power BI Desktop Power BI Desktop handles the migration of reports and dashboards from Oracle-connected systems. You rebuild semantic models to connect with Fabric instead of Oracle. The tool offers Direct Lake mode, which queries data directly from OneLake without moving it.
Teams use Power BI to recreate visualizations, validate business metrics, and test performance. The desktop version lets you develop locally before publishing to the Fabric workspace. This approach ensures reports work correctly before end users see them.
8. On-Premises Data Gateway The gateway creates a secure bridge between your on-premises Oracle database and cloud-based Fabric. It handles authentication and data transfer without exposing your internal systems to the internet. Organizations with security requirements rely on this for safe connectivity.
You install the gateway on a server in your network that can reach both Oracle and Azure. It supports scheduled refreshes and real-time queries. The gateway becomes essential when you need to keep Oracle running during a phased migration.
Oracle to Microsoft Fabric Migration Step-by-Step Process 1. Discovery and Assessment Start by documenting everything in your current Oracle environment. You need to know what data you have, how it flows, and what depends on it. This phase typically takes two to four weeks depending on the size of your database. Teams inventory all schemas, tables, stored procedures, and ETL jobs during this time.
Create a complete list of Oracle databases, instances, and versions currently running Map out dependencies between applications, reports, and database objects Identify data volumes, growth rates, and performance baselines for capacity planning 2. Environment Setup Once you understand what needs moving, you set up your Microsoft Fabric workspace . This involves provisioning resources in Azure and configuring security policies. You also establish the connection between your Oracle system and Fabric. Most organizations complete this phase in one to two weeks.
Provision Fabric capacity units and create workspace for your migration project Configure OneLake storage structure and set up security groups with proper access controls Install on-premises data gateway if your Oracle database runs in your own data center 3. Schema Migration Now you convert Oracle database schemas into formats that work with Fabric. Every table structure, relationship, and constraint needs careful mapping. Data types that exist in Oracle might need different equivalents in Fabric. This phase usually takes two to four weeks depending on schema complexity.
Use SSMA or similar tools to convert Oracle table definitions to Fabric-compatible formats Map Oracle data types to appropriate Fabric equivalents while preserving precision and scale Recreate foreign keys, indexes, and constraints in the Fabric lakehouse or warehouse 4. Data Movement Moving the actual data requires extracting it from Oracle, transforming formats if needed, and loading into Fabric. Large datasets get staged in Azure Blob Storage first to manage the transfer efficiently. The timeline here varies widely based on how much data you have. Terabytes of historical data can take weeks to transfer completely.
Extract data from Oracle using Azure Data Factory connectors or Oracle export utilitiesStage extracted data in Azure Blob Storage with compression to reduce transfer time Load data into OneLake using incremental patterns to avoid overwhelming network bandwidth 5. ETL Pipeline Recreation Your existing ETL workflows need complete rebuilding for Fabric. PL/SQL stored procedures get converted to Spark jobs or SQL scripts. Oracle-specific tools like SQL*Loader get replaced with Fabric Data Factory pipelines . This often becomes the longest phase, taking four to eight weeks for complex environments.
Analyze existing Oracle ETL logic and document business rules embedded in PL/SQL code Build new pipelines in Fabric Data Factory using visual designers or notebook-based approaches Configure scheduling, error handling, and monitoring for all new data workflows 6. Analytics and BI Migration Reports and dashboards that connected to Oracle need recreation in Power BI. You rebuild semantic models to point at Fabric data sources instead of Oracle. Business users should see the same metrics and visualizations they had before. This phase typically takes two to four weeks.
7. Testing and Validation Before switching over, you need thorough testing to catch any issues. Data gets reconciled between Oracle and Fabric to ensure nothing was lost or corrupted. Performance tests verify that queries run fast enough. Users test their workflows to confirm everything works as expected. Plan for two to three weeks of intensive testing.
Run data reconciliation queries to verify row counts and totals match between systems Execute performance benchmarks comparing query speeds against Oracle baselines Conduct user acceptance testing with business teams to validate reports and workflows 8. Cutover and Go-Live This is when you actually switch from Oracle to Fabric for production workloads. You perform a final data sync , then redirect applications to connect to Fabric instead of Oracle. The cutover window is usually planned for a weekend or low-activity period. Most organizations complete this in one to two weeks including final preparations.
Execute final incremental data sync to capture any changes made during testing phase Update application connection strings and database drivers to point at Fabric endpoints Monitor system performance closely during first few days with support team on standby 9. Optimization and Monitoring After going live, the work continues with tuning and improvements. You monitor how Fabric performs under real workloads and adjust capacity as needed. Query patterns get optimized, costs get reviewed, and users provide feedback. This phase runs ongoing as you learn how to get the most from your new platform.
Track pipeline execution times and query performance to identify optimization opportunities Review capacity consumption and adjust compute resources to balance performance with costs Gather user feedback on report performance and analytics capabilities for continuous improvement 12 Key Benefits of Oracle to Microsoft Fabric Migration 1. Significant Cost Reduction Oracle’s licensing model charges per processor or per user, which adds up quickly. Microsoft Fabric uses pay-as-you-go pricing where you only pay for what you actually use. Companies typically see 40% to 60% reduction in total data platform costs after switching.
You also eliminate expensive hardware purchases and data center costs. The unified platform removes the need for multiple separate tools, cutting down on software licensing fees across your stack.
2. Improved Performance and Scalability Fabric scales automatically based on your workload without manual intervention. When query demands spike, the system adds compute resources on its own. When things slow down, it scales back to save money. This elastic approach handles growth much better than Oracle’s manual scaling.
Query performance improves through distributed processing and optimized storage formats. Direct Lake mode lets you query data without moving it, which speeds up analytics significantly compared to traditional data warehouse approaches.
3. Unified Analytics Platform Instead of juggling separate tools for data engineering, warehousing, and business intelligence , everything happens in one place. Teams collaborate more easily when they share the same workspace. Data engineers , analysts, and scientists all work together without switching between different systems.
This reduces the complexity of managing multiple platforms. You get data ingestion , transformation, storage, analytics, and visualization all integrated. The single interface cuts down on training time and operational overhead.
4. Real-Time Analytics Capabilities Oracle struggles with real-time insights because it was designed for batch processing. Fabric handles streaming data natively, giving you instant visibility into what’s happening right now. Businesses can react to events as they occur instead of waiting for overnight batch jobs to complete.
The platform processes events, logs, and streaming data continuously. You can build dashboards that update in real time without complex custom coding. This becomes especially valuable for fraud detection , inventory management, and customer behavior tracking.
5. Built-In AI and Machine Learning Fabric includes AI capabilities without requiring separate products or licenses. Copilot lets users ask questions in plain English and get answers instantly. You don’t need to know SQL or complex query languages to explore data.
Azure Machine Learning integrates directly for building predictive models. Data scientists can train, deploy, and monitor models all within the same platform. This makes advanced analytics accessible to more people in your organization.
6. Enhanced Security and Compliance Microsoft Fabric runs on Azure’s enterprise security framework with built-in encryption and access controls. Microsoft Purview handles governance automatically, tracking data lineage and enforcing policies. You get compliance certifications for major regulations without additional configuration.
Role-based access control and row-level security protect sensitive information. Multi-factor authentication and conditional access policies add extra layers of protection. For regulated industries, having security built in from the start simplifies audits.
7. Faster Time to Insights The unified platform eliminates time wasted moving data between different tools. Analysts can go from raw data to finished dashboards without switching systems. What used to take days or weeks now happens in hours.
Organizations report 90% reduction in time spent searching for data. OneLake acts as a single source of truth, making information easier to find and use. Teams spend more time analyzing and less time preparing data.
8. Reduced Vendor Lock-In Oracle’s proprietary technology makes switching platforms extremely difficult. Fabric uses open standards and supports multiple data formats. You can bring data from anywhere and use it alongside your existing systems without being forced into one vendor’s ecosystem.
The platform works with various cloud providers and on-premises systems. You maintain flexibility to change strategies as your business needs evolve. This freedom reduces long-term risk and gives you more negotiating power.
9. Lower Administrative Overhead Oracle databases need constant attention from skilled administrators for patching, tuning, and maintenance. Fabric handles most of these tasks automatically. Updates happen in the background without disrupting your work.
The serverless architecture means you don’t manage infrastructure. Auto-scaling, auto-pause, and automatic optimization reduce the workload on IT teams. Staff can focus on delivering business value instead of keeping systems running.
10. Better Collaboration Across Teams Fabric breaks down silos between technical and business users. Everyone works from the same data in OneLake, eliminating version control issues. Business analysts can build their own reports without waiting for IT support.
Integration with Microsoft Teams and Office 365 brings analytics into daily workflows. People can share insights, discuss findings, and make decisions without switching contexts. This collaborative environment speeds up decision making across the organization.
11. Simplified Data Integration Fabric includes over 150 connectors for different data sources. You can pull in data from databases, applications, APIs, and cloud services without custom coding. The platform handles both batch and streaming integration patterns natively.
OneLake provides a single place to store everything regardless of format. Structured databases, unstructured files, and streaming data all live together. This eliminates the complexity of managing separate storage systems for different data types.
12. Future-Ready Architecture Cloud-native design means you benefit from continuous improvements without major upgrades. Microsoft adds new features regularly that become available automatically. Your platform evolves without expensive migration projects every few years.
The architecture supports emerging technologies like generative AI and advanced analytics . As new capabilities develop, they integrate into your existing environment. This keeps your data platform current without constant reinvestment.
Best Practices for Oracle to Microsoft Fabric Migration 1. Start With a Pilot Project Don’t try to migrate everything at once. Pick a small, non-critical dataset or application to test your migration approach. This lets you learn what works and what doesn’t without risking important business operations. Use the lessons from this pilot to refine your process before tackling larger, more complex migrations.
Choose a dataset with moderate complexity that represents your overall environment Test the complete migration workflow from extraction through reporting to identify gaps Document issues encountered and solutions found for reference during full-scale migration 2. Document Everything Thoroughly Keep detailed records of every decision, configuration change, and customization you make. Future team members need to understand why things were built a certain way. Good documentation also helps troubleshoot issues faster when problems arise months after going live.
Create architecture diagrams showing data flows between Oracle and Fabric components Record all schema mappings, data type conversions, and transformation logic in shared documents Maintain a decision log explaining why you chose specific approaches over alternatives 3. Establish Data Quality Checkpoints Set up validation rules at each stage of the migration process. Catching data quality issues early prevents them from multiplying downstream. Regular checks ensure that what leaves Oracle matches what arrives in Fabric without corruption or loss.
Run row count comparisons after each data load to verify completeness Compare aggregate calculations like sums and averages between source and target systems Implement automated data profiling to catch anomalies before they reach production 4. Plan Detailed Rollback Procedures Things can go wrong even with careful planning. Having a clear rollback plan lets you reverse course quickly if critical issues appear. Know exactly how to switch back to Oracle if the Fabric environment doesn’t perform as expected during cutover.
Keep Oracle systems fully operational and synchronized until Fabric proves stable Document step-by-step procedures for redirecting applications back to Oracle if needed Test the rollback process during non-production migrations to verify it actually works 5. Involve Business Users From the Start Technical teams often focus on moving data while forgetting about the people who actually use it. Bring business stakeholders into planning conversations early. They know which reports matter most and can help prioritize what gets migrated first.
Schedule regular check-ins with department heads to review migration progress and priorities Let business users test migrated reports before final cutover to catch functional issues Create feedback channels where users can report problems without going through multiple layers 6. Implement Comprehensive Monitoring Set up monitoring tools before migration begins, not after problems occur. Track pipeline execution times, data freshness, query performance, and system errors. Early warning alerts help you spot trends before they become serious problems.
Configure alerts for failed pipeline runs, data quality violations, and performance degradation Build dashboards showing key metrics like data lag, processing times, and error rates Review monitoring data weekly during migration to identify patterns requiring attention
7. Use Version Control for All Code Treat your migration code the same way developers treat application code. Store pipeline definitions, transformation scripts, and configuration files in version control systems like Git. This provides a safety net when changes break things and you need to go back to a working version.
Commit all Fabric notebooks, pipeline definitions, and Power BI semantic models to repositories Require code reviews before deploying changes to production environments Tag releases with version numbers to track which code runs in each environment 8. Automate Testing Wherever Possible Manual testing catches some issues but misses others due to human fatigue and oversight. Automated tests run consistently every time without getting tired or distracted. Build test suites that verify data accuracy , transformation logic, and report calculations automatically.
Create automated SQL scripts that compare key metrics between Oracle and Fabric Build unit tests for custom transformation logic in notebooks and pipelines Set up automated regression testing to ensure new changes don’t break existing functionality 9. Schedule Migrations During Off-Peak Hours Moving data and switching systems impacts performance. Do heavy migration work when fewer people use the system. Weekend or overnight windows give you time to handle unexpected issues without affecting business operations.
Identify low-traffic periods by analyzing historical usage patterns and query volumes Communicate maintenance windows clearly to all affected users well in advance Have technical staff available during cutover periods to address problems immediately 10. Create Tiered Migration Priorities Not all data carries the same importance. Separate your migration into tiers based on business criticality and complexity. Move simpler, less critical workloads first to build confidence and expertise before tackling mission-critical systems.
Classify datasets as high, medium, or low priority based on business impact Start with read-only reporting databases before touching transactional systems Save the most complex ETL pipelines and critical applications for later phases after gaining experience 11. Build a Knowledge Transfer Program The team that performs the migration won’t always be around to support it. Create training materials and sessions that spread knowledge across your organization. This prevents situations where only one person understands how something works.
Record video walkthroughs showing how to perform common Fabric administration tasks Run hands-on workshops where team members practice building pipelines and troubleshooting issues Create a central wiki with answers to frequently asked questions and common problems 12. Set Realistic Timeline Expectations Migrations almost always take longer than initial estimates. Build buffer time into your schedule for unexpected complications. Rushing through migration leads to mistakes that cause bigger problems later. It’s better to go slower and get things right the first time.
Communicate delays promptly to stakeholders rather than promising unrealistic completion dates Add 20% to 30% contingency time to account for unforeseen technical challenges Break the timeline into smaller milestones that you can track and adjust as needed
In this real client story, a large enterprise needed to move its old data processes off an Informatica-centric setup and onto Microsoft Fabric . The goal was to stop waiting hours for reports, improve performance across analytics, and get ready for future growth with a modern data platform .
About the Client The client was an established company with heavy use of legacy Informatica ETL tools for data movement and reporting. The team relied on batch processes that ran at fixed times and struggled with slow queries, hard-to-maintain pipelines, and limited real-time insight. As the business scaled, these limits became harder to ignore and decision makers wanted faster access to insights without disruption.
Key Challenges The existing Informatica workflows were old, complex, and slow to update. Reports and dashboards took too long to refresh, blocking timely decisions. There was no single platform for analytics, so data teams spent too much time moving data instead of analyzing it. The migration had to happen without stopping business operations or losing data quality. Solution and Approach The project team used Kanerika’s automated migration process to convert Informatica mappings and workflows into Microsoft Fabric data pipelines and ingestion jobs. This included:
Automated scanning of Informatica assets and workflow logic. Conversion of ETL mappings into Fabric-ready data flows with preserved business logic. Deployment into Microsoft Fabric workspaces with minimal manual work. Business Outcomes As a result of the migration, the client saw a big improvement in report speed and data freshness. Slow nightly batches were replaced with pipelines that finished faster and delivered data sooner. The team reported fewer data delays, better performance for analytics, and a simpler architecture that cut down maintenance effort.
Overall, the move helped the business make decisions quicker, reduced pressure on IT, and set the foundation for future growth on Microsoft Fabric.
Accelerate Your Microsoft Fabric Migration With Kanerika’s FLIP Kanerika helps organizations move from legacy data platforms to Microsoft Fabric faster and with fewer risks. Our proprietary FLIP migration accelerators automate the complex process of transitioning from expensive systems like Informatica, SQL Server, and Tableau to modern platforms like Power BI and Microsoft Fabric.
The traditional manual migration approach takes months and requires constant intervention. FLIP changes this by automating schema conversion, data validation , and pipeline recreation. What normally takes 12 to 18 months gets completed in weeks.
Our automated approach reduces human error during migration. The system handles repetitive tasks like code conversion and data mapping while your team focuses on business-critical decisions. You get accurate migrations without the usual trial and error.
Cost savings start immediately. Organizations reduce both migration expenses and ongoing platform costs by moving to Fabric’s consumption-based model. FLIP tracks every change and validates data at each step, ensuring nothing gets lost in translation.
As a Microsoft Solutions Partner , Kanerika brings certified expertise and proven methodologies. We handle everything from initial assessment through post-migration optimization, delivering a production-ready Fabric environment that scales with your needs.
Transform Your Legacy Systems with Fabric Migration Partner with Kanerika for secure, fast, and reliable end-to-end delivery.
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FAQs 1. Why do companies migrate from Oracle to Microsoft Fabric Companies move because Microsoft Fabric brings data engineering, storage, real time processing and analytics into one platform. This removes the need for multiple tools and helps teams work with data faster. Fabric also integrates naturally with Power BI, which means teams can turn raw data into clear dashboards with less effort. For many organisations this leads to smoother reporting, better cost control and a more flexible cloud setup.
2. How difficult is it to migrate from Oracle to Microsoft Fabric The process is not as complex as many expect, but it does require a clear plan. Most teams start by exporting tables from Oracle, preparing them for a Lakehouse in Fabric and then rebuilding pipelines using Data Factory or Spark. Fabric includes guided tools and connectors that simplify the move. With careful mapping and clean data, the shift can be done with limited disruption.
3. What benefits do you get when you migrate from Oracle to Microsoft Fabric Moving to Fabric gives you one unified space for data storage, workflows, analytics and AI. This reduces switching between tools and lowers maintenance effort. Fabric also supports real time analytics, stronger security controls and cloud scale. The result is faster insight, lower cost of ownership and a more future-ready setup for business teams.
4. Can Microsoft Fabric handle workloads that currently run on Oracle Yes. Fabric is designed for heavy data workloads and supports both batch and real time processing. It can manage large datasets, complex joins and high-volume pipelines without slowing down. Teams can also scale compute resources based on need, which makes it suitable for finance, retail, manufacturing, supply chain and other data-heavy departments.
5. How long does it take to migrate from Oracle to Microsoft Fabric Timelines vary based on data size and workflow complexity. Small databases may move within a few days once planning is complete. Larger systems with hundreds of tables or advanced logic may take a few weeks. The main factors that affect timing are data quality, transformation needs and the number of pipelines that must be rebuilt.
6. What tools or services help you migrate from Oracle to Microsoft Fabric You can use Microsoft Fabric Data Factory, Azure Data Factory, Spark notebooks and Lakehouse tools to move and prepare data. Many teams also use built-in connectors that pull data from Oracle into Fabric without manual steps. These tools help with mapping, validation and automation, which makes the overall migration smoother and more predictable.
