Picture a mid-size company where finance data lives in SQL Server, customer records sit in Salesforce, and operational data is spread across an on-premises database that nobody wants to migrate. The analytics team spends more time wrangling data than actually analyzing it.
This is not an edge case. It is the default state for most organizations that have grown organically, acquired tools over time, or inherited systems from past IT decisions. And the cost of that fragmentation shows up everywhere, in slow reporting cycles, unreliable dashboards, and decisions made on stale numbers.
Data Factory in Microsoft Fabric is the integration layer built to change that. It connects to 170+ data sources, moves data at scale, transforms it, and orchestrates the entire workflow from one place inside Fabric’s unified platform .
In this article, we’ll cover what Data Factory in Microsoft Fabric actually is, its key features and capabilities, real-world use cases, and what existing Azure Data Factory users need to know before making the switch.
Key Takeaways Data Factory is Microsoft Fabric’s built-in service for connecting, moving, and transforming data at scale It connects to 170+ sources across on-premises, cloud, and multi-cloud environments Supports both ETL and ELT , and teams can combine both in the same pipeline Dataflow Gen2 lets analysts build transformation workflows without writing a single line of code Copilot is built in, so teams can design pipelines and fix errors using plain-language prompts
What Is Data Factory in Microsoft Fabric? Data Factory is one of six core workloads inside Microsoft Fabric, the platform Microsoft built to bring data engineering, analytics, real-time intelligence, and business intelligence together in one environment.
All of these workloads sit on top of OneLake, Fabric’s centralized data lake . That matters for Data Factory specifically because pipelines you build do not output data to a separate storage system. The results land directly in OneLake, where every other Fabric workload can access them without copying data or managing connectors between services.
Its core job is straightforward: connect to data sources, move data, transform it, and orchestrate workflows. The difference from standalone integration tools is that everything happens inside the same platform your analytics and reporting teams already use.
A note for Azure Data Factory users: Fabric Data Factory is the next generation of ADF, not a competing product. Existing ADF pipelines, connectors, and patterns carry forward, with meaningful additions on top.
So that is what it is. Now for how it actually functions under the hood.
How Data Factory in Microsoft Fabric Actually Works Data Factory is built around four core capabilities. Understanding how they fit together makes the rest of the feature set easier to evaluate.
Before getting into each one, it also helps to know that Fabric supports both ETL and ELT patterns. ETL transforms data before it reaches the destination, which works well when clean, structured data is needed on arrival. ELT loads raw data first and transforms it in place using Fabric’s compute engines, which suits larger datasets where cloud-scale processing handles the heavy lifting. Teams can combine both in the same solution.
1. Connect Data Factory pulls from 170+ native sources, spanning on-premises systems, cloud platforms, SaaS applications, and multi-cloud environments. Hybrid setups are supported through on-premises data gateways and virtual network gateways, so legacy systems do not need to be migrated before they can be connected.
Azure SQL, SharePoint, Salesforce, SAP, Snowflake, and 165+ other sources supported natively On-premises connectivity through data gateway or virtual network gateway Multi-cloud support covers AWS, Google Cloud, and hybrid environments in the same pipeline 2. Move Getting data from source to destination is handled through three purpose-built options, each suited to different scenarios. Choosing the right one depends on your volume, latency requirements, and how much control the workflow needs.
Copy Job Copy Activity gives teams fine-grained control over parallelism and transformation logic during the copy process Mirroring creates near real-time database replicas inside OneLake, supporting SQL Server, PostgreSQL, Cosmos DB, and Snowflake Raw data rarely arrives in a format analytics tools can use directly. Data Factory handles transformation through both low-code and code-first options, so analysts and engineers can both contribute without getting in each other’s way.
Dataflow Gen2 offers 300+ visual transformations including joins, aggregations, and data cleansing , no code required dbt job brings SQL-based transformations natively into Fabric for teams already running dbt workflows Pipeline activities support notebooks, Spark job definitions, stored procedures, and SQL scripts for custom transformation logic 4. Orchestrate Pipelines in Data Factory go beyond simple linear sequencing. They handle real workflow logic, including loops, conditionals, branching, and error handling, so complex multi-step processes can be expressed as a single managed workflow.
Schedule-based and event-driven triggers supported, including file arrival, folder events, and pipeline completion events Variable libraries (in preview) allow teams to reuse the same variables across multiple pipelines in a workspace 5. Copilot Copilot is embedded directly in Data Factory and supports pipeline design, dataflow authoring, and error diagnosis using plain-language prompts. Users describe what they need, and Copilot generates the corresponding expressions or transformation steps.
Summarizes existing dataflow queries and pipelines so teams can understand inherited logic quickly Diagnoses failed pipeline runs with categorized issues, root cause analysis, and suggested fixes
The pipeline expression builder Copilot is currently in preview. The broader Copilot integration across pipelines and dataflows is generally available.
That flexibility across all five capabilities is what the features section below builds on.
Key Features of Microsoft Fabric Data FactoryThe five capabilities above describe what Data Factory does structurally. The features below are what make each of those capabilities useful at enterprise scale.
1. 170+ Native Connectors, Including On-Premises and Multi-Cloud Data Factory connects to databases, file systems, APIs, cloud platforms, SaaS applications, and on-premises systems without requiring a separate integration layer. For teams managing hybrid environments, this means both on-prem and cloud sources can feed the same pipeline.
Covers Azure SQL, SharePoint, Salesforce, SAP, Snowflake, and 165+ other sources On-premises connectivity through data gateway or virtual network gateway Multi-cloud support for AWS and Google Cloud alongside Azure sources 2. Three Data Movement Options for Different Scenarios Copy Job handles most standard scenarios with bulk, incremental, and CDC delivery modes, and now auto-creates destination tables on the fly. Copy Activity gives engineers fine-grained control over parallelism and copy behavior. Mirroring supports near real-time database replication into OneLake without a traditional ETL pipeline .
Copy Job: simplified movement with bulk, incremental, and CDC support, no manual schema setup needed Copy Activity: manual control over parallel copying, transformation logic, and custom configurations Mirroring: near real-time replication from SQL Server, PostgreSQL, Cosmos DB, and Snowflake into OneLake Dataflow Gen2 gives analysts a visual interface to clean, reshape, and transform data without writing code. It handles joins, aggregations, data cleansing, and custom logic through 300+ built-in operations, and supports Python and R for teams that need more flexibility.
300+ built-in transformations accessible through a drag-and-drop interface Preview-only steps let teams validate logic against sample data without affecting production Teams already running dbt workflows can now run them natively inside Fabric Data Factory without rebuilding from scratch. dbt models are authored, orchestrated, and deployed inside Fabric, with access to Fabric’s governance and monitoring on top.
dbt jobs run natively inside Fabric as of public preview announced at Ignite 2025 Combines dbt’s version-controlled, testable approach with Fabric-native governance and CI/CD Supports analytics engineering teams that prefer SQL-based modular transformation workflows 5. Pipeline Orchestration with Full Control Flow Logic Pipelines support loops, conditionals, branching, and error handling, covering complex multi-step workflows in a single orchestrated process. Schedule-based and event-driven triggers let pipelines start automatically based on time, file arrivals, or the completion of another pipeline.
Supports loops, conditionals, branching, and error handling for complex workflow logic Event-based triggers on file arrival, Lakehouse folder events, and pipeline completion Apache Airflow integration available for teams building DAG-based orchestration in Python Microsoft Fabric Data Agents: How They Work in 2026 Learn how Microsoft Fabric Data Agents turn natural language questions into real-time data answers
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What This Means for the Businesses? Key Benefits Features matter. What organizations care about in practice is what those features change day to day for the people running the data function. Here is where the impact shows up.
When data integration , transformation, storage, and analytics run inside one platform, teams stop spending time maintaining connections between separate systems. The overhead of keeping ADF, Synapse, and standalone visualization tools in sync largely goes away.
Replaces separate integration, compute, and analytics tools with one Fabric capacity subscription Reduces pipeline maintenance caused by schema changes or API updates in disconnected systems Simpler onboarding for new data team members who only need to learn one environment 2. Analysts and Engineers Can Both Contribute Dataflow Gen2’s low-code interface lets analysts build and maintain transformation logic without waiting for an engineering ticket. Engineers retain full access to notebooks, Spark, SQL scripts, and dbt when more complex logic is needed.
Analysts manage their own data prep without creating a backlog in the engineering queue Both roles work inside the same platform, reducing handoff friction between teams 3. Governance Comes Included Every pipeline, transformation, and dataset in Fabric inherits the same governance model automatically. Sensitivity labels, Purview integration, lineage tracking, and role-based access are applied at the platform level rather than configured per tool.
Sensitivity labels and data classifications persist across all connected workloads Lineage tracking covers the full journey from source system to Power BI report Regulated industries get a compliance-ready governance structure without a separate setup 4. Cost Structure Simplifies Instead of licensing separate integration, compute, and visualization services, organizations pay for Fabric capacity that covers all workloads. For teams currently running ADF, Synapse, and Power BI Premium separately, consolidation onto Fabric changes both the cost model and the management overhead.
Eliminates overlapping licensing between ADF, Synapse, and standalone ETL tools Operational spend on infrastructure management decreases as workloads consolidate onto one platform
Where Teams Are Actually Using Fabric Data Factory? Top Use Cases Knowing what a tool can do is one thing. Seeing where it fits into real workflows makes it easier to evaluate whether it is the right fit for your situation. Here are five scenarios where organizations are using Data Factory in Microsoft Fabric today.
1. Centralizing Multi-Source Data for Reporting A finance team needs a consolidated weekly view pulling from an ERP system, a CRM, and several spreadsheets. Copy jobs pull from each source, Dataflow Gen2 standardizes the formats, and the output loads directly into a Fabric Data Warehouse that Power BI reads from.
Copy jobs handle extraction from multiple source systems on a single schedule Dataflow Gen2 standardizes formats and applies cleansing logic before the data lands in the warehouse 2. Near Real-Time Analytics on Operational Data A retail operations team needs inventory and order data refreshed continuously, not in overnight batches. Mirroring replicates SQL Server data into OneLake in near real-time, giving analysts access to data that is minutes old rather than hours old.
Analysts work with near real-time data without any additional infrastructure to maintain Operations teams can act on current inventory and order status rather than yesterday’s snapshot 3. Migrating from Azure Data Factory An organization running Azure Data Factory pipelines needs to bring them into Fabric’s ecosystem to take advantage of the unified monitoring hub, Copilot, and native Lakehouse connectivity. Microsoft provides tooling for both the assessment and the migration itself.
PowerShell upgrade module handles pipeline conversion from ADF to Fabric Existing pipeline logic is preserved, and teams gain Fabric-native monitoring and Copilot on top
Kanerika’s FLIP-powered migration accelerator also automates the migrations ADF and synapse pipelines to Fabric, saving hours of manual work, costs and resources.
4. Self-Service Data Prep for Analysts An analytics team works with data from SharePoint, APIs, and flat files. They need to clean and reshape it regularly, but every change currently requires an engineering ticket and a wait. Dataflow Gen2 changes that dynamic.
Analysts build and maintain transformation logic through the visual Dataflow Gen2 interface Preview-only steps let them validate changes against sample data without touching production Engineers are freed from routine transformation requests and focus on higher-complexity pipeline work 5. Automating Business Workflows Alongside Data Pipelines A pipeline finishes loading data and the operations team needs to know . Rather than building a separate notification system, the pipeline includes an Office 365 Outlook activity that sends a configured email on completion.
Office 365 Outlook activity sends automated notifications when pipeline runs complete or fail Pipelines can update records in connected systems, turning a data job into a broader business workflow Already on Azure Data Factory? Here’s What to Know The use cases discussed above assume a clean start on Fabric. But for many organizations, the more pressing question is what happens to the Azure Data Factory investment they already have.
Fabric Data Factory is the next generation of ADF, built on the same foundation but with meaningful additions that ADF does not have. Here is how the key differences break down.
1. CI/CD Is Simpler in Fabric There is no dependency on ARM templates in Fabric. Teams can cherry-pick individual workspace items for check-in and check-out, and the built-in deployment pipelines work without needing an external Git repo.
No ARM template dependency for CI/CD workflows Built-in deployment pipelines work without an external Git repo Teams can cherry-pick individual pipeline items for version control rather than committing the full workspace 2. Monitoring Is More Useful The monitoring hub in Fabric provides cross-workspace visibility that ADF’s dashboard does not. Troubleshooting copy activities includes a detailed breakdown view that shows significantly more than the ADF equivalent.
Cross-workspace monitoring from a single hub, not per-pipeline dashboards Detailed copy activity breakdown for faster troubleshooting Copilot provides error summaries with root cause analysis and suggested fixes directly in the UI
3. ADF and Synapse Pipelines Can Run from Inside Fabric The new pipeline activity lets teams run and monitor existing ADF and Synapse pipelines from within Fabric, alongside Fabric dataflows and notebooks. This makes it practical to start using Fabric before committing to a full migration.
Run and monitor ADF and Synapse pipelines natively from a Fabric workspace Combine existing ADF pipelines with Fabric dataflows and notebooks in the same workflow Evaluate Fabric capabilities in parallel with existing ADF workloads before committing to a full cutover 4. Connector Parity Is Still Catching Up Most connectors are available in Fabric Data Factory, but organizations with specialized or less common sources should check the connector continuity documentation before setting a migration timeline.
Most major connectors are available and in parity with ADF Specialized or legacy connectors may not yet be fully supported in Fabric Microsoft’s connector parity documentation is the most reliable reference before planning a migration
For teams ready to move, Microsoft provides a PowerShell upgrade module and an ADF-to-Fabric item migration feature, both documented in the Fabric migration guide.
Getting the migration right is where working with an experienced implementation partner makes a meaningful difference.
How Kanerika Helps Organizations Implement and Migrate to Microsoft Fabric Data Factory The value of Fabric Data Factory depends heavily on how it is set up. Connecting 170+ data sources is possible, but knowing which connections to build first, how to structure pipelines for long-term maintainability, and how to configure governance correctly takes experience with the platform.
Kanerika is a Microsoft Solutions Partner for Data and AI and a Microsoft Fabric Featured Partner. The team includes Fabric-certified engineers, MVPs, and Superusers who work with organizations at every stage of Fabric adoption , from initial architecture to full platform rollout.
Microsoft Fabric Implementation Kanerika helps organizations design and build Fabric environments where Data Factory is one piece of a larger, integrated data architecture. That includes OneLake structure, data warehouse design , governance configuration, and pipeline strategy across all connected workloads.
Implementation is not treated as a standalone project. The goal is an architecture that supports the organization’s data needs today and scales without a rebuild as those needs grow.
For organizations moving off Azure Data Factory, Synapse, or on-premises ETL tools, Kanerika’s FLIP migration accelerator platform reduces the time and risk involved in the transition.
FLIP migration paths relevant to Data Factory:
Azure to Fabric Migration: moves existing Azure data estates into Fabric’s native environment Informatica to Microsoft Fabric: replaces legacy ETL pipelines with Fabric-native equivalents
The Azure to Fabric Migration Accelerator reached General Availability as a Microsoft Fabric workload , making it a production-ready, validated path for organizations ready to move.
Case Study: Achieving a 90% Data Accuracy for a US Material Handling Company with Fabric across a wide network of service centers and warehouses across the US. Before working with Kanerika, their data was fragmented and unreliable.
The challenges they were dealing with: Data across SQL Server and SharePoint was completely siloed, with no central repository Inconsistent data quality was skewing KPI reporting across operational teams No unified architecture meant real-time decision-making was not possible What Kanerika built: A Data Lakehouse on Microsoft Fabric integrating data from SQL Server and SharePoint
A full data cleansing and validation process to fix KPI reliability at the source
A Power BI reporting framework with role-specific dashboards for different operational teams
The results: 90% improvement in data accuracy and KPI reliability 85% increase in operational visibility across service centers 100% scalable architecture built to grow with the business
A fragmented data estate became a clean foundation for operational decisions, without a multi-year transformation project.
A fragmented data estate became a clean foundation for operational decisions, without a multi-year transformation project.
Wrapping Up Upgrading your data infrastructure is a significant decision, and Microsoft Fabric makes a strong case as the platform to build on. Data Factory sits at the center of that, giving teams a single place to connect, move, transform, and orchestrate data across an entire estate, with AI assistance, unified governance, and direct integration with the rest of the Fabric platform built in.
Getting there faster, with less risk, is where the right partner matters. Kanerika brings certified expertise, production-tested migration tooling through FLIP, and a team that has done this across industries. Talk to us today to get started.
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FAQs What is the difference between ADF and Fabric Data Factory? Azure Data Factory (ADF) is a standalone cloud ETL service, while Fabric Data Factory is a native component within Microsoft Fabric’s unified analytics platform. The key distinction lies in integration depth—Fabric Data Factory connects seamlessly with OneLake, Power BI, and other Fabric workloads without requiring separate configurations. ADF operates independently and requires additional setup for cross-service connections. Fabric Data Factory also introduces Dataflow Gen2 and enhanced Copilot capabilities unavailable in standalone ADF. Kanerika helps enterprises evaluate which data integration approach aligns with their analytics strategy—connect with our Fabric specialists today.
Does Microsoft Fabric include Data Factory? Yes, Microsoft Fabric includes Data Factory as one of its core integrated workloads. Unlike purchasing Azure Data Factory separately, Fabric bundles data integration capabilities directly into the platform alongside Power BI, Synapse Data Engineering, and Real-Time Analytics. This native inclusion means pipelines and dataflows operate within a unified governance model and share OneLake storage automatically. Organizations gain simplified licensing and reduced infrastructure complexity when using Data Factory in Microsoft Fabric. Kanerika’s Microsoft Fabric experts can guide your team through platform onboarding—reach out for a tailored implementation roadmap.
What does Data Factory in Microsoft Fabric actually do? Data Factory in Microsoft Fabric orchestrates data movement and transformation across cloud and on-premises sources into OneLake. It enables building ETL and ELT pipelines using a visual interface, scheduling data refreshes, and integrating with over 150 connectors. Dataflow Gen2 provides Power Query-based transformations for business users, while data pipelines handle complex orchestration logic. The workload supports both code-free and code-first approaches for maximum flexibility. Kanerika designs Fabric Data Factory architectures that scale with enterprise data volumes—schedule a consultation to optimize your data workflows.
How is Microsoft Fabric Data Factory different from Azure Data Factory? Microsoft Fabric Data Factory differs from Azure Data Factory through its deep platform integration and unified data estate. While ADF requires manual connections to storage and analytics services, Fabric Data Factory writes directly to OneLake with automatic delta format optimization. Fabric includes Dataflow Gen2 with enhanced Power Query capabilities and native Copilot assistance for pipeline generation. Licensing shifts from per-pipeline pricing to capacity-based consumption within Fabric. Both share core pipeline concepts, making migration feasible. Kanerika specializes in Azure to Microsoft Fabric migrations—let us assess your current ADF estate for a smooth transition.
What is included in Microsoft Fabric? Microsoft Fabric includes Data Factory, Data Engineering, Data Warehouse, Data Science, Real-Time Analytics, and Power BI within a single unified platform. All workloads share OneLake as the centralized storage layer, eliminating data silos. Fabric provides built-in governance through Purview integration, capacity-based licensing, and Copilot AI assistance across experiences. Data Factory specifically handles data integration and orchestration, while other components address analytics, machine learning, and visualization needs. This consolidation reduces infrastructure complexity significantly. Kanerika helps enterprises unlock full Fabric capabilities—contact us for a comprehensive platform adoption strategy.
Can I migrate existing Azure Data Factory pipelines to Microsoft Fabric? Yes, existing Azure Data Factory pipelines can be migrated to Microsoft Fabric Data Factory with careful planning. Microsoft provides compatibility for core pipeline activities, linked services, and datasets, though certain configurations require adjustment for OneLake destinations. Integration runtimes need reconfiguration, and Dataflow Gen1 should upgrade to Dataflow Gen2. The migration preserves business logic while enabling access to Fabric’s unified governance and enhanced features. Testing in parallel environments ensures continuity before cutover. Kanerika’s ADF to Fabric migration accelerators reduce transition time significantly—request a free migration assessment to start.
What is replacing SSIS? Azure Data Factory and Microsoft Fabric Data Factory are replacing SSIS for modern cloud-based data integration. While SSIS remains supported for on-premises SQL Server workloads, new implementations favor cloud-native ETL platforms that offer better scalability, managed infrastructure, and broader connector ecosystems. Fabric Data Factory extends this by integrating data pipelines directly with OneLake and Power BI. Organizations modernizing legacy SSIS packages gain improved monitoring, version control, and collaboration capabilities. Kanerika has migrated hundreds of SSIS packages to cloud platforms—reach out to modernize your data integration infrastructure.
Is ADF better than SSIS? ADF is better than SSIS for cloud-first organizations requiring scalable, serverless data integration. Azure Data Factory offers managed infrastructure, 100+ native cloud connectors, and pay-per-use pricing without server maintenance. SSIS excels in on-premises SQL Server environments with existing package investments and requires fixed infrastructure. ADF provides superior monitoring through Azure Monitor and integrates natively with cloud storage and SaaS applications. For unified analytics, Microsoft Fabric Data Factory extends ADF capabilities further. Kanerika evaluates your current SSIS workloads and designs optimal migration paths to ADF or Fabric—book a technical consultation today.
What is Dataflow Gen2 and how is it used in Microsoft Fabric Data Factory? Dataflow Gen2 is the enhanced Power Query-based transformation engine within Microsoft Fabric Data Factory for self-service data preparation. It enables business analysts and data engineers to build visual data transformations using familiar Power Query M language without writing code. Unlike Gen1, Dataflow Gen2 outputs directly to OneLake storage, supports staging configurations, and runs on Fabric capacity rather than premium licensing. Common uses include data cleansing, merging sources, and preparing datasets for Power BI or data warehouses. Kanerika builds scalable Dataflow Gen2 solutions for enterprise transformation needs—contact us to accelerate your data preparation workflows.
What is the difference between ETL and ELT in Microsoft Fabric? ETL extracts, transforms, then loads data into the destination, while ELT loads raw data first and transforms it within the target system. Microsoft Fabric supports both patterns through Data Factory. ETL suits scenarios requiring data cleansing before storage, using Dataflow Gen2 for transformations. ELT leverages Fabric’s compute-heavy environment, loading data into OneLake then transforming via notebooks or SQL endpoints. ELT typically performs better with large datasets since transformations use scalable Fabric capacity. Choose based on data volume, transformation complexity, and governance requirements. Kanerika architects optimal ETL and ELT pipelines in Fabric—discuss your requirements with our data engineers.
Is ADF an ETL tool? Yes, Azure Data Factory is an ETL and ELT tool designed for cloud-scale data integration. ADF orchestrates data movement from 100+ sources, applies transformations through mapping data flows or external compute, and loads results into destinations like Azure SQL, Synapse, or OneLake. It supports both code-free visual development and code-based customization. Within Microsoft Fabric, Data Factory extends these capabilities with tighter platform integration, Dataflow Gen2 transformations, and unified governance. ADF handles batch processing, incremental loads, and complex pipeline orchestration. Kanerika implements production-grade ADF and Fabric Data Factory solutions—let us optimize your data integration architecture.
What are the key components of ADF? Azure Data Factory’s key components include pipelines, activities, datasets, linked services, integration runtimes, and triggers. Pipelines organize activities into logical workflow units. Activities define operations like copy data, execute stored procedures, or run data flows. Datasets represent data structures within sources and destinations. Linked services store connection configurations for external systems. Integration runtimes provide the compute infrastructure for data movement. Triggers schedule or event-activate pipeline execution. Microsoft Fabric Data Factory shares these concepts while adding OneLake native integration. Kanerika’s ADF certified engineers design robust data pipeline architectures—connect with us to build your integration foundation.
What is ADF used for? ADF is used for building cloud-scale ETL and ELT data pipelines that extract from diverse sources, transform data, and load into analytics destinations. Common use cases include data warehouse loading, SaaS application integration, legacy system migration, and real-time data synchronization. Azure Data Factory handles batch ingestion, incremental updates, and complex orchestration across hybrid environments. Within Microsoft Fabric, Data Factory adds unified governance, OneLake native storage, and Copilot-assisted development. Enterprises rely on ADF for production-grade data integration at scale. Kanerika delivers end-to-end ADF implementations tailored to your data strategy—schedule a discovery call to explore solutions.
How do I get to the Data Factory in Fabric? Access Data Factory in Fabric by signing into app.fabric.microsoft.com and selecting a workspace with Fabric capacity enabled. From the workspace, click the New button and choose Data Pipeline or Dataflow Gen2 under the Data Factory category. Alternatively, use the experience switcher in the bottom-left corner and select Data Factory to see all related items. You can also create Data Factory items from the Fabric home page by selecting the Data Factory workload tile. Ensure your account has appropriate workspace permissions. Kanerika provides hands-on Fabric onboarding for enterprise teams—reach out to accelerate your platform adoption.
What is Mirroring in Microsoft Fabric and when should you use it? Mirroring in Microsoft Fabric replicates external databases into OneLake in near real-time without building traditional ETL pipelines. It creates a synchronized copy of Azure SQL, Cosmos DB, or Snowflake data that stays current automatically. Use mirroring when you need low-latency analytics on operational data, want to avoid complex change data capture pipelines, or require unified access across disparate sources in OneLake. Mirroring complements Data Factory by handling continuous replication while pipelines manage batch transformations. Kanerika implements mirroring strategies alongside Data Factory for comprehensive data integration—contact us to design your hybrid architecture.
Does Microsoft Fabric Data Factory support Apache Airflow for workflow orchestration? Yes, Microsoft Fabric Data Factory supports Apache Airflow as a managed orchestration option for teams preferring Python-based DAG workflows. Fabric offers Apache Airflow jobs that run within the platform’s capacity, enabling data engineers to leverage existing Airflow skills and code. This complements native data pipelines by supporting complex dependency management and programmatic workflow definitions. Teams can orchestrate Fabric items, external services, and custom scripts through Airflow DAGs while benefiting from Fabric’s unified governance. Kanerika helps enterprises integrate Airflow orchestration within Fabric environments—discuss your workflow automation needs with our data engineering team.
How does Copilot work inside Microsoft Fabric Data Factory? Copilot in Microsoft Fabric Data Factory uses generative AI to assist pipeline development through natural language commands. Users describe data integration tasks conversationally, and Copilot generates pipeline activities, suggests transformations, and creates dataflow logic automatically. It accelerates development by translating requirements like move daily sales data from SQL to OneLake into configured pipeline components. Copilot also explains existing pipeline logic and recommends optimizations. The feature requires Fabric capacity with Copilot enabled and works within the pipeline and dataflow editors. Kanerika leverages Copilot capabilities to accelerate client implementations—explore AI-assisted data integration with our Fabric experts.
Why use Synapse over ADF? Synapse Analytics offers advantages over standalone ADF when requiring integrated analytics alongside data integration. Synapse combines pipelines, dedicated SQL pools, Spark notebooks, and data exploration in one workspace, reducing context switching. It provides better performance for large-scale transformations using Spark or SQL compute directly within the platform. However, Microsoft Fabric now supersedes Synapse for new implementations, offering deeper unification with Power BI and OneLake storage. ADF remains optimal for pure data integration without analytics compute needs. Kanerika evaluates your analytics requirements and recommends the right platform—connect with us for an architecture assessment.
