Azure Synapse and Microsoft Fabric: A Practical Roadmap to Integrate and Modernize Your Data Ecosystem

If you’re running analytics in Azure today, you’ve likely felt the shift: Microsoft Fabric is here, and it brings an “all-in-one” data and analytics platform that promises to simplify architectures and accelerate insights. Meanwhile, Azure Synapse Analytics remains a powerful, battle-tested workhorse across SQL, Spark, and Data Explorer workloads. The question leaders ask is no longer “either/or,” but “how do we integrate Synapse and Fabric to modernize without disruption?”

This guide offers a clear, actionable path to make both platforms work together—so you can modernize at the right pace, reduce complexity, and deliver value faster.

What Is Azure Synapse Analytics? A Quick Recap

Azure Synapse Analytics is a unified analytics service that brings together:

• Dedicated SQL pools (MPP) and serverless SQL for warehousing and ad hoc querying
• Apache Spark pools for data engineering and machine learning
• Synapse Pipelines (powered by Azure Data Factory) for orchestration and ingestion
• Data Explorer (Kusto) for log/time-series analytics
• Tight integration with Azure Data Lake Storage Gen2 (ADLS), Power BI, and Azure ML

It’s flexible, secure, and ideal for teams that want fine-grained control over infrastructure, networking, and cost models.

What Is Microsoft Fabric? The Lakehouse-First, SaaS Analytics Platform

Microsoft Fabric is a SaaS platform that unifies engineering, analytics, real-time, and BI under one experience:

• OneLake: a single, logical data lake across your org
• Lakehouse and Warehouse experiences (Delta and SQL-first)
• Data Engineering (Spark), Data Factory (pipelines/dataflows)
• Real-Time Analytics (KQL), Eventstreams
• Power BI as the native semantic and visualization layer
• Deep governance and security integration across workspaces and domains

Fabric prioritizes simplicity, standardization, and speed-to-value—especially for organizations looking to reduce tool sprawl and operational overhead.

Synapse vs. Fabric: It’s Not a Binary Choice

Think “platform synergy” rather than “replacement.” Here’s a practical way to position them:

• Choose Synapse when you need: strict VNet isolation, highly tuned MPP SQL pools, or existing ADF/Synapse pipelines that must keep running at scale.
• Choose Fabric when you want: a SaaS experience with OneLake, a tightly integrated lakehouse + BI stack, simplified operations, and faster delivery for cross-functional teams.
• Choose both in hybrid: keep heavy ETL/ML or specialized workloads in Synapse; land curated Delta tables in OneLake and use Fabric for semantic models, reporting, and self-service analytics.

Curious why lakehouse-first platforms matter? Explore the core patterns and benefits in this deep dive on data lakehouse architecture.

Integration Patterns That Work in the Real World

Pattern 1: Lift-and-Shift to Fabric Lakehouse (When You’re Ready)

• Convert or land data as Delta tables in OneLake.
• Re-platform core models to Fabric Warehouse or Lakehouse.
• Rebuild or connect Power BI datasets directly to Fabric items.
• Best for teams ready to reduce platform complexity and standardize on Fabric.

Pattern 2: Coexistence/Hybrid with Shortcuts

• Keep ingest/transform in Synapse (or Databricks) writing to ADLS in Delta/Parquet.
• Use Fabric Shortcuts to reference that data without copying it into OneLake.
• Build semantic models and reports in Fabric while maintaining current pipelines.
• Best for gradual modernization without disrupting production.

Pattern 3: Synapse for Heavy Engineering, Fabric for BI at Scale

• Use Synapse Spark or SQL pools for complex transformations and ML training.
• Write curated data to Delta in a medallion structure (Bronze/Silver/Gold).
• Expose Gold Delta to Fabric Lakehouse/Power BI for governed self-service.
• Best for organizations invested in engineering-first architectures that want a modern BI layer.

Pattern 4: Real-Time Bridge

• Stream events into Synapse Data Explorer or Event Hubs.
• Mirror or land aggregates in Fabric Real-Time Analytics and Lakehouse.
• Blend real-time dashboards with historical context in Power BI.

Ingestion and Orchestration: Keep What Works, Adopt What’s Better

• If you rely on ADF/Synapse Pipelines, you can modernize safely by standardizing patterns before migration. A highly scalable approach is metadata-driven ingestion in Azure Data Factory.
• Fabric’s Data Factory experience (pipelines and dataflows) offers low-code options and closer integration with Lakehouse and Power BI. Use it for new workloads or when you’re ready to consolidate tooling.
• Focus on idempotent, parameterized pipelines that write Delta consistently, with schema evolution and data quality checks baked in.

Governance and Security: Set the Guardrails Early

Strong governance is the difference between a demo and a durable platform.

• Identities and access: Use Azure AD/Entra ID with least-privilege, workspace-level RBAC, and domain-based organization in Fabric.
• Data protection: Sensitivity labels, row-level and object-level security, and parameterized access policies.
• Lineage and cataloging: Leverage Microsoft Purview for discovery, lineage, and policy-based governance.
• BI governance: Align workspace roles, dataset certification, and deployment pipelines with a clear center of excellence. For a practical blueprint, see this guide on Power BI governance.

Performance Essentials: Make Delta and Semantic Models Shine

• Delta Lake best practices: partition by query predicates, compact small files, and OPTIMIZE frequently to reduce read overhead.
• Modeling for BI: centralize business logic in semantic models (measures, relationships, RLS), not in reports. Reuse models across departments to ensure consistency.
• Warehouse vs. Lakehouse: for SQL-heavy analytics with BI concurrency, Warehouse is often the simplest route; for engineering and ML flexibility, Lakehouse (Spark + Delta) fits best.
• Capacity planning: right-size Fabric capacities, isolate critical workloads by workspace, and set concurrency limits to protect SLAs.

Cost Management: Visibility Before Optimization

• Synapse:
• Dedicated SQL pools: scale DWUs based on SLA and concurrency.
• Serverless SQL: great for ad hoc; monitor per-TB scanned.
• Spark pools: size and auto-pause to avoid idle burn.
• Fabric:
• Use capacity SKUs aligned to workload patterns.
• Segment workspaces by business unit or environment to track spend.
• Schedule refresh windows and control long-running Spark jobs.
• Cross-platform:
• Adopt unit-cost metrics (cost-per-query, cost-per-refresh, cost-per-user).
• Tag resources and centralize monitoring to spotlight outliers early.

A Pragmatic 90-Day Modernization Plan

• Phase 0: Discover and Prioritize
• Inventory sources, pipelines, models, and reports.
• Identify business-critical datasets and SLAs.
• Phase 1: Foundation and Governance
• Configure Fabric domains, workspaces, security, and Purview integration.
• Define naming conventions, environments, and CI/CD approach.
• Phase 2: Land and Standardize Data
• Write curated data to Delta (medallion pattern).
• Set up orchestration (Synapse Pipelines or Fabric Data Factory).
• Phase 3: Model and Visualize
• Create Lakehouse/Warehouse objects and Power BI semantic models.
• Validate performance, RLS, and refresh behaviors.
• Phase 4: Operate and Optimize
• Enable monitoring, cost controls, and data quality SLAs.
• Train users and roll out self-service with guardrails.

Common Pitfalls to Avoid

• Recreating 1:1 legacy schemas without adopting Delta and Lakehouse patterns.
• Skipping a semantic model and putting logic in visuals.
• Ignoring file size/partitioning in Delta (death by tiny files).
• Underestimating BI refresh windows and capacity needs.
• Overmixing tools without clear ownership (who does what, where, and why).
• No Dev/Test/Prod separation—every data platform needs change control.

Example Architecture: Mid-Market Enterprise

• Sources: ERP, CRM, web/app telemetry, CSV/S3 drops
• Ingestion: ADF/Synapse Pipelines (metadata-driven), event streaming for real time
• Storage: ADLS Gen2 + OneLake Shortcuts; Delta across Bronze/Silver/Gold
• Transform: Synapse Spark or Fabric Data Engineering (Spark)
• Serve: Fabric Warehouse for SQL-first analytics; Lakehouse for engineering and ML
• BI: Power BI semantic models and certified datasets on top of Lakehouse/Warehouse
• Governance: Purview, RLS/OLS, labeling; deployment pipelines for BI

Decision Checklist: Are You Ready to Add Fabric?

• You want to reduce tool sprawl and centralize BI + engineering.
• Your teams need faster time-to-insight with less platform operations.
• You can standardize on Delta Lake and semantic models.
• You have a governance plan and clear ownership across domains.
• You’re prepared to migrate in phases, not a “big bang.”

FAQs

1) Is Microsoft Fabric replacing Azure Synapse Analytics?

No. Fabric and Synapse overlap, but they target different operating models. Fabric is a SaaS platform centered on OneLake, semantic models, and integrated BI. Synapse remains powerful for PaaS-style control, dedicated SQL pools, and advanced networking. Many organizations run both in a hybrid setup—especially during modernization.

2) What’s the difference between OneLake and ADLS Gen2?

OneLake is the logical, unified data layer in Fabric. It can reference data in ADLS, S3, and other sources via Shortcuts, so you don’t have to copy data to use it. ADLS Gen2 is a storage service in Azure. In hybrid models, you can keep data in ADLS and expose it in Fabric through OneLake Shortcuts.

3) Can I reuse my Synapse or Databricks Delta tables in Fabric?

Yes. If your tables are Delta, Fabric Lakehouse can read them directly via Shortcuts or by landing curated copies in OneLake. This is the fastest path to value—no need to refactor everything at once.

4) Should I use Fabric Warehouse or Lakehouse?

• Use Warehouse when you want a SQL-first experience with BI concurrency and a familiar warehousing model.
• Use Lakehouse when you need Spark-first engineering, ML workflows, and flexible transformations in Delta.

Many teams use both: Warehouse for governed BI and Lakehouse for data engineering.

5) How do I migrate existing ADF/Synapse pipelines?

Start by standardizing patterns (parameters, retries, lineage, data quality) and consolidating into a metadata-driven framework. Then, either:

• Keep them running in Synapse and surface outputs in Fabric, or
• Rebuild critical pipelines in Fabric Data Factory over time.

This guide to metadata-driven ingestion in Azure Data Factory explains an approach that scales cleanly.

6) How does Fabric impact Power BI governance?

Fabric elevates the role of the semantic model. Establish certified datasets, define workspace roles, and implement deployment pipelines. Create a center of excellence with clear standards for RLS, naming, and versioning. For a practical playbook, see Power BI governance.

7) What’s the best way to handle schema evolution and small files?

Adopt Delta Lake best practices: schema evolution with expectations, regular OPTIMIZE/compaction, and partitioning by common filter columns. Avoid tiny files by batching writes and using auto-optimize where available.

8) How do I estimate Fabric capacity?

Start with usage patterns:

• Number of concurrent refreshes/queries
• Size and frequency of data loads
• Spark job profiles and durations

Pilot with conservative capacity, measure saturation and refresh times, then right-size. Segment critical workloads into dedicated workspaces to isolate performance.

9) Do I need a lakehouse to get value from Fabric?

You’ll get the most from Fabric by embracing the lakehouse pattern (Delta + semantic models). If you’re SQL-first, Fabric Warehouse can still deliver immediate wins, but landing data in Delta unlocks flexibility, interoperability, and cost efficiency across the stack. Learn why in this explainer on lakehouse architecture.

10) Can I mix real-time analytics with historical reporting in Fabric?

Yes. Use Real-Time Analytics (KQL databases/Eventstreams) for streaming ingestion and fast aggregation. Land snapshots or aggregations into Lakehouse/Warehouse for historical context, then blend both in Power BI for complete, up-to-the-minute dashboards.

Modernizing your data ecosystem doesn’t have to be disruptive. Treat Synapse and Fabric as complementary tools in one strategy: standardize on Delta, build strong governance, and phase your migration. Do that, and you’ll deliver insights faster while simplifying your platform for the long run.