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Fabric vs. Databricks vs. Synapse

TL;DR

For a greenfield Azure Commercial analytics workload that is Power BI-centric, pick Microsoft Fabric. For Spark/ML-heavy or multi-cloud workloads, pick Azure Databricks. For Azure Government or existing dedicated SQL pool estates, pick Azure Synapse Analytics.

When this question comes up

  • A new agency or business unit is scoping its first cloud analytics platform and wants one primary engine.
  • An existing SQL DW or on-prem Hadoop workload is being modernized and must land on Azure.
  • Leadership wants to consolidate multiple tools into a single "Fabric-equivalent" control plane.

Decision tree

flowchart TD
    start["Azure Commercial or Government?"] -->|Commercial| q_fit
    start -->|Government| q_gov

    q_fit{"Need Fabric-unique feature?<br/>(OneLake, Direct Lake,<br/>Data Activator, RTI)"}
    q_fit -->|Yes — PBI / OneLake central| q_shape
    q_fit -->|No — Spark / ML heavy| q_spark
    q_fit -->|No — SQL-first warehouse| q_sql

    q_shape{"Need custom Docker / non-notebook<br/>Python or >10 TB tuned Spark?"}
    q_shape -->|Yes| rec_db["**Recommend:** Azure Databricks"]
    q_shape -->|No| rec_fab["**Recommend:** Microsoft Fabric"]

    q_spark{"Need MLflow / Unity Catalog /<br/>Photon mastery?"}
    q_spark -->|Critical| rec_db
    q_spark -->|Nice to have| q_eco

    q_eco{"Heavy M365 / Power BI /<br/>Purview investment?"}
    q_eco -->|Yes| rec_fab
    q_eco -->|Mixed / multi-cloud| rec_db

    q_sql{"Dedicated-pool MPP needed?<br/>(>50 concurrent users)"}
    q_sql -->|Yes| rec_syn["**Recommend:** Azure Synapse"]
    q_sql -->|No| rec_fab

    q_gov{"Compliance ceiling?"}
    q_gov -->|FedRAMP High / IL4| q_gov_platform
    q_gov -->|IL5 / IL6| rec_syn

    q_gov_platform{"Databricks authorized in Gov?"}
    q_gov_platform -->|Yes| rec_db
    q_gov_platform -->|No — Synapse standard| rec_syn

Per-recommendation detail

Recommend: Microsoft Fabric

When: Commercial tenant, Power BI / OneLake / Data Activator is central, notebooks and T-SQL cover the transformation needs.

Why: Unified control plane (OneLake, Data Factory, Warehouse, Power BI, Data Activator, Real-Time Intelligence) with Direct Lake eliminating Power BI semantic-model refresh cycles.

Tradeoffs:

  • Cost: F-SKU capacity base cost ($$$) plus pay-as-you-use CU overage.
  • Latency: Direct Lake sub-second over gold-layer Delta.
  • Compliance: Commercial GA only; FedRAMP High not yet GA in Azure Gov.
  • Skill match: Low — SQL + notebooks + Power BI.

Anti-patterns:

  • Custom Docker images or non-notebook Python entry points.
  • Any Azure Government workload today (2026-Q2).
  • Teams with deep MLflow + Unity Catalog practice they do not want to give up.

Linked example: examples/commerce/

Recommend: Azure Databricks

When: Spark, ML, streaming, or multi-cloud portability are critical; team has or wants MLflow + Unity Catalog expertise.

Why: Best-in-class Spark/Delta/Photon with the most mature lakehouse tooling; Unity Catalog gives fine-grained access control across workspaces.

Tradeoffs:

  • Cost: DBU-based; right-sizing is an operational concern; Photon improves $/TB.
  • Latency: Interactive seconds, streaming sub-minute, SQL Warehouses sub-second for BI.
  • Compliance: Azure Commercial and Azure Gov (FedRAMP High, IL4, IL5 with qualifying SKUs).
  • Skill match: Higher — Spark, Python/Scala, Unity Catalog required.

Anti-patterns:

  • Pure SQL warehousing with no Spark needs.
  • Workloads where Power BI Direct Lake and Data Activator are the primary requirement.

Linked example: examples/iot-streaming/

Recommend: Azure Synapse Analytics

When: Azure Government, existing dedicated-pool SQL DW, or IL5/IL6 compliance ceiling.

Why: Longest Gov compliance track record; mature dedicated MPP pools; serverless SQL for ad-hoc over ADLS.

Tradeoffs:

  • Cost: Dedicated pools capacity-reserved ($$$); serverless SQL pay-per-TB-scanned.
  • Latency: Dedicated pools sub-second for high concurrency.
  • Compliance: Full FedRAMP High, IL4, IL5 in Azure Gov.
  • Skill match: Medium — T-SQL first, Spark pools secondary.

Anti-patterns:

  • Greenfield Commercial lakehouse — Fabric or Databricks wins on velocity.
  • Intermittent / bursty workloads on dedicated pools — use serverless SQL or Databricks SQL Warehouse.

Linked example: examples/usda/