A practical roadmap to modern data estates using Snowflake and Fabric, covering governance, Direct Lake, secure sharing, and a 90-day plan for measurable wins.

Your finance team in Mumbai is closing the month on Oracle Fusion, while operations still run critical processes on EBS. A regulatory audit is due, product demand spikes, and leaders want the same-hour KPIs, not yesterday’s batch. Yet the warehouse you built for nightly loads and scale up appliances cannot keep pace with schema drift, concurrency spikes, and real-time alerts. This is where Reimagining Data Management with Snowflake and Fabric becomes a practical discussion, not a platform beauty contest.

The performance gap is measurable. Firms that operate with real-time data show higher revenue growth and profit margins than slower peers because frontline decisions use trusted, accessible data rather than stale aggregates. In parallel, the global analytics market and streaming workloads continue to expand, which pressures legacy designs that duplicate data across tools and regions.

Two shifts define the new playbook. First is multi-cloud elasticity, which isolates workloads and scales without contention, as evidenced by Snowflake replication and cross-region continuity. Second is a unified data plane in Microsoft Fabric, where OneLake, Direct Lake, ingestion, and governance live in one SaaS experience, so teams stop stitching brittle ETL chains. The rest of this article explains how these levers translate into AI readiness and real-time decisioning, with a practical roadmap to move from legacy to modern.

 

Two architectural levers that change everything

Lever 1: Decoupled compute and storage for true elasticity and concurrency

Modern estates separate persistent storage from on-demand compute so teams can scale without fighting for the same box. In Snowflake, virtual warehouses are independent compute clusters you can size and auto-suspend or auto-resume, all reading the same shared data layer. That removes resource contention, lets finance, data engineering, and data science run simultaneously, and keeps costs predictable. Snowflake’s design explicitly chose storage and compute separation and supports multi-cluster shared data to handle spikes cleanly.

What changes in practice: isolate workloads per team or job, burst for month-end close, and stop queuing ad hoc analytics while ETL runs. If you operate across regions or clouds, Snowflake’s replication and failover features extend the same model across providers for resilience and locality.

Lever 2: A unified data plane with built-in governance and metadata

Enterprises also need one logical place to land, transform, secure, and serve data without stitching five tools. Microsoft Fabric provides OneLake as a single, unified, logical data lake for the entire organization, plus Direct Lake, so Power BI consumes data straight from Delta tables without import copies. Real-Time Intelligence handles streaming, event processing, and actions in the same platform. Governance integrates through Microsoft Purview for catalog, lineage, sensitivity labels, and audit, so protection and accountability follow the data into reports.

What changes in practice: Fewer brittle ETL hops, one copy patterns, consistent access controls, and auditable lineage from source to dashboard. Teams ship features faster because models, metrics, and permissions live on one substrate rather than being duplicated.

 

Snowflake multi cloud elasticity — what it concretely delivers for enterprises

Cross-cloud presence for resilience and locality

Snowflake enables database and account replication across regions and cloud providers, with planned and unplanned failover or failback. This supports business continuity, regional performance, and data residency without replatforming.

Burst concurrency without contention

Virtual warehouses are independent MPP compute clusters that do not impact one another. Thus, finance can close the month, engineering can run heavy transforms, and analysts can explore ad hoc data simultaneously. Multi-cluster warehouses add or remove clusters automatically to absorb spikes during peak periods.

Separation of compute and storage

Compute scales up or down independently of the shared data layer. Teams can size warehouses to workload profiles, pause them when idle, and avoid noisy neighbor effects while reading the same governed storage. This design choice is foundational to Snowflake architecture. 

Zero-copy data sharing and productization

Providers can publish tables and views to other Snowflake accounts without copying data. Consumers query live, governed datasets and only pay for their compute. Shares can be distributed through direct shares or listings, enabling broad and controlled distribution. 

 

Fabric end to end integration — what it concretely delivers for enterprises

OneLake as a single logical store with built in experiences

Fabric provides OneLake, a unified, logical data lake that comes with every tenant. Teams land, manage, and access data in one place without standing up infrastructure. Power BI can query Delta tables in OneLake directly using Direct Lake mode, which removes import copies and refresh chains for large models.

Streaming and event scenarios in the same plane

Real-time intelligence handles ingestion, transformation, storage, analytics, visualization, and actions on data in motion, so operations dashboards and alerts are live, in addition to batch analytics.

Continuous replication and mirroring for faster onboarding

Fabric Mirroring continuously replicates external sources into OneLake with low latency. Current support includes Snowflake, Azure SQL, SQL Server, Azure Cosmos DB, PostgreSQL, and Azure Databricks, which helps migrate or coexist without long rewrite cycles.

Embedded governance and lineage

Governance is integrated through Microsoft Purview. Sensitivity labels apply across Fabric items and persist on supported export paths, while governance and compliance features provide labeling, inheritance, and audit coverage. Lineage and catalog help teams trace data from source to report. 

 

How Snowflake plus Fabric patterns enable three outcomes

Outcome 1: Scalable analytics at enterprise scale

By isolating compute per team and auto scaling during peaks like month-end close, you remove resource contention and slow dashboards. Snowflake multi-cluster warehouses handle large numbers of concurrent users and queries without queuing. In Fabric, Direct Lake reads delta tables in OneLake without import copies, so large models refresh faster and stay interactive. Together, this delivers predictable SLAs for finance, operations, and analytics. 

Outcome 2: AI readiness with governed, reusable data

Both platforms reduce copying and let teams build on a consistent data foundation. Snowflake secure sharing and listings publish live, governed datasets to internal domains or partners without new pipelines. Fabric provides a logical lake and unifies governance with Microsoft Purview, so labels, lineage, and audit follow the data into reports. The result is cleaner feature creation, faster retraining, and controlled access for model teams. 

Outcome 3: Real time decisioning across the estate

Fabric Real-Time Intelligence brings ingestion, transformation, analytics, visualization, and actions into one place for event-driven scenarios. This supports alerts, streaming KPIs, and even real-time actions that feed operational systems. On the resilience side, Snowflake replication and failover across regions and clouds keep critical analytics available during incidents so frontline decisions can continue.

 

Practical roadmap: turning a legacy estate into a modern data foundation

Phase 1: Map and rationalize

Inventory sources, schemas, privacy boundaries, SLAs, and peak concurrency. Decide your primary pattern: Snowflake first for multi-cloud distribution and governed sharing, Fabric first for an integrated SaaS plane, or a hybrid. Establish a governance baseline so labels, catalog, and lineage are not an afterthought. In Fabric, integrate Microsoft Purview for sensitivity labels and end-to-end lineage into reports. In Snowflake, plan account structure, roles, and sharing models up front.

Checkpoint outcomes

  • Blueprint chosen with data domains, residency needs, and BI standards documented.
  • Purview integration is enabled for your Fabric workspaces and items.

Phase 2: Land the unified lake and controls

Create landing zones and shared storage. In Fabric, use OneLake as the logical store for all analytics data and define workspaces per domain. If you need quick coexistence, use Fabric Mirroring to continuously replicate selected platforms into OneLake while you retire brittle hops. Apply RBAC, labels, and lineage to control travel with data.

Checkpoint outcomes

  • OneLake active with initial domains, workspaces, and access policies.
  • First mirrored source validated with freshness objectives.

Phase 3: Migrate workloads incrementally

Move the first high-value analytics slice. In Fabric, publish a Power BI model using Direct Lake so reports read Delta tables in OneLake without import copies. In Snowflake, isolate ETL and analytics on separate virtual warehouses and enable multi-clustering where peaks justify it. If resilience or residency is a requirement, configure replication and failover across regions and clouds.

Checkpoint outcomes

  • Direct Lake reports in production with reduced refresh overhead.
  • Warehouse queuing removed at peak by right sizing or multi cluster. 
  • Replication runbook tested for a region failover scenario.

Phase 4: Operationalize analytics and AI

Wire near real-time pipelines for one operational KPI. In Fabric, use Real-Time Intelligence to ingest, transform, visualize, and trigger actions on events in the same platform. In Snowflake, publish governed datasets through secure sharing or listings so model and partner teams consume live data without new copies. Add cost and concurrency tuning as an ongoing practice.

Checkpoint outcomes

  • Streaming KPI live with dashboard and alert playbooks.
  • Zero copy distribution enabled through Snowflake sharing or listings.

 

Conclusion

Legacy warehouses designed for nightly batch, single vendor stacks, and scale-up appliances are now a brake on growth. What changes outcomes is not a new badge on the box but two architectural shifts you can operationalize today. Decoupling compute and storage removes resource contention and gives every team predictable performance. A unified data plane with built-in governance shortens the path from raw events to trusted decisions and preserves auditability.

Snowflake and Microsoft Fabric enable these shifts in complementary ways. Snowflake brings multi-cloud elasticity, isolated concurrency, and zero-copy distribution of governed data. Fabric brings OneLake, Direct Lake, real-time processing, and integrated governance in a single SaaS experience. Used on their own or together, they turn data estates into foundations for scalable analytics, AI readiness, and real-time decisioning.

If you are considering a Snowflake first, Fabric first, or hybrid approach, Trinus can help assess your current estate. Contact the Trinus expert team today to begin the assessment.

 

FAQs

1. We run Oracle Fusion and some EBS. Should we pick Snowflake or Fabric, or both?

If your priority is multi-cloud reach, governed data sharing with partners, and clean concurrency at peak, start Snowflake first. If your priority is one SaaS plane for ingestion, lakehouse, real-time, and Power BI without duplicate copies, start Fabric first. Many enterprises run a hybrid: Snowflake as the enterprise backbone for sharing and distribution, Fabric for domain self-service via OneLake and Direct Lake.

2. How do we add real-time decisioning without rewriting every pipeline?

Begin with one KPI that truly needs freshness, such as order risk or cash position. Enable change data capture from the source, land events in your lake, and publish the metric through Fabric Real Time Intelligence or a small, isolated Snowflake workload. Keep the rest of your batch ELT as is for now. Prove alert accuracy, then expand to the next KPI.

3. What governance steps are non-negotiable on day one?

Define domains, roles, and access early so teams do not share credentials. Turn on catalog and lineage, apply sensitivity labels, and ensure labels flow into reports. Isolate compute by team to avoid noisy neighbors, set cost and concurrency guardrails, and run a basic region failover or recovery drill so you know the process before an incident happens.