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Databricks Delta Sharing: Enabling Cost Efficient Cross Cloud Data Access

Source: https://techcommunity.microsoft.com/blog/azurearchitectureblog/databricks-delta-sharing-enabling-cost-efficient-cross-cloud-data-access/4511400 Delta sharingis an open protocol for secure data sharing that allows organizations to share live data stored in Delta Lake with external consumers—across cloud providers such as Azure, AWS, and Google Cloud—while keeping the data in its original location. Consumers access the same up‑to‑date data without the provider having to copy or move it to another cloud.

In multi‑cloud environments, data sharing typically leads to increased storage duplication and cross‑cloud data transfer charges. Delta Sharing changes this model by exposing data in place, enabling access rather than replication.

Key architectural benefits include:

• Cross‑cloud data availability without replication Data producers share tables directly from their existing data lake, eliminating the need for extract‑and‑load pipelines.
• Reduced egress costs compared to copy‑based approaches Since data is not duplicated by default, Delta Sharing avoids large-scale data movement that typically drives cloud egress charges. Egress occurs only when data is queried across clouds.
• Secure and governed access Access is managed using fine‑grained permissions, auditability, and centralized governance through Unity Catalog, ensuring shared data remains secure and compliant.
• Vendor‑neutral and open Consumers do not need to run Databricks. Delta Sharing supports multiple clients such as Spark, Pandas, and BI tools, making it suitable for heterogeneous analytics ecosystems.

A common misconception is that cross‑cloud analytics always require data replication. With Delta Sharing:

• Live accessis used for shared analytics and reporting use cases
• **Replication (deep clone)**becomes an explicit design choice only when local copy or offline processing is required—rather than a default requirement

This gives architects the flexibility tooptimize for cost, performance, or isolation depending on the workload, instead of paying replication costs upfront.

Figure: Databricks Delta Sharing Architecture:

Delta Sharing enables secure, real‑time data access across public clouds without replicating data. Consumers query data in place through a secure control plane, ensuring governance, reduced operational overhead, and optimized egress costs.

This diagram visually represents Databricks Delta Sharing architecture with a clear left‑to‑right flow that works well for blogs and technical readers:

• Left– Data Producer (Azure Databricks + Unity Catalog + Delta Lake Storage)
• Center– Delta Sharing Service (secure control plane, metadata & access control)
• Right– Data Consumers (AWS / GCP / Spark / BI tools / Pandas)
• Bottom flow– Read‑only, on‑demand access withno data replicationandegress only on read

Overview

The diagram illustrates how Databricks Delta Sharing enables secure, real‑time data access across public clouds (Azure, AWS, GCP) without replicating data, helping reduce operational overhead and data‑egress costs.

At a high level, data remains in the producer’s cloud storage, while consumers in other clouds access it securely on demand using the Delta Sharing protocol

1️****⃣ Data Producer (Azure Databricks)

• TheData Providerhosts curated datasets in a Delta Lake (for example, onAzure Data Lake Storage Gen2).
• Data is governed usingUnity Catalog, which controls:

No data is copied or exported during sharing—datasets remain in the producer’s storage account.

2️****⃣ Delta Sharing Service (Control Plane)

• Delta Sharing acts as thesecure control layer, not a data storage layer.
• It manages:

The control plane ensures that only authorized consumers can discover and query shared datasets.

3️****⃣ Secure Network Access

• Consumers connect to the producer’s storage usingHTTPS-based secure endpoints.
• Depending on architecture:

All access occurs under the governance policies defined by the data producer.

4️****⃣ Data Consumers (AWS / GCP / Azure)

• Consumers may run:

Consumersquery the data in place, directly from the producer’s Delta tables

Importantly, consumers do not need to copy or store the data locally unless they explicitly choose to.

5️****⃣ Data Flow (Read‑Only, On‑Demand)

• When a consumer runs a query:

This “access‑instead‑of‑replicate” model avoids continuous data synchronization pipelines and minimizes unnecessary data movement.

6️****⃣ Cost Optimization and Egress Considerations

• No default data replicationmeans:
• Data egress charges occuronly when data is actually read across clouds, not upfront or continuously.

Compared to traditional copy‑based sharing, this significantly reduces overall egress exposure for many analytics workloads

7️****⃣ Optional: Local Replication (Explicit Choice)

• If required, consumers can perform adeep cloneto bring a local copy into their own cloud.
• This is anexplicit architectural decision, used only when:

Replication is optional — not a prerequisite for sharing.

Delta Sharing is particularly effective for:

• Sharing curated datasets across cloud platforms
• Enabling partner or third‑party analytics without data duplication
• Supporting centralized data platforms while allowing decentralized consumption
• Reducing operational overhead in cross‑cloud data ecosystems

Databricks Delta Sharing enables organizations to provide secure, real‑time cross‑cloud data access without data duplication—helping reduce operational complexity and egress costs in multi‑cloud architectures.