SYSTEM Cited by 1 source

Delta Sharing

Delta Sharing is an open protocol (originated at Databricks, 2021) for secure data exchange between parties without copying the data first. It is the exchange layer Databricks positions under systems/unity-catalog: UC is the catalog / governance plane, Delta Sharing is the bytes-on-the-wire plane between UC metastores or with any conforming external client.

Role in the Mercedes-Benz case study

Mercedes-Benz uses Delta Sharing in three deployment shapes, all on one protocol:

1. Cross-cloud / cross-hyperscaler — AWS (provider metastore) ↔ Azure (recipient metastore). The headline case; bridges after-sales data from AWS to dozens of Azure consumers.
2. Cross-region / cross-metastore inside one cloud — when regions or metastores inside one cloud need to exchange data products.
3. External sharing with partners / suppliers — framed as a more secure alternative to FTP drops or shared-secret exchange; recipient may or may not be on Databricks themselves.

Same wire protocol, three trust boundaries. (Source: sources/2026-04-20-databricks-mercedes-benz-cross-cloud-data-mesh)

Properties that made it fit

• Open. Open-source protocol + multiple client implementations (Databricks-native, pandas, Spark, Python, etc.); avoids a pure proprietary lock-in argument when talking to external partners.
• Incremental updates. A share isn't a snapshot copy; recipients can pull deltas as new data lands on the provider side. This is the property that makes the patterns/cross-cloud-replica-cache viable — without incremental semantics, a sync window would be "re-download 60 TB".
• UC-federated sources. Mercedes-Benz's tables were Iceberg on AWS Glue; UC federates them so they can be shared as Delta Sharing tables without the producer re-writing into Delta first. The format translation happens at the sharing boundary.

Pairing with Deep Clone for egress control

Direct Delta-Sharing reads across clouds are a live cross-cloud query; each read pays egress. For bulk, latency-tolerant consumers, the pattern Mercedes-Benz implemented is:

Provider (AWS) ─ Delta Share ─► Sync Job (Delta Deep Clone, incremental) ─► Local Delta replica (ADLS, Azure)
                                                                             │
                                                                             └► Local consumers (no cross-cloud hop per query)

Each sync pays egress once per window; all subsequent reads are local. See patterns/cross-cloud-replica-cache for the general shape.

Seen in

• sources/2026-04-20-databricks-mercedes-benz-cross-cloud-data-mesh — primary case study; cross-cloud AWS→Azure data mesh, three deployment shapes, pairing with systems/delta-lake Deep Clone for egress-bounded consumers.

Related

• systems/delta-lake — underlying table format Delta Sharing exchanges; Deep Clone is a Delta feature.
• systems/unity-catalog — the governance/catalog plane that speaks Delta Sharing.
• concepts/open-table-format — Delta Lake is one of the canonical implementations; Delta Sharing is its wire-level exchange protocol.
• concepts/data-mesh — Delta Sharing is an exchange-tier building block when a mesh spans trust / cloud / region boundaries.
• patterns/cross-cloud-replica-cache — canonical pattern Delta Sharing enables via incremental-update semantics.