Now GA: Share Materialized Views and Streaming Tables with Delta Sharing

We are excited to announce the General Availability (GA) of Materialized View & Streaming Table (MV/ST) Delta Sharing, a powerful set of capabilities that simplifies and expands how data teams collaborate within your organization and with external partners and customers.

Many of you first explored these features during our Public Preview—now, we have incorporated your feedback and delivered additional capabilities in GA.

MV/ST Delta Sharing Primer

Sharing data—whether in real time or as aggregates—comes with challenges:

• Today, teams are forced to build redundant pipelines and rely on outdated, batch-processed sources, leading to increased costs, greater complexity, and significant data latency.
• Sharing raw tables can expose sensitive information unintended for the recipient.
• Sharing aggregate data requires extra processing, thereby slowing data delivery.

Ultimately, balancing freshness, performance, security, and simplicity is hard, and older architectures rarely get it right.

Using the open‑source Delta Sharing protocol, MVs and STs can be shared across clouds, regions, and platforms to a wide range of recipients.

Materialized Views (MVs) deliver precomputed, aggregated query results, allowing teams to share only the necessary insights instead of full raw datasets—improving security and relevance. This is especially valuable when consumers need filtered or summarized results but not detailed source data, such as sharing daily industry‑level performance summaries from financial transactions with a hedge fund customer.

Watch this demo to see how a data provider can share MV with both Databricks users and other platforms.

Streaming Tables (STs) are built for continuous, real‑time ingestion—ideal for operational dashboards, live inventory tracking, or IoT monitoring. Sharing STs gives data consumers live, always‑fresh data without duplicating pipelines. For example, a retailer could share real‑time sales data directly with a logistics partner.

Watch this demo to see how a data provider can share ST with both Databricks users and other platforms.

What’s New in MV/ST Sharing GA?

1. Share Views Built on Top of an MV/ST

Providers can now define and share custom views directly on top of their MV/STs. This enables them to tailor what each vendor, supplier, or partner sees—such as delivery performance metrics or live inventory figures—without duplicating data or exposing unnecessary details.

Example: A truck manufacturer can share specific, real-time inventory views for each supplier, eliminating the need for multiple custom pipelines.

2. Create Views on Shared MV/ST Data

Recipients can create views directly on shared MV/STs, allowing tailored analytics without duplicating data.

Example: A sales manager can filter a shared transaction MV for their region and month-to-date results, enabling relevant analysis using always up-to-date data.

3. Build recipient-side pipelines on top of Shared MV/STs

Data recipients can create new materialized views or streaming tables derived from the shared data —no redundant pipelines or data copies needed.

Example: An auto parts supplier receives a shared sales MV from a manufacturer and can build a new MV for regional sales, focused only on their own operations.

4. Advanced Sharing with Column Mapping (CMs)

Providers can share MVs or STs using column mapping for flexible schema management. This enables providers to rename or hide columns, adapt schema to partner requirements, and perform metadata-only changes—making it easier to update, customize, and manage tables without costly data rewrites or impacting performance.

Example: A multinational retailer shares a sales MV with regional partners. Using column mapping, they can rename “product_id” to “SKU” for partners whose systems expect that field, and hide columns containing internal business codes. As a result, each partner seamlessly receives data in the expected format and only accesses the columns needed for their workflow.

5. Join or Union Multiple Shared MV/STs

Recipients can join or union multiple shared MVs or STs to enable unified analysis across data domains, vendors, or businesses.

Example: An automotive firm can aggregate inventory STs from various suppliers for a real-time supply chain dashboard, or join these with quality MVs for integrated defect tracking. This streamlines cross-partner analytics, eliminates data silos, and removes the need for custom data pipelines.

6. Join/Union Shared and Local MV/STs

Recipients can enhance shared data by joining it with their own internal MVs or STs, allowing them to contextualize external data within their proprietary models and reports.

Example: A logistics partner can join real-time sales STs from a retailer with internal routing and warehouse MVs to optimize delivery, or merge external metrics with internal KPIs for comprehensive reporting and dashboards.

How Reltio uses Streaming Table Sharing

Reltio Data Cloud™ delivers trusted, real-time, context-rich data across domains—providing 360° views of customers, products, and suppliers. Trusted by global enterprises, Reltio powers innovation, reduces risk, and enables agentic AI workflows.

How Joint Customers Previously Consumed Reltio Data in Databricks
To use Reltio’s data in Databricks, customers traditionally relied on the Reltio Data Pipeline for Databricks. It enabled Reltio’s customers to export their data from Reltio, and then consume it in Databricks for their downstream processes. For example, a life sciences company streams healthcare provider and organizational data to power processes such as CRM, rebate management, and field enablement. Another global pharmaceutical company replaces slow, manual batch exports with real-time streaming, leading to faster analytics in clinical trial planning and sales operations.

Challenges with the Previous Approach

• Duplicated data and extra storage costs from exporting and copying datasets.
• Managing access controls on the data copies added to operational overhead and governance complexity.

How MV/ST Sharing Solves These Challenges
With MV/ST Sharing now generally available, Reltio can instantly share streaming tables and materialized views with customers in real time with no data copying required—eliminating export pipelines and duplication. Customers receive curated, high-quality datasets directly in Databricks and are ready to power advanced analytics, AI/ML, real-time personalization, and operational reporting with minimal setup.

Sharing Materialized Views and Streaming Tables with Delta Sharing lets our customers securely access the most current, insight-ready data from Reltio-empowering faster decisions, more accurate analytics, and greater agility without the headaches of traditional data exports or integrations. — Ansh Kanwar, Reltio Chief Product Officer

MV/ST Sharing is now generally available. Whether you’re sharing live data streams or pre-computed results, please give it a try!

Get started

• Read the Streaming Table documentation to start with Streaming Table Sharing
• Read the Materialized View Sharing documentation to start with Streaming Table Sharing
• Watch the Delta Sharing in Action: Architecture and Best Practices from Data and AI Summit 2025
• Watch the What’s New with Data Sharing & Collaboration session from Data and AI Summit 2025 to learn more about the latest announcements in Data Sharing