Delta Sharing Alternatives: When You Need Real-Time Data Sharing
Delta Sharing is a strong choice for sharing tables and datasets with partners. But if your partners need live events, positions, or payments as they happen, keep reading.
Delta Sharing is an open protocol created by Databricks for secure data sharing across organizations. A provider shares Delta Lake tables through their catalog, and recipients read them with the tools they already use: pandas, Apache Spark, Power BI, or their own Databricks workspace. No proprietary lock-in on the recipient side, no full-copy exports flying around as CSVs.
For analytical sharing, that model works well. Curated datasets, ML training data, reporting extracts, data marketplace listings: if the partner's question is "give me the current state of this dataset so I can query it," Delta Sharing answers it cleanly.
The searches for "Delta Sharing alternatives" usually come from a different place. The need shifts when what you share is a stream: payment events a partner must react to in seconds, vehicle or shipment positions updating continuously, inventory changes that drive a partner's replenishment logic. Shared tables can be read incrementally, but these are events a partner subscribes to with a stream-native client, not a dataset they query.
What table sharing solves
A partner needs a dataset: current state, queryable, refreshed on a schedule. Analytics, BI dashboards, model training, reconciliation. Delta Sharing and similar data exchange protocols were designed for exactly this.
Where stream sharing fits better
A partner needs to react to events as they occur: a payment authorized, a truck rerouted, a stock level crossing a threshold. Kafka-native consumption gives them a long-running subscription to every event, rather than reads over a shared table.
These are architectural characteristics, not a scorecard. The two models are built for different data shapes, and each is the right tool for its shape.
| Dimension | Batch table sharing (Delta Sharing) | Real-time stream sharing (Conduktor Partner Zones) |
|---|---|---|
| Data shape | Tables and datasets: rows the recipient queries | Event streams: continuous records the recipient subscribes to |
| Freshness | As fresh as the latest table version or refresh cycle | Continuous: partners receive events as they're produced |
| Consumption model | Query snapshots, change feeds, or Spark streaming over shared tables | Kafka-native clients continuously consume records from shared topics |
| Partner access | REST-based protocol via pandas, Spark, Power BI, or Databricks | Standard Kafka clients (Java, librdkafka, any language) |
| Governance & masking | Catalog-governed table and view controls; row/column masking support depends on the sharing mode | Per-partner field-level masking and encryption applied at the wire, on the same source topic |
| Partner-side infrastructure | A query engine or connector that speaks the protocol | Any Kafka client; no replica cluster or pipeline on either side |
| Typical use cases | Analytics, BI, ML training, reporting, data marketplace products | Operational integration: payments, logistics tracking, live inventory, telemetry |
Start from the data shape and the partner's job, not from the tooling. If your case is analytical, Delta Sharing is a good answer and you should use it.
Delta Sharing: partners query, they don't react
Your partner loads data into a warehouse, a notebook, or a BI tool and runs queries. Scheduled freshness (hourly, daily) is fine. Curated datasets, training data, data marketplace products.
Delta Sharing: both sides live in the lakehouse
The recipient already works in Databricks or Spark, governance runs through a catalog, and the sharing relationship is fundamentally "here is a dataset."
Delta Sharing: the data is naturally batch
Monthly statements, end-of-day positions, aggregated metrics. Turning batch data into a stream adds complexity for no benefit.
Stream sharing: partners react to events
Payment authorizations, fraud signals, shipment positions, stock movements. A table refreshed every hour arrives too late to act on.
Stream sharing: no export pipeline to build
Partners consume from the live source of truth. No ETL job that dumps streams into tables, no replica cluster, no per-partner integration code.
Stream sharing: different views of one stream
One partner sees full records, another sees masked fields, both from the same topic, with access removed by deleting the partner's zone when a contract ends.
Many organizations end up with both: streams for operational integration, tables for analytics. The same events that partners consume live can land in Delta tables for the analytical sharing relationship.
Conduktor Gateway is a wire-level proxy that sits in front of your existing streaming infrastructure. Partner Zones are its answer to external sharing: partners consume live streams directly, under your rules, without any data being copied.
- No replica, no pipeline. Partners read from your existing cluster through an isolated zone. There's no export job, no mirrored cluster, no secondary copy of the data to secure and reconcile.
- Per-reader masking and encryption. Field-level masking and encryption are applied per partner at the proxy. Two partners consume the same topic and see different views of it, enforced before data leaves your organization.
- Standard clients on the partner side. Partners connect with any standard Kafka client. No SDK, no API wrapper, no protocol translation layer to build or maintain.
- Centralized audit evidence. Gateway audit and Partner Zone events centralize access and configuration activity, and per-zone traffic analytics track bytes, records, and consumer lag: the evidence trail compliance teams ask for before approving a data sharing or data monetization deal.
- Clean offboarding. When a partnership ends, delete the Partner Zone and partner access is removed; credentials are time-limited, so keep TTLs tight for fast cutoff. No provider-side replica or mirrored cluster remains to clean up. Data a partner already consumed sits on their side, as with any sharing protocol.
These guarantees hold when the Gateway is the only network path to the brokers; the proxy sits inline on the data path, so treat it as critical infrastructure (HA, capacity) like the cluster itself.
This is the streaming counterpart to what Delta Sharing does for tables: share without copying, govern per recipient, keep control at the provider. It's part of a broader data streaming platform that covers governance, security, and self-service on the infrastructure you already run.
Share live streams with partners, without copying data.
Partner Zones give external consumers governed, real-time access to your streams: per-partner masking, centralized audit evidence, clean offboarding. See it on your own use case.
Is Delta Sharing real-time?
Delta Sharing shares tables, and recipients see data as of the latest shared table version. Providers can update tables frequently, and Spark can even read a shared table as a streaming source, so freshness can reach minutes. But the unit of sharing remains the table: it's a pull model over datasets, not a push model over events. For partners that must react to individual events in seconds, a stream-native approach fits better.
Can I use Delta Sharing and real-time stream sharing together?
Yes, and many organizations should. Delta Sharing covers the analytical relationship: curated datasets, reporting, ML training data. Partner Zones cover the operational relationship: live events partners react to. The same source events can feed both, so the two are complements, not competitors.
What do partners need to consume a shared stream?
A standard Kafka client and the credentials you issue for their Partner Zone. They point the client at your Conduktor Gateway endpoint and consume. No custom SDK, no integration library, no infrastructure on their side beyond the client they likely already run.
How is access controlled in real-time stream sharing?
Each partner gets an isolated zone with its own credentials, an explicit list of accessible topics, per-partner rate limits, and field-level masking or encryption rules. Access and configuration activity is logged centrally. When the relationship ends, delete the zone; credentials also expire by TTL, and since no provider-side copy was created, there's no replicated infrastructure to clean up.