Real-Time Data Sharing
Turn your streams into a partner-facing product.
Partners read live data from the Kafka you already run. Each one gets an isolated zone with its own credentials, and sees only the fields you choose to share.
Sharing Kafka data externally usually means a second cluster
A DMZ cluster, a MirrorMaker pipeline per partner, and engineering time for every new integration. It holds up while the partner list is short.
Every partner adds infrastructure to run
A replica cluster and a replication pipeline per partner. You patch and monitor each one whether or not the partner is consuming from it.
One topic, different views per partner
Partners need different fields from the same topic. Without per-reader filtering, the options are duplicating topics or writing a custom filter for each consumer.
No single record of who accessed what
Access lives in each partner's cluster config. When compliance asks for evidence of what a partner could read last quarter, someone reconstructs it by hand.
Partner Zones
Partners consume directly from the Kafka cluster you already run, through an isolated zone. The proxy handles the separation, so there is no second cluster to stand up and no replication to operate.
Field-Level Masking & Encryption
Share what partners need, mask what they don't. Masking and encryption run as Gateway interceptors scoped to each zone, before data leaves your network. Two partners can read the same topic and see different fields.
Audit Trails
Connections, authentications and denied requests are logged by default. Add the audit interceptor to record produce and fetch operations per topic, and export the trail to your SIEM for GDPR, HIPAA, SOC 2 and PCI-DSS reviews.
Scoped Access Control
Define which topics a partner can reach, and apply produce and consume rate limits per zone. Ending a partnership means deleting the zone: access goes with it, and nothing was replicated, so there is nothing to clean up.
Native Kafka Protocol
Partners connect with the Kafka client they already use. If they can consume from Kafka, they can consume from a Partner Zone.
Topic Aliasing
Expose a topic under a name that means something to the partner, without renaming anything internally. Inject or strip message headers per zone.
How Data Sharing Works
Every step happens on your side of the network. The partner receives credentials and a bootstrap endpoint.
Create a Partner Zone
Provision the zone in Console or through the API. It carries its own credentials, its own topic list, and the rate limits you set for that partner.
Select and Shape the Data
Choose which topics to expose, and under which names. Apply field-level masking and encryption so each partner reads only what they are authorized to read.
Connect with Standard Kafka Clients
Share the zone credentials with the partner. They point a standard Kafka client at Conduktor Gateway. No integration work on either side.
Monitor Usage per Partner
Console tracks ingress and egress bytes per zone. That is the number behind usage attribution and renewal conversations.
Measurable Impact
Real results from teams sharing real-time data with partners on Conduktor.
$150K+ Saved Per Partnership
Partners consume from your existing cluster via Partner Zones. No replica clusters, no Cluster Linking, no custom APIs to build or maintain — whether you're replacing existing infrastructure or avoiding the cost entirely.
2-4 Week Partner Onboarding
What previously required months of per-partner engineering takes weeks. Reusable templates mean each new partner builds on the last, not from scratch.
2-3 Week Deal Approval
Automated audit logs and field-level encryption give compliance teams the evidence they need. Deals that stalled in review — or were blocked entirely — now close in weeks.
New Revenue From Data You Already Have
Real-time data sharing opens data monetization opportunities from inventory feeds, telemetry, transaction patterns, and other data that was previously locked inside your platform.
Approaches to Real-Time Data Sharing
When shared data feeds a report, batch exchange works fine. When it drives an operational decision (rerouting a shipment, blocking a transaction, updating a price), it has to arrive as a stream. Conduktor's data streaming platform governs that layer, and Partner Zones extend the same controls across organizational boundaries.
Warehouse-native technologies like Databricks Delta Sharing solve a different problem: sharing tables and datasets between lakehouses, at table granularity. They do that well, and many organizations run both layers. If you're evaluating where each fits, see Delta Sharing alternatives for real-time data, and real-time data clean rooms for privacy-preserving collaboration on live streams.
What is real-time data sharing?
Real-time data sharing gives external partners access to live event streams as they are produced, rather than periodic batch exports or table snapshots. With Conduktor, partners consume directly from your existing streams through isolated Partner Zones, with masking and encryption applied per reader.
How do we securely expose Kafka events to external partners?
Put Conduktor Gateway in front of the cluster and give each partner an isolated Partner Zone. The partner authenticates against the Gateway with credentials scoped to that zone, reads only the topics you exposed, and sees only the fields your masking rules allow. Nothing is replicated, and your internal topic names stay internal.
Do partners need their own Kafka cluster, or a special client?
No to both. Partners connect to your existing cluster through Conduktor Gateway using any standard Kafka client, librdkafka or Java included. No partner-side infrastructure is required beyond a consumer application.
How is shared data secured, and what evidence do compliance teams get?
Every Partner Zone enforces authentication and topic-level access control. Field-level masking and encryption run as Gateway interceptors scoped to the zone, applied before data leaves your network. Connections, authentications and denied requests are logged by default, and the audit interceptor extends that to produce and fetch operations per topic, which is the trail GDPR, SOC 2 and PCI-DSS reviews ask for.
Can different partners see different data from the same topic?
Yes. Masking and encryption interceptors are scoped per zone, so one partner reads full records while another reads masked fields, from the same source topic. For record-level filtering by field value, our team can walk through the right architecture for your data model. Book a demo to discuss.
How is this different from Databricks Delta Sharing?
Delta Sharing shares tables and datasets from a lakehouse, at table granularity, and it does that well. Conduktor shares live streams: partners see events within seconds of production. Many organizations run both, see Delta Sharing alternatives for a detailed comparison.
We already have a DMZ cluster and MirrorMaker setup. Does this replace it?
For new partnerships, Partner Zones remove the replica cluster and the replication pipeline from the diagram. For existing setups, our team can walk you through how other organizations have moved across.
How do I track what each partner is consuming?
Console tracks ingress and egress bytes per Partner Zone, refreshed every few minutes. That is the number behind usage attribution and renewal conversations.
What happens when a partnership ends?
Delete the Partner Zone and the partner loses access. Credentials issued to a zone expire on their time to live rather than being revoked one by one, so deleting the zone is the clean cut. No data cleanup is required because nothing was replicated.
Can partners produce data back into my Kafka cluster?
Yes. A Partner Zone can grant write access as well as read, with the same authentication and topic-level controls. Encryption applies on the write path, masking applies when data is read back out.
Transport & Logistics
OEM dealer networks, supplier integration, logistics partner feeds, and commercial data products.
Explore →Financial Services
Regulatory reporting, credit and risk exchange, and FinOps chargeback across partners, regulators, and clouds.
Explore →Retail & E-commerce
Supplier integration, carrier data feeds, marketplace connectivity, and loyalty partner data.
Explore →Read more customer stories
Swiss Post scaled Kafka to 800+ users and 5x more applications with governance, RBAC, and self-service.Read Swiss Post's story →
Bitvavo meets DORA and MiCA obligations with automated audit trails and governed access across its Kafka platform.Read Bitvavo's story →A leading European airline consolidated 25 Kafka clusters onto Confluent Cloud with Conduktor.Read the story →Talk through your partner architecture
If you're running a DMZ cluster and a pipeline per partner today, we can walk through what replaces it, and what stays.