Real-time data that reaches the process .
We connect machines, shops and core systems through Apache Kafka into a real-time data backbone – the basis for live analytics, automation and operational AI. Running on Confluent Cloud or in your own environment.
- < 1 s
- from event to reaction
- 300+
- data & AI projects
- 100 %
- infrastructure as code
- 24/7
- streaming operations
One data backbone instead of nightly batch runs
Many decisions come too late because data only reaches reporting the next morning. Kafka decouples systems and delivers events the moment they happen – connected once, used many times.
Systems cleanly decoupled
Instead of point-to-point interfaces between ERP, MES, shop and CRM there is one bus. New consumers can be added without touching the source systems.
Events instead of snapshots
Every status change is preserved as an event. Analyses can be replayed at any time – even retrospectively for new questions.
Contracts through schemas
Schema registry and data contracts prevent a single field change in a source system from quietly breaking half of your analytics.
Streaming and lakehouse together
The same events land in operational systems and in the lakehouse – real-time control and analytics work on one truth.
What we build for you with Confluent & Kafka
From the first topic map to monitored continuous operations: one team for platform, integration, stream processing and the use cases on top.
Platform & operations
A cluster setup that considers security, cost and availability from day one.
- Confluent Cloud, Platform or self-managed – decided by your needs
- Networking, private link and encryption
- Terraform modules for clusters, topics and permissions
- Monitoring, alerting and runbooks
Integration & connectors
Connect source systems without rebuilding your core systems.
- SAP, Oracle and MS SQL via change data capture
- Kafka Connect for Salesforce, shop systems and APIs
- IoT and MES data via MQTT and OPC UA
- Sinks into lakehouse, data warehouse and search index
Stream processing
Logic in the data stream instead of the nightly batch.
- Apache Flink and Kafka Streams for aggregation and joins
- Rules, thresholds and anomaly detection live
- Enrichment of events with master data
- Exactly-once processing for critical processes
Governance & data contracts
So the data backbone does not turn into chaos.
- Schema registry with compatibility rules
- Topic naming concept and ownership per domain
- Roles, ACLs and audit trail
- Data protection: masking and retention periods
Real-time use cases
The bus is a means to an end – success is measured in the process.
- Live monitoring of assets and scrap rates
- Stock levels and delivery capability in real time
- Next best action and fraud detection in the moment
- Operational AI models directly on the event stream
Migration & cost control
Replace existing interfaces without putting operations at risk.
- Replacing file transfers and point-to-point connections
- Migration from self-managed Kafka to Confluent Cloud
- Sizing of throughput, partitions and retention
- Cost reporting per domain and enablement of your team
Why companies build on Kafka with us
We build streaming architectures where they pay off – in manufacturing, retail, logistics and finance – and connect them to lakehouse and BI.
- Data engineers and solution architects with Kafka, Flink and cloud experience in a permanent team.
- We advise against streaming when a batch process is enough. Real time costs operations – the use case has to justify it.
- Streaming, lakehouse and reporting come from one team, without handover losses between vendors.
- Enablement is part of the deal: in the end your team runs the platform itself.
Typical streaming projects from our practice
Machine data live on the shop floor
OPC UA events from production flow through Kafka into dashboards and rule sets – deviations become visible during the running shift.
Reaction in seconds instead of the next day
Inventory across all channels
ERP, shop and warehouse events are merged into one consistent stock view and pushed back to all sales channels.
Fewer oversells and corrections
SAP changes without a nightly run
Change data capture continuously moves documents and master data into the lakehouse – the nightly extraction run is gone.
Reporting without delay
From the first topic to a productive data stream in 90 days
Use case & event map
We clarify which processes truly need real time and cut domains, topics and responsibilities.
Cluster & governance
Confluent setup, networking, permissions, schema registry and CI/CD as code – production ready.
First stream in operation
One source, one processing step, one consumer – live, monitored and with measurable value in the process.
Scale & hand over
More domains and use cases, operational routine, cost control and training for your team.
Foundation first, real-time stream second?
Without a solid data platform, streaming stays patchwork. We build both – a lakehouse on Databricks or Fabric and the event backbone aligned to it.
Frequently asked questions
Do we really need real time?+
Not everywhere. For each use case we check whether faster data actually improves the decision. Where an hourly run is enough, we do not recommend streaming operations.
Confluent Cloud or self-managed?+
Confluent Cloud saves operational effort and is usually the faster route. With strict network or location requirements we run Confluent Platform on your side – the architecture stays the same.
How do we connect SAP?+
Via change data capture or certified connectors. The source system stays untouched, load stays controlled, and documents are available continuously instead of overnight.
What happens during an outage?+
Events remain stored in the cluster and are processed after the incident. We design retention, replication and recovery so that no message is lost.
45 minutes of straight talk about your real-time plans.
We look at your systems and processes, name the most rewarding real-time use case and sketch the architecture behind it.
- Result: target picture, architecture sketch and effort estimate
- An honest assessment of whether streaming or batch fits better
- Free and non-binding