Real-Time Data & Streaming
Turn events into information when it matters.
Some processes cannot wait for the next overnight data run. We build event-driven data flows for applications, analytics and operational decisions. Low latency is only part of the task: formats, ordering, retries, error handling and operations must work together. We define the data freshness your process needs and how to measure it.
Your options
Services that move your project forward
Define timing requirements and business context
We identify relevant events and when they should lead to an action or analysis. Business time references and measurable requirements are agreed together.
Fresh data is delivered where it provides a clear benefit.
Design events and data contracts
Schemas, business meaning, identifiers and changes are defined with producing and consuming teams. Compatibility follows agreed rules.
Data flows remain understandable even with the further development of several systems.
Integrate sources and consumers
Appropriate interfaces, connectors, and platforms are selected based on the environment. Kafka and alternative messaging services are evaluated based on throughput, usage, and operational requirements.
The technical integration fits sources and target applications.
Handle errors and retries
We explicitly account for duplicate, late and invalid events. Processing, backlogs and controlled replay receive appropriate procedures.
Exceptions are part of the design and can be handled during operation.
Implement processing and data delivery
We develop transformation, enrichment and delivery for the agreed data flow. Business results and technical states are checked together.
The consuming applications receive defined and verified data.
Prepare IT operations and observability
Throughput, delay, errors and dependencies become visible with suitable signals. Experience with messaging, Prometheus and Grafana from the SYNEDAT PLATFORM supports a comprehensible operating model.
Your team recognizes bottlenecks and can react in a targeted manner.
Where to start
Real-Time Data & Streaming Use cases
Three example situations show how we can help.
Operational decisions require more up-to-date data
We check which events need to be available more quickly and what processing is required for this.
Multiple applications respond to the same changes
A coordinated event model can reduce coupling and clarify responsibility between the teams involved.
An existing data stream causes errors that are difficult to explain
We examine ordering, duplicates, backlogs and retry behavior within a defined scope.
From requirements to results
A clear process with agreed milestones
Record events and requirements
Sources, consumers, data volumes and required freshness are specified.
Design contracts and processing
We agree schemas, delivery guarantees, error handling and technical options.
Implement and verify the data flow
A limited flow is tested with representative loads, failures and business cases.
Hand over operations and plan expansion
Signals, instructions, responsibilities and next integrations are documented.
Your benefit
What you receive
- A coordinated event and interface model for the agreed scope.
- An implemented data flow with documented processing and error handling.
- Results from the agreed functional, load and replay tests.
- Operational documentation, monitoring and knowledge transfer for your team.
Ways to work with us
Choose a starting point that fits your needs. We agree the scope and required effort in a tailored proposal.
Streaming and event check
For an informed decision: freshness requirements, data flows and technical options.
Request a quote: Streaming and event checkBuild and validate an initial data flow
For a practical start: defined source, recipients and error cases.
Request a quote: Build and validate an initial data flowImprove an existing streaming environment
For existing environments: additional integrations, better observability, and aligned operating procedures.
Request a quote: Improve an existing streaming environmentSYNEDAT PLATFORM
Platform experience for your project
We use these selected tools in SYNEDAT PLATFORM or its delivery processes. We adapt suitable practices to your project and align their integration with your existing systems.
Deployment and platform automation
Kubernetes · Azure Kubernetes Service · Helm · Argo CD · Terraform
Versioned configuration and declarative deployment connect infrastructure and applications. GitOps makes proposed changes reviewable and the desired state explicit. Operational transitions and recovery procedures are still planned for the specific application.
Repeatable changes and clearer responsibility boundaries.
Observability and operations
Prometheus · Grafana · Alloy · Loki · Tempo
Metrics, logs and traces provide different views of applications and platforms. We organize data sources, dashboards and alert paths around specific operating questions. Retention, sensitive data and costs are considered when planning data collection.
Better incident diagnosis and informed operating decisions.
Data, search and messaging
MySQL · Redis · RabbitMQ · Solr · OpenSearch
Data storage, caching, messaging and search have different consistency, access and recovery requirements. We select and connect these components around your business processes. Ownership and maintenance are part of the integration.
Technical components that fit your data and application processes.
Questions before you get started
What does real-time mean in a data project?
The term needs a specific goal. Depending on the process, milliseconds, seconds or a few minutes may be sufficient. We define measurement points and acceptable delays for all processing so that requirements become verifiable.
When does streaming make more sense than batch processing?
If current events need to trigger an evaluation or action in a timely manner, streaming can be useful. Regular larger processing runs remain suitable for other tasks. Both methods can work together in one architecture.
Is Kafka always necessary?
No. Choices are based on event model, throughput, retention, integrations, and IT operations. Kafka may be a suitable foundation; other messaging or cloud services may be a better fit for specific needs.
How are duplicate events handled?
The processing receives appropriate identifiers and rules for repetitions. Depending on the use case, events that have already been processed are detected or steps are designed in such a way that repetitions do not have undesirable consequences. This is tested with specific cases.
Can the order of all events be guaranteed?
Ordering guarantees depend on the platform, partitioning and processing. A global order is a specific requirement with technical consequences. We identify the business keys for which order matters and how to verify it.
What happens if data is incorrect or late?
Detection, quarantine, correction and reprocessing are planned around the business process. Technical failures and invalid business data may require different handling. Ownership and recovery procedures remain documented.
Can existing databases be integrated?
Depending on the system, interfaces, change logs or suitable connectors are available. Effects on the source, authorizations and consistency are checked. The specific connection is technically evaluated before a commitment is made.
How can running costs be controlled?
Data volume, retention, processing, networking and the operating model are considered together. Measurable load assumptions and a limited pilot support planning. The proposal describes consumption costs and engineering services.
Discuss your next step
Where does your data come too late for the next decision?
Describe the source, consumer and required timing. We will define a suitable first data flow and measurable objectives.
Real-Time Data & Streaming
Your next step
Tell us what you need. We will route your enquiry to the right team and discuss the next steps with you.
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