Data Architecture, Lakehouse & Data Mesh
A data foundation that supports your decisions.
When metrics conflict and every new report needs separate interfaces, the data architecture deserves a closer look. We connect your business goals with data sources, processing, access and ownership. Whether you have an existing warehouse, a lakehouse or a more distributed organization, the aim is a usable architecture with documented decisions and a realistic implementation path.
Your options
Services that move your project forward
Understand decisions and data needs
We identify the analyses, applications and processes to support. Data freshness, granularity, quality and user groups provide the starting point.
Architectural decisions are based on specific usage scenarios.
Capture sources and dependencies
We review existing systems, interfaces, datasets and owners. Conflicting definitions and critical dependencies become visible early.
You can see which foundations exist and where gaps remain.
Evaluate architecture options
Warehouse, lakehouse and hybrid approaches are compared based on your requirements. Storage, processing, IT operations, costs and existing competencies are all included in the assessment.
A documented choice balances technical suitability with practical delivery.
Plan data products and responsibilities
Important datasets receive defined users, quality requirements, interfaces and owners. We consider data mesh where business domain teams can take responsibility for their data products.
Data is provided with clear expectations and named contacts.
Integrate protection and IT operations
Access, data storage, monitoring and recovery are part of the architecture. Experience with data and platform services from the SYNEDAT PLATFORM is brought in according to the target environment.
The architecture takes into account the subsequent IT operation from the very beginning.
Develop a roadmap and test key assumptions
We prioritize actions, dependencies and a suitable initial data flow. A limited technical proof of concept tests important assumptions before broader implementation.
The target architecture leads to a concrete next step.
Where to start
Data Architecture, Lakehouse & Data Mesh Use cases
Three example situations show how we can help.
Reporting and AI need the same reliable data
We clarify common principles and different requirements for access, timeliness and processing.
An established warehouse needs to evolve
The assessment recognizes existing strengths. New components are considered against a specific business benefit.
Several departments develop their own data solutions
Common standards and clear ownership can support reuse while allowing business domains to address their own needs.
From requirements to results
A clear process with agreed milestones
Clarify goals and status quo
Usage scenarios, data sources and existing architecture are recorded together.
Agree architecture options and ownership
Technical options, data products and necessary responsibilities are evaluated.
Practical testing of assumptions
A defined data flow or prototype examines important integration and usage issues.
Hand over the roadmap and decisions
Architecture, priorities and next implementation steps are documented and discussed.
Your benefit
What you receive
- A documented target architecture with reasons for the key decisions.
- An overview of relevant sources, data products and technical dependencies.
- Defined requirements for quality, access, and operational responsibility.
- A prioritized roadmap and results from the agreed proof of concept.
Ways to work with us
Choose a starting point that fits your needs. We agree the scope and required effort in a tailored proposal.
Data architecture check
For orientation: usage scenarios, sources, bottlenecks and next decisions.
Request a quote: Data architecture checkTarget architecture and roadmap
For dependable planning: architecture options, ownership and prioritized implementation.
Request a quote: Target architecture and roadmapBuild the first data product
For practical proof: defined sources, usage and verifiable results.
Request a quote: Build the first data productSYNEDAT 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 is the difference between a warehouse and a lakehouse?
A warehouse typically focuses on structured data and analytical queries. A lakehouse combines functions for reliable table processing with flexible data storage. The right architecture depends on data types, usage, IT operations and existing systems.
Is Data Mesh a specific product?
Data mesh is primarily an organizational and architectural model with business domain ownership of data products and shared foundations. A new tool alone does not establish these responsibilities. We assess whether the required teams and processes are in place.
Can the existing platform continue to be used?
Yes. We evaluate existing components and investments in connection with the new requirements. A step-by-step further development can be useful. Changes are justified by specific usage scenarios.
How is a first data product selected?
A useful first data product has identifiable users, a clear benefit, available sources and a responsible business domain. Its scope should test important assumptions while remaining manageable. Acceptance criteria are agreed before implementation.
Does the data platform have to go to the cloud?
No. On-premises, cloud-based, and hybrid models are evaluated based on requirements and existing dependencies. Data storage, interfaces, operational expertise, and ongoing costs influence the decision.
How does the architecture work lead to implementation?
The architecture is linked to prioritized actions, named owners and an appropriate technical proof of concept. The engagement defines deliverables and the next decisions to make.
How are data quality and governance taken into account?
Important data receives agreed definitions, quality requirements and owners. Access, change processes and traceability are built into the architecture. More detailed implementation can follow as a separate engagement.
What influences the effort the most?
Variety of sources, documentation status, number of teams involved, technical dependencies and depth of testing are decisive. Architectural work, prototype and implementation are described in a comprehensible manner in the offer.
Discuss your next step
Which decision needs a stronger data foundation?
Describe your main use case and existing data sources. We will define a practical starting point for architecture and implementation.
Data Architecture, Lakehouse & Data Mesh
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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