Source system integration
Connect APIs, databases, manufacturing systems, files and event streams with explicit ownership and data contracts.
Data & AI Engineering
Data Engineering: Turn fragmented data into dependable systems
Enterprise data rarely fails because it does not exist. It fails at interfaces, definitions and operational handovers. We develop data pipelines, models and platforms that reliably supply analytics and applications.
The challenge is not just the tool
Data from ERP, CRM, production and specialist systems is often copied into manual reports or fragile scripts. Definitions drift, data arrives late and teams spend time reconciling numbers rather than improving processes.
We start from the consumer: which decision or application needs the data, at what latency, under which access rules? That determines the integration pattern and architecture. A new data platform is an option, not a predetermined answer.
What we engineer
Pipelines and orchestration
Implement batch or streaming ingestion with retries, idempotency, schema handling and auditable transformations.
Platforms and data products
Design warehouse, lakehouse or distributed solutions around actual access, volume, latency and operating requirements.
Models and quality rules
Define business entities, keys, history and automated validation to keep consuming applications consistent.
Monitoring and operations
Track freshness, failures, processing costs and upstream changes with clear recovery ownership.
Architecture and trade-offs
A warehouse is suitable for many structured reporting needs. Heterogeneous analytical and machine-learning workloads may justify a lakehouse or distributed data products. The chosen design should match change frequency, ownership, cost and the existing technology landscape.
Batch is often sufficient when decisions are made daily or weekly. Streaming only helps when the process benefits from lower latency. Data governance and security requirements must be enforced at the storage, API and consumption layers, not solely in documentation.
From the requirement to production
01 · Define the data consumer
Identify the business decision, source information, freshness and quality targets.
02 · Inspect the sources
Examine schemas, identifiers, interfaces, access rules and operational constraints.
03 · Design the architecture
Choose storage, processing patterns, contracts and integration boundaries.
04 · Deliver the pipelines
Build, test and connect transformations to consuming systems.
05 · Operate and improve
Monitor availability, freshness, costs and data quality in production.
Related capabilities and use cases
Turn reliable data into business metrics and operational reporting.
Define access, ownership and enforceable quality controls.
Connect machines, ERP and MES in a practical manufacturing workflow.
Frequently asked questions
Do we need a new data platform first?
Not necessarily. Existing integration services and storage may already fit the business requirements. We evaluate changes only when they deliver a clear benefit.
When should we use streaming?
When an operational process benefits from near-real-time data. For many analytical tasks, robust batch processing is simpler and less expensive.
How do you control data quality?
With contracts, schema validation, business rules, freshness monitoring and responsibility for correcting failed records.
How does Data Engineering differ from Analytics?
Engineering makes data available and dependable. Analytics turns it into consistent metrics, analysis and decisions.
Explore a related capability
Build reliable warehouse, lakehouse and data-product architectures.
Technical discovery
Which data flows are currently unreliable?
We review current systems, technical boundaries and a practical first implementation step.
Aleksandar Basara
Managing Director
aleks@valanor.co
+49 (0) 711 16 44 6 83
Lindenspürstrasse 22, 70176 Stuttgart
