Data Engineering
and Analytics
We turn data into value – from ingestion to dashboards. Fast, understandable and secure.
Why professional Data Engineering?
The direct path from raw data to decisions – reliable, scalable and compliant.
Scalable architecture
Grows with your needs – from gigabytes to petabytes, in the cloud or on-prem.
- Horizontal scaling
- Cloud-native components
- Cost-efficient storage tiers
- Multi-cloud options
Real-time capability
React immediately: recommendations, alerts, or operational decisions.
- Low latency
- Event-driven processes
- Complex event processing
- Monitoring & alerts
Clean, trustworthy data
Quality builds trust: clear rules, checks and responsibilities.
- Automated checks
- Traceable lineage
- GDPR-compliant processing
- Data catalog & metadata
BI people actually use
Dashboards and reports your teams understand – with self-service where it fits.
- Easy to use
- Automated reports
- Mobile friendly
- Shared workspaces
Data governance & quality
Clear ownership, transparent data flows and verified quality – the foundation for trust.
Ownership & catalog
Compliance & security
Quality & observability
Modern data technologies
Proven, extensible and tailored to your needs.
Ingestion & transformation (ETL/ELT)
Pipelines that are stable, tested and designed to grow. Your data lands cleanly and traceably where it is needed.
Apache Airflow
Orchestrates and schedules workflows
Apache Spark
Fast processing of large data volumes
Kafka Streams
Transformations directly in the stream
dbt
Versioned SQL models with tests
Talend
Enterprise-grade graphical integration
Apache NiFi
Visual flows with fine-grained monitoring
Storage & data model
From data lake to warehouse/lakehouse: clean models, separated compute/storage and sensible cost tiers.
Amazon Redshift
Cloud warehouse for reporting
Snowflake
Elastic, multi-cloud ready
Google BigQuery
Serverless warehouse with ML features
Delta Lake
ACID tables on the data lake
Apache Iceberg
Open table format for very large data
Databricks Lakehouse
Lake + warehouse for BI and ML
Real-time & events
Low latency, reliable processing and exactly-once semantics – production-grade.
Apache Kafka
High-throughput event streaming
Apache Flink
Stream processing with very low latency
Amazon Kinesis
Managed streaming on AWS
Apache Storm
Distributed real-time computation
Redis Streams
In-memory streams with persistence
Apache Pulsar
Cloud-native messaging & streaming
Analytics & BI
Self-service for business teams and deep-dive analytics for data teams – with roles, permissions and governance.
Tableau
Self-service BI & visualizations
Power BI
Microsoft-centric BI
Looker
Semantic models & Git workflows
Apache Superset
Open-source visualization
Jupyter
Interactive analytics
MLflow
Experiment tracking & model registry
Our implementation process
From analysis to operations – transparent, step by step.
Kickoff & target picture
We clarify your data questions, assess sources and define a realistic target state.
Architecture & model
We design platform and data model – with security, governance and capacity that grows with you.
Build pipelines
Robust ETL/ELT with tests, monitoring and automated error handling.
Analytics & BI
Dashboards, metrics and self-service – tailored to your teams.
Optimization & ops
We keep the system healthy: performance, cost, alerts – including SLAs & 24/7 if needed.
Industry-specific use cases
Examples that deliver measurable impact – tailored to your context.
Understand customers & manage stock
Personalization, forecasts and smart pricing – across channels.
See risk, prevent fraud
Fast scoring and compliant reporting – in real time.
Keep assets & quality under control
Maintenance before failure, stable processes, transparent supply chains.
Improve care with data
Improve outcomes, plan resources, measure results.
Frequently asked about Data Engineering
Clear, practical answers.
When is a lakehouse worth it?
When BI/reporting and data science should work on the same data and you want to grow flexibly. You combine low-cost object storage with reliable ACID tables.
How do we ensure data quality?
Automatic checks in every pipeline, versioning, clear ownership, alerts on deviations and targeted error handling.
Which tools are best for real time?
Kafka/Kinesis for events, Flink for fast processing, and suitable OLAP stores for queries. The choice depends on latency, volume, cloud and team skills.
Do you provide ops & support SLAs?
Yes – depending on your needs, e.g. up to 99.95% availability, defined response/recovery times, 24/7 monitoring and regular security updates.
Ready for data-driven success?
We take your data platform to the next level – efficient, secure and measurable.