Behind every successful data solution lies a solid technical foundation. From standard connectors and API integrations to connections with databases, applications and cloud services.
For architects, developers and the technically curious
This page shares technical information about the building blocks C-data applies in practice. We show which techniques and standards we use. You also get insight into how they fit within a robust, manageable data architecture.
ETL & Data pipelines
Extracting, transforming and loading data between systems. From simple integrations to complex multi-source transformations.
CloverDXPythonApache Sparkdbt
API integrations
Connections via REST, SOAP and GraphQL APIs for retrieving and writing data from external systems and services.
REST APIGraphQLSOAPOAuth
Database connections
Direct connections to relational and analytical databases via JDBC, ODBC and native drivers for reliable data throughput.
JDBCODBCSQLNative drivers
File formats
Support for all common structure and exchange formats for reading and writing data.
CSV / TSVJSONXMLParquetExcel
Cloud & hybrid
Integrations with cloud platforms and hybrid architectures, with data sovereignty and manageability at the centre.
AzureAWSGoogle CloudOn-premise
Security & governance
Encryption, access control, logging and audit trails as an integral part of every integration architecture.
TLS/SSLRBACAudit loggingGDPR
Technical architecture
How integrations fit into the data architecture
Every integration is part of a larger whole. C-data designs integrations that fit within a coherent architecture, from source to end users.
Consumption
BI, Dashboards & AI applications
TableauPower BIML models
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Storage
Data warehouse / Data lake / Analytical DB
ExasolSnowflakeAzure Synapse
↑
Integration
ETL / Transformation / Orchestration
CloverDXPythondbt
↑
Sources
Operational systems, APIs, files & cloud
ERPCRMREST APISQL ServerMariaDB
Integration approaches
Common integration patterns
Depending on your situation, data volumes and latency requirements, we choose the most suitable integration pattern.
Batch integration
Data is processed periodically (nightly, hourly) in bulk. Suitable for reporting, data warehousing and historical analysis where real time is not required. Reliable, easy to monitor and cost-efficient.
Near-real-time integration
Data is processed with a short delay (minutes) via streaming or micro-batches. Suitable for operational dashboards and situations where slight latency is acceptable.
Event-driven integration
Integrations are triggered by events in source systems. Leads to looser coupling between systems and scales well. Widely used in microservices architectures and modernisation.
API-based integration
Systems exchange data via standardised APIs (REST, GraphQL). Promotes reusability, versioning and loose coupling. The standard approach for SaaS platforms and cloud services.
Technology never stands alone
The connectors, APIs and integrations described here are part of solutions that fit processes, governance and day-to-day practice. We help organisations not only with design and implementation, but also with optimisation, management and knowledge transfer.
Processes first
Technology follows an understanding of your processes and information needs
Manageability
All integrations are documented, monitored and maintainable
Knowledge transfer
We make sure your team understands the solution and can manage it independently
Vendor-independent
We advise based on your situation, not on partnership agreements
Technical questions about data integration?
Whether it is a specific integration, an architecture question or platform selection: C-data is happy to think along with you.
