Trusted by Europe's leading organizations
DNA Solutions unifies disparate data sources into central, high-performance querying environments. We build cloud data lakes, warehouses and streaming pipelines so your teams get real-time access to production information, without vendor lock-in.
Trusted by Europe's leading organizations
DNA Solutions builds central data platforms that consolidate fragmented sources into one querying environment, ending vendor lock-in and giving teams real-time access to production data. We migrate Oracle and SAP workloads to cloud-native data lakes, then layer warehousing, streaming pipelines and self-service analytics on top. Our senior team brings 20+ years across enterprise data infrastructure.
DNA Solutions designs technology that lands on your bottom line. European enterprises trust us with extreme data volumes and critical financial pipelines.
See client resultsDNA Solutions built and maintains a Deloitte-audited billing platform processing €300M in audited transactions every month.
By optimizing software licensing fees for a major European organization, DNA Solutions delivered over €1M in yearly cost savings.
A senior team of engineers and consultants across Europe.
T-Systems, Satellic, European Commission: our longest engagements last because we deliver.
A data platform is only as good as it is on a busy day. Here is how we build it to stay fast and governed under load.
We design central, high-performance querying environments on PostgreSQL, Snowflake and AWS Redshift, applying lakehouse patterns that combine the flexibility of a data lake with the query performance of a warehouse. Raw, structured and semi-structured data live in one governed layer, so analysts and downstream systems read from a single source of truth instead of chasing copies scattered across departments.
We build Power BI, Tableau and custom dashboards directly over live production data instead of stale nightly extracts. Decision-makers see operational reality as it happens, with metrics that reconcile back to the underlying records rather than drifting from them. We tune the query layer and caching so dashboards stay responsive even when they sit on top of billions of rows, and we model the metrics with your teams so everyone reads the same definition of a number across departments.
We build ETL, ELT and streaming pipelines on Apache Kafka and Airflow for real-time ingestion across many sources at once. The architecture decouples ingestion from consumption, so a spike in incoming events never slows down the analytics layer. Pipelines include schema validation, retry policies and dead-letter handling, so a malformed feed from one source is quarantined rather than left to corrupt the wider platform. Each source stays independently monitored, which makes it straightforward to add a new feed without reworking the ones already in production.
We set up data catalogs, access control and GDPR-compliant governance so teams get self-service access without losing control over sensitive data. Every dataset has a documented owner, a lineage trail and an access policy, so an analyst can find and query what they need while compliance keeps a clear audit of who touched which data, and when. Governance is built into the platform, not bolted on after the fact.
DNA Solutions engineers complete data platforms for high-volume enterprises. From cloud migration to vector search and real-time ingestion, our architectures turn fragmented data into one fast, governed querying environment.
What we buildMigrating Oracle and SAP workloads to cloud-native data lakes, validated in parallel until cutover. Vendor lock-in eliminated, query times moved from days to hours.
Building vector databases for semantic search and retrieval over enterprise documents and operational data. Often paired with our AI/ML engagements to ground assistants in your own governed data.
ETL, ELT and streaming pipelines on Apache Kafka and Airflow for real-time ingestion across many sources at once. Schema validation and retry policies keep one bad feed from corrupting the platform.
Data is structured incompatibly across telecom, tolling and retail. The platform core is shared; the sources and models on top are sector-specific.
OSS/BSS data, charging records and audiovisual platform analytics unified into one querying environment for operators handling millions of subscribers.
Unifying tolling, transaction and sensor data into one analytics platform, so fragmented reporting becomes a single, consistent operational view.
Sales, inventory and supply-chain data unified for operational reporting, with real-time dashboards over live data instead of disconnected exports.
How we unify and migrate enterprise data for European operators.
A scalable microservices architecture that rates high volumes of GPS events and applies multi-country tax rules to generate unified invoices.
Rating pipeline, multi-country
A private document classification engine that beat Azure AI Document Intelligence on two of three benchmark datasets, 94.7% versus 84.2%.
94.7% vs 84.2% on Azure AISenior decision-makers on the data, tolling and financial platforms DNA Solutions has delivered.
"DNA works with us to deliver digital systems at scale so that we can serve our customers digitally. They are both reactive to requests and proactive with ideas and proposals."
"I appreciated the collaborative spirit and the effort to deliver a reliable solution within a reasonable budget. The step-by-step approach with a demo before deployment made all the difference."
"DNA Solutions has delivered online tools that have made the client's employees and customers' lives easier. For instance, the client can now handle cases in a maximum of two days instead of five."
Common questions before consolidating data onto one platform.
DNA Solutions builds cloud data lakes, data warehouses and lakehouse architectures on PostgreSQL, Snowflake and AWS Redshift, with streaming pipelines on Apache Kafka and Airflow. The goal is a single, central, high-performance querying environment that consolidates your fragmented sources, so analysts and downstream systems read from one source of truth instead of chasing copies scattered across departments. We start from your existing estate, map where data lives today and design the target architecture around how your teams actually query it. Raw, structured and semi-structured data land in one governed layer, with the ingestion, warehousing and self-service layers built on top. Where it fits your roadmap, we add a vector database for semantic search and the retrieval layer your AI workloads depend on.
Yes. DNA Solutions migrates Oracle and SAP workloads to cloud-native data lakes, which removes vendor lock-in and gives teams real-time access to production data. We start with a licensing and schema audit of your current estate, then build the integration and ETL pipelines that bridge your existing systems with the new platform. The migration runs with parallel validation: we compare source and target so it stays reversible until you are ready to switch over, and nothing goes live until the outputs reconcile. We favour an open-source and cloud-managed stack on PostgreSQL, Snowflake and AWS Redshift, so there are no proprietary licences carried into the new platform and your team keeps full control over the architecture, the deployment and the data it holds.
DNA Solutions implements vector databases for semantic search and retrieval over enterprise documents and operational data, often alongside our AI/ML engagements. This lets your teams query unstructured content by meaning rather than exact keywords, and it provides the retrieval layer that AI assistants need to answer from your own governed data instead of generic sources. We embed your documents and records, store the vectors next to the structured data already in the platform, and wire the retrieval into the applications that consume it. Because the vector layer sits inside the same governed environment, access control and lineage apply to it the same way they apply to the rest of your data. In practice this is what lets an internal assistant cite a real contract, ticket or report rather than a plausible guess.
We set up data catalogs, access control and GDPR-compliant governance so analytics stay self-service without losing control over sensitive data. Every dataset has a documented owner, a lineage trail and an access policy, so an analyst can find and query what they need while compliance keeps a clear audit of who accessed which data, and when. Governance is designed into the platform from the start, not added afterwards. What you can expect from an engagement: a discovery phase that maps your sources and access rules, a target architecture you sign off on before any build, and a phased rollout validated on your own data. At handover, we walk your engineers through the architecture and the runbooks so your internal team can operate and extend the platform on their own.