02 — Data Operations
From raw data to reliable intelligence
Data problems are quiet. A column changes type, a nightly job runs twice, a source stops sending — and a report is wrong for days before anyone notices. We build platforms where every dataset has an owner, a contract and a history, and where a broken assumption stops the pipeline instead of reaching the boardroom.
Discipline 02
Reliable intelligence starts with reliable data.
Data is only valuable when it can be trusted. We design and operate platforms that collect, transform, move, store and serve information across complex environments.
Whether it comes from databases, applications, sensors, event streams or external systems, our work is not simply moving data from A to B — it is making it traceable, observable, consistent and usable.
What we build
10 · Data Operations- Data pipelines
- ETL / ELT platforms
- Real-time streaming systems
- Batch processing architectures
- Data lakes & warehouses
- Data integration platforms
- Data quality systems
- Lineage & observability
- High-volume processing
- Analytics infrastructure
How we work
Data Operations
The face — how the system sees
- 01
Change data capture over nightly dumps
Where the source allows it, we read database logs with Debezium and stream changes through Kafka. Inserts, updates and deletes arrive in order and close to real time, without extra load on the source system.
- 02
Contracts at every boundary
Producers and consumers agree on schema, meaning, freshness and ownership in a versioned data contract. Schema-registry compatibility rules and CI checks reject breaking changes before they are deployed.
- 03
Quality checks inside the pipeline
Tests for nulls, uniqueness, referential integrity, value ranges and volume run as pipeline steps. A failed check quarantines the batch and alerts its owner; it never silently publishes a half-built table.
- 04
Lineage and observability as standard
Column-level lineage shows where every figure comes from and what breaks if a source changes. Freshness, volume and schema drift are monitored per dataset, the way latency is monitored for a service.
- 05
Governance from the point of entry
Personal data is tagged when it arrives, not when an auditor asks. Masking, row-level policies and retention rules follow the tag through every layer, designed to support KVKK and GDPR requirements.
What you receive
- Source-to-target data architecture and flow maps
- Ingestion pipelines for CDC, batch and streaming
- Versioned data contracts and a schema registry
- dbt models with automated quality tests
- Column-level lineage and a searchable data catalogue
- Data observability dashboards, alerts and runbooks
Technology range
- Apache Kafka
- Debezium
- Apache NiFi
- Apache Airflow
- dbt
- Apache Spark
- Apache Flink
- ClickHouse
- PostgreSQL
- Apache Iceberg
- Trino
- MinIO
- Great Expectations
- OpenLineage
- Grafana
We are technology-agnostic and work with the stack you already run.
What changes
What changes
- 01
One number per question
When finance and operations ask for the same metric, they get the same answer, because both read from the same tested model.
- 02
Problems stopped upstream
Broken sources and schema changes are caught in the pipeline, by the team that owns them, before they reach a report or a model.
- 03
A foundation for AI
Clean, documented and access-controlled datasets become the ground that forecasting, scoring and language models can safely stand on.
Related
Frequently asked questions
Frequently asked questions
01Do we need to replace our existing data warehouse?
Usually not. We start from what you have — an Oracle or SQL Server warehouse, a Hadoop cluster, files on shared storage — and add what is missing: change capture, tests, lineage and orchestration. Migration is proposed only when the current platform cannot meet a concrete requirement, and then it is done table by table with reconciliation, not in a single cut-over.
02Batch or streaming — which do we need?
That depends on how quickly a decision has to be made. Fraud scoring and machine alarms need streaming; a monthly regulatory report does not. Most platforms end up with both, sharing the same contracts, quality checks and lineage so that the two paths agree on the numbers.
03How do you handle personal data under KVKK?
Personal fields are classified and tagged at ingestion. From there, masking, pseudonymisation, row-level access and retention policies are applied automatically in every layer, and every access is logged. The platform is designed to support KVKK and, where EU data is involved, GDPR requirements; the legal assessment remains with your legal and compliance teams.
04Can you operate the platform after it is built?
Yes. Data platforms need continuous care: sources change, volumes grow and new consumers arrive. We can run the platform day to day with defined on-call and incident procedures, or train your team and hand it over with runbooks and documented ownership for every dataset.
Let’s talk about your data operations needs.
Pipelines, streaming and warehouses that make data traceable, observable and consistent, so every report and model downstream can trust what it reads.