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Data engineering that keeps working under real load.

Pipelines, streaming and warehouses for products and enterprises that depend on their data, built so the numbers can be trusted, replayed and traced.

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From source to decision
Orchestration · schedules · dependenciesSourcesevents,files, APIsStreamKafkaProcessclean, join,enrichStoreClickHouse,IcebergServeBI, ML,APIsRejected rowsrejectedfix, replayreplay and backfillFreshnessLineageCostAlertsEvery step can be replayed and traced.
1+ TBprocessed every day on a platform we deployed
4×lower cloud cost on a telecom telemetry system
15architecture decisions for an iGaming data platform
Technologies behind this work
Pipelines & processing
Apache Kafka
Apache Airflow
Apache Spark
Apache Flink
dbt
Dagster
Airbyte
Python
Node.js
Storage & platform
ClickHouse
Apache Iceberg
PostgreSQL
Snowflake
BigQuery
Databricks
Elasticsearch
Redis
Docker
Kubernetes
Terraform
Grafana
Prometheus
What is included

Three areas, from raw events to numbers you can trust.

Built around how your teams read the data, not around a tool.

Ingest

Get the data in, reliably.
  • Batch and streaming ingestion
  • Provider and source onboarding
  • Historical data migration

Store and model

Shape it for the questions you ask.
  • Data warehouse and lakehouse design
  • Data contracts and schema versioning
  • Replay and backfill

Trust it

So a number can be explained.
  • Quality checks and a place for rejected rows
  • Observability, lineage and ownership
  • Access controls, cost and performance tuning
Typical situations

When teams come to us.

Trust

Your pipelines break and nobody can explain a number

We trace lineage, add contracts and replay, so a number can be explained and fixed at the source.

Scale

The data outgrew the first design

We move to streaming ingestion, columnar storage and cost controls, without losing the history.

Teams

Several teams need the same data

We agree ownership, contracts and access rules, so each team can build on the data without stepping on the others.

What you get

Deliverables you will keep.

Concrete artifacts, documented and handed over.

A target data architecture with written decisions
Working pipelines with replay and backfill
Data contracts and schema versioning
Lineage, ownership and access rules
Monitoring for freshness and failures
A runbook for your team
How to start

Three ways to begin.

Larger work starts with a short paid discovery.

All offers
Discovery

Short paid discovery

Map the system, the risks and the constraints. Ends with a clear plan, not a strategy deck.

A few working days to a few weeks
Engagement

AI / Data / Cloud Modernization Engagement

Modernize workflows, data, AI systems and cloud architecture.

Custom scope
Engagement

Product Engineering Support

Senior engineers across frontend, backend, cloud, QA and AI/data, month by month.

Monthly
Proof

Work you can open and check.

See all 7 case studies
1+ TB
per day, in real time
Urban-Intel data platform
A real-time platform turning a terabyte of daily data into operational decisions.
4×
lower cloud cost
Telecom telemetry and accounting
Real-time telemetry, accounting and OLAP for a top-three telecom provider, with the data flows preserved.
15
decisions, written down
iGaming enterprise data platform
Kafka, ClickHouse, Iceberg and Airflow, with data contracts, replay and governance.
Where this ends

Not quite what you need?

Need the architecture decided first?Enterprise data architectureWant to ask your data questions in plain language?AI, automation & agentsNeed the cloud foundation underneath?Cloud, infrastructure & data
FAQ

Questions about data engineering.

What is the difference between data engineering and data architecture?
Architecture decides what to build and why: platform choices, standards, ownership. Engineering builds and runs it: pipelines, storage, quality checks. We do both, and either can be bought alone.
Do you work with the platform we already have?
Yes. We start with a short paid discovery that maps what is running, where it is fragile and what it costs, then recommend what to keep.
Which tools do you use?
Our published work uses Kafka, ClickHouse, Apache Iceberg and Airflow, with PostgreSQL and Python around them. We choose tools for your data and your team, and say plainly when something is not a fit.

Tell us about your data.

Describe where it comes from, who uses it and what breaks. We reply with a clear next step, usually within 24 hours.

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