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Services
We engineer data platforms end to end - ingestion, streaming, lakes and warehouses, transformation and BI - so every team works from the same fast, well-governed data.
See it in action
A simplified view of the kind of Data & Big Data system we build - data moves between each stage as it would in production.
What we deliver
Take one service or combine several - we design, build, integrate and scale each of them.
Data & Big Data, end to end
From the first workshop to production support - one accountable team.
Reliable pipelines, models and tests that turn raw data into tables your teams can trust.
Spark, Flink and lakehouse platforms that process billions of events cost-effectively.
Open-format storage on object stores, with cataloguing, governance and fine-grained access.
Modelled warehouses on Snowflake, BigQuery, Redshift or Postgres, built for fast analytics.
Batch and incremental ingestion from apps, databases and SaaS tools, with monitoring and backfills.
Kafka and Flink streaming for live metrics, alerts and event-driven features.
Analysis that answers real business questions, from cohorts and funnels to experiments.
Self-serve dashboards on a shared semantic layer, so every team uses the same numbers.
Orchestrated, observable pipelines with data-quality checks at every step.
Data pipeline developmentParallel processing jobs tuned for throughput and cost across clusters.
Lineage, ownership, quality and privacy controls that satisfy auditors and speed teams up.
How we deliver
How a Data & Big Data engagement runs - and what you walk away with at every step.
Sources, quality and ownership mapped against the questions your data must answer.
You get: Data map and gap analysis
Ingestion, storage, modelling and governance chosen for your volumes, latency and budget.
You get: Target data architecture
Tested, observable pipelines and data models with quality checks at every step.
You get: Trusted, documented tables
Dashboards, documentation and training so every team can trust and use the data.
You get: Dashboards and runbooks
FAQ
Snowflake, BigQuery, Databricks, Redshift and Postgres, plus open lakehouse formats such as Iceberg and Delta. We recommend one based on your volumes, latency needs, skills and budget.
Yes. We usually start by adding tests and monitoring to what you already have, then replace the parts that keep breaking, one at a time.
Data-quality checks run in every pipeline, metrics are defined once in a shared semantic layer, and lineage shows where each figure comes from.
Let’s talk
Tell us what you're building. We'll come back with a clear plan, the right team and an honest timeline.