Big Data Processing

We design data platforms in layers, with explicit interfaces between them. A bronze layer captures source data faithfully; a silver layer cleanses, conforms and joins; a gold layer exposes the entities and metrics consumed by analysts, data scientists and applications. Each layer has its own owners, its own quality expectations, and its own evolution rules. The discipline is unglamorous and the payoff is substantial: a data set that misbehaves can be located, explained and fixed quickly.

Within each layer we use the storage formats and query engines best suited to the workload. Lakehouse formats — Apache Iceberg, Delta Lake, Apache Hudi — increasingly let us run warehouse-style analytics on lake-economics storage, while preserving the engineering properties (ACID writes, time travel, schema evolution) that production work requires. Where genuine warehouse performance is needed at the gold layer, we are happy to land curated marts in Snowflake, BigQuery, Redshift or Synapse.

Streaming is treated as a first-class delivery mode rather than an afterthought. We use Kafka, Kinesis or Pub/Sub for transport, Flink, Spark Structured Streaming or Beam for processing, and Iceberg or Delta for landing — with the same quality and governance controls that batch pipelines enjoy. Lambda-style architectures with parallel batch and stream paths are something we now actively avoid; the operational cost is rarely justified.

• Bronze / silver / gold layered design
• Open table formats (Iceberg, Delta, Hudi)
• Streaming-first ingestion where it pays
• Cloud-native storage with lifecycle policies
• Reusable transformations in dbt or Spark
• Reverse ETL to operational systems

Quality is not an attribute of a data set; it is an attribute of the pipeline that produces it. We instrument every transformation with explicit expectations — schema, value ranges, referential integrity, freshness — and treat violations as first-class incidents with their own runbooks. Tools such as Great Expectations, dbt tests and Monte Carlo find a regular place in our deployments, but the tooling matters less than the cultural commitment to fix the producer rather than apologise to the consumer.

We pay particular attention to lineage. Every important table has a generated lineage diagram showing the upstream sources, the transformations applied, and the downstream consumers. When a dashboard moves unexpectedly, the analyst can trace it to a specific job execution rather than waiting for the data team to investigate. This single capability removes a meaningful share of the ad-hoc support load from data teams.

Privacy controls live alongside quality controls. Personal data is identified at ingestion, classified, and protected according to the policy that governs it. Masking, tokenisation and aggregation are applied as data crosses layer boundaries, so that sensitive raw data never reaches a query surface that does not need it. This is the architectural form of privacy by design, and it is far cheaper than retrofitting controls later.

Data pipelines that look elegant in development and fragile in production usually share the same root cause: they were never designed to be operated. We design pipelines to be idempotent, replayable from any point, and instrumented in ways that make incidents easy to diagnose. Failures are expected and absorbed; recovery does not require a heroic engineer at three in the morning.

Orchestration is centralised — typically in Airflow, Dagster or Prefect — with shared conventions for naming, retries, alerting and SLAs. Where appropriate we layer in service-level objectives at the data-product level, so that consumer-facing freshness and availability commitments are explicit and measured rather than aspirational.

We treat cost as a property of the platform, not as something noticed at month-end. Compute and storage are continuously profiled by job, by team and by data product, and the resulting visibility tends to fund itself within a quarter. We have repeatedly cut cloud-data spending by twenty to forty per cent without sacrificing performance simply by replacing wasteful patterns the team had inherited and never measured.

“A pipeline you cannot operate is a liability, not an asset. Operability is part of the design, not a polish step.”

A meaningful share of our work involves moving organisations off legacy ETL platforms — Informatica, Ab Initio, hand-written shell scripts, dated SSIS estates — onto modern, cloud-native stacks. These migrations are usually constrained by the fact that the existing platform is, however awkwardly, running the business; downtime and silent regressions are not acceptable.

We approach legacy migration as an engineering programme, not a lift-and-shift. We catalogue what is actually used (typically far less than what is built), we identify the consumers, we redesign rather than translate, and we run the new and old systems in parallel until consumer-facing parity has been independently verified. The temptation to rebuild everything is resisted; the goal is to migrate value, not lines of code.

Where appropriate we incorporate the migration into a wider modernisation: introducing a semantic layer, consolidating duplicate logic, moving from row-level to column-level security, and retiring the inevitable backlog of zombie reports. The result is rarely cheaper to run on day one but is consistently cheaper to change, which is where most data spend actually lives.

Engagements range from focused interventions — a streaming use case to deliver, a cost programme to run, a migration to assess — to multi-year platform partnerships. We are equally comfortable embedding a small team into your platform group and owning a discrete delivery end-to-end, and we tailor the contract structure (T&M, fixed-price milestones, managed service) to whichever model best suits the work.

We do not sell platforms or seats, and we have no preference between cloud providers beyond what suits your environment. The engineers we deploy are the engineers you meet. Where we identify gaps we cannot fill, we say so and help you source elsewhere.