Enterprise Big Data Engineering
We architect and deliver petabyte-scale data platforms that process, govern, and serve data across your enterprise with the latency, lineage, and compliance controls that regulated industries require. From real-time streaming pipelines to enterprise data lakehouses, our engineering practice builds the data infrastructure that AI and analytics strategies depend on.
Senior-only delivery · £960M revenue influenced
Data Pipeline
Real-time pipeline architecture.
Ingest
Process
Store
Delta Lake
Serve
API
Events/sec
1.2M
Latency
<60s
Uptime
99.9%
Cost
-40%
Methodology
Our approach.
Data Architecture Assessment & Lakehouse Design
We assess your current data landscape — sources, volumes, latency requirements, and consumer patterns — and design a modern data lakehouse architecture on Delta Lake, Apache Iceberg, or Apache Hudi that unifies batch and streaming workloads while maintaining ACID transaction guarantees at petabyte scale.
Ingestion Pipeline Engineering
We build high-throughput ingestion pipelines that capture data from operational databases, SaaS APIs, event streams, and third-party feeds — with schema evolution handling, exactly-once delivery guarantees, and ingestion SLA monitoring that alerts before downstream consumers are impacted.
Real-Time Streaming Architecture
Apache Kafka, Apache Flink, and Spark Structured Streaming are implemented to deliver sub-minute data freshness for operational analytics, fraud detection, and customer-facing personalisation use cases — with topic partitioning strategies and consumer group management designed for long-term operational stability.
Data Quality, Governance & Lineage
We implement data quality frameworks using Great Expectations or Soda Core, column-level lineage tracking with OpenLineage, and a data catalogue integration with Apache Atlas or DataHub — giving your data governance team the controls required to satisfy GDPR, CCPA, and sector-specific data regulations.
Performance Optimisation & Cost Engineering
We apply file format optimisation (Parquet, ORC), partition pruning, Z-ordering, and cluster auto-scaling strategies to reduce query costs and improve performance by 3-10× versus unoptimised architectures — with ongoing cost anomaly monitoring and automated rightsizing recommendations.
Data platform failures are the silent killer of enterprise AI strategies.
Organisations invest in data science teams, ML tooling, and analytics platforms only to find that the underlying data infrastructure cannot provide the data quality, availability, and lineage required for production workloads. Data arrives late. Schemas drift unexpectedly. Lineage is untracked. Regulatory audits cannot be answered. Our big data engineering practice is built around the principle that data infrastructure is a product with SLAs, not a utility that operates on a best-effort basis. Every pipeline we build ships with quality contracts, lineage tracking, and operational monitoring that gives your data consumers the reliability they need to build critical systems on top.
What we deliver.
Core capabilities across every big data engagement.
Technology Stack
Battle-tested at petabyte scale.
Apache Spark
Flink
Kafka
Airflow
dbt
Docker
Kubernetes
PostgreSQL
Elasticsearch
Snowflake
Redis
Python
Data quality commitment.
99.9%
Pipeline SLA Uptime
Every pipeline ships with monitoring and automated alerting
<60s
End-to-End Latency
Real-time streaming from source to serving layer
40%
Cost Reduction
Infrastructure optimisation through rightsizing and spot instances
Build the Data Foundation Your AI Strategy Depends On
Our data architects will assess your current infrastructure and design a scalable, governed data platform aligned to your analytics and AI roadmap.