Big Data Solutions

Big Data Solutions

Big data architecture and processing solutions engineered for the volume, velocity, and variety that traditional databases can't handle.

30+
Big Data Systems Delivered
PB-scale
Data Volumes Handled
100%
Cost-Optimised Architecture
99.9%
Target Processing Reliability
Big Data

Big Data Solutions Built for Trustworthy Decisions

When data volume, velocity, or variety exceeds what traditional databases handle efficiently, you need purpose-built big data architecture. We design and build distributed processing systems and data lake architectures sized appropriately to your actual scale — avoiding both under-provisioned bottlenecks and wastefully over-engineered infrastructure.

  • Data lake and lakehouse architecture design
  • Distributed processing pipeline development (Spark, Databricks)
  • Real-time streaming data architecture (Kafka, Kinesis)
  • Big data infrastructure cost optimisation
  • Scalable storage architecture for structured and unstructured data
  • Big data platform migration and modernisation
Our Approach

Why Our Big Data Solutions Delivery Works

We size big data infrastructure to your actual and realistically projected scale, since over-provisioned 'big data' architecture for data that would fit comfortably in a normal database just adds unnecessary cost and complexity.

Right-Sized Architecture

Infrastructure scaled to your real data volume, not hypothetical scale.

Streaming Capability

Real-time processing architecture for high-velocity data needs.

Cost Optimisation

Distributed processing tuned to control infrastructure spend at scale.

Flexible Storage

Data lake architecture handling structured and unstructured data alike.

Delivery Process

How We Deliver Big Data Solutions

We assess your actual data volume, velocity, and processing needs first, since many businesses don't need full big data architecture — and we say so when a simpler solution fits better.

  • Assess actual data volume, velocity, and processing requirements
  • Design appropriate architecture (data lake, lakehouse, streaming)
  • Build distributed processing pipelines and infrastructure
  • Optimise for cost-efficiency at your real scale
  • Deploy with monitoring and establish ongoing operations
FAQs

Frequently Asked Questions

We assess this honestly during scoping — many businesses don't need distributed big data infrastructure and are better served by a well-designed traditional data warehouse. We recommend based on your real scale, not by default.

We work with Spark, Databricks, and cloud-native services like AWS EMR, GCP Dataproc, and Azure Synapse, selecting based on your existing cloud provider and team's skills.

We size compute and storage to actual workload patterns, use appropriate data partitioning and compression, and implement cost monitoring so distributed processing spend doesn't scale unpredictably.

Yes, we regularly modernise legacy Hadoop or on-premise big data platforms to cloud-native architectures, planning migrations to minimise disruption to existing pipelines and reporting.

Get More Value From Your Data

Book a free consultation to discuss your big data architecture needs.