Big Data Platforms

Big Data Platforms

Modern big data platform implementation — data lakes, lakehouses, and distributed processing — built to handle massive scale reliably and cost-effectively.

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25+
Big Data Platforms Implemented
PB-scale
Data Volumes Supported
100%
Cost-Optimised Architecture
99.9%
Target Processing Reliability
Big Data Platforms

Big Data Platforms Built for Reliability

Implementing a big data platform — data lake, lakehouse, or distributed processing environment — requires architecture decisions that are expensive to reverse once large volumes of data and dependent pipelines exist. We implement big data platforms sized to your genuine scale, with the governance and cost controls that keep them manageable long-term.

  • Data lake and lakehouse platform implementation
  • Distributed processing platform setup (Spark, Databricks)
  • Big data platform governance and access control
  • Platform migration from legacy big data infrastructure
  • Cost optimisation architecture for large-scale data platforms
  • Platform documentation and operational handover
Our Approach

Why Our Big Data Platforms Delivery Works

We implement platforms with governance and cost control built in from the start, since an ungoverned big data platform without access controls or cost visibility becomes both a security liability and a runaway expense as it scales.

Right-Sized Platform

Data lake or lakehouse architecture matched to your real scale.

Governance Built In

Access control and data governance established from day one.

Cost-Optimised

Architecture designed to control cost even at massive scale.

Migration-Ready

Structured migration paths from legacy big data infrastructure.

Delivery Process

How We Deliver Big Data Platforms

We assess your real scale requirements, design an appropriately sized platform with governance built in, and implement with a clear migration path if replacing legacy infrastructure.

  • Assess data volume, velocity, and processing requirements
  • Design platform architecture (data lake, lakehouse, distributed processing)
  • Implement with governance and access control from the start
  • Migrate from legacy platforms where applicable
  • Document and hand over for ongoing operations
FAQs

Frequently Asked Questions

Big Data Solutions (under Data & Analytics) covers the broader architecture and processing pipeline design; Big Data Platforms focuses specifically on implementing the underlying platform infrastructure itself — many engagements combine both.

We implement Databricks, cloud-native services like AWS EMR and GCP Dataproc, and open-source Spark-based platforms, selecting based on your cloud provider and existing infrastructure.

We build cost visibility and governance into the platform from implementation, using appropriate partitioning, compression, and lifecycle policies so costs don't scale unpredictably as data volume grows.

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

Build a Platform That Scales Without Breaking the Budget

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

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