Dreamware / Services / Data & Analytics / Big Data Systems
Big Data Systems
Distributed computing, real-time analytics at scale.
About this service
Big data systems are necessary when the volume, velocity, or variety of your data exceeds what conventional databases and warehouses can handle efficiently. Not every organisation needs big data infrastructure — but when you do, the right architecture makes the difference between analytical capabilities that work and ones that are perpetually hitting their limits.
We design and implement distributed data processing systems using Spark, Flink, and cloud-native equivalents. We build real-time analytics systems on streaming infrastructure and design data architectures that can handle petabyte-scale datasets. We're honest about when you actually need this — many organisations would be better served by a well-tuned conventional warehouse.
The NZ context matters here. Most NZ businesses don't have the data volumes that require true big data infrastructure. When they do — typically in telecommunications, financial services, government, and large retail — we have the experience to design it right.
How Dreamware approaches this
Big data architecture starts with measurement. What's the actual data volume? What are the query patterns? What latency is acceptable? These numbers determine whether distributed computing is genuinely required or whether a well-optimised data warehouse would serve equally well at a fraction of the complexity and cost.
When distributed systems are warranted, we design with operational simplicity in mind. The most scalable architecture is worth nothing if your team can't operate it. We make managed services work hard before reaching for self-managed infrastructure, and we document operational procedures thoroughly.
What you get
- Big data architecture design — documented system design with technology choices and rationale
- Distributed processing pipeline — production Spark or Flink jobs for your processing requirements
- Real-time analytics layer — streaming infrastructure for latency-sensitive analytical requirements
- Performance benchmarks — documented throughput and latency characteristics under load
- Operational runbook — procedures for monitoring, scaling, and troubleshooting
- Cost model — infrastructure cost at different data volumes and query loads
Investment guide
Big data projects are typically the largest data engagements, running $40,000–$200,000+ NZD. Architecture design without implementation runs $15,000–$30,000. Full implementation of distributed processing infrastructure for a production system sits significantly higher depending on scope. We recommend a feasibility assessment ($5,000–$10,000) before committing to large-scale implementation.
All pricing in NZD excluding GST. Fixed-price engagements where scope allows — we'll confirm pricing after a free scoping conversation.
Ready to get started?
Book a free conversation. We'll tell you honestly what's realistic, what it costs, and how we'd approach it.