Stabilizing and Scaling Spobik
Turning a slow e-commerce site into a fast, secure revenue-generating platform
Key Results:
- 6x faster load time (600 ms → 38 ms)
- Updates for 500,000+ items in ~10 min
- Largest Contentful Paint: 0.24 seconds
Big data projects usually get funded on a tool decision made before anyone measured the volumes. Forbytes assesses what you collect, how fast it grows, and what you need from it, then delivers an architecture, a technology selection, and a plan with costs attached to each stage.
| Big Data Consulting Deliverable | Business Impact |
|---|---|
| Data volume and growth assessment | Capacity planning based on measured numbers |
| Target big data architecture | One design your teams build against instead of parallel experiments |
| Technology and platform selection | Tools chosen against your workload, budget, and hiring market |
| Cost model per storage and processing tier | Infrastructure spend becomes predictable before commitment |
| Data governance framework | Ownership, access, retention, and compliance written down |
| Phased implementation roadmap | Work sequenced so early stages deliver before the platform is complete |
Data & Infrastructure AssessmentMeasuring current volumes, growth rates, query patterns, and what the existing setup costs to run.
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#2
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Big Data Architecture DesignDesigning storage, processing, and access layers sized against measured volumes and expected workload growth.
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#4
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Governance & Security FrameworkDefining ownership, access rules, retention, quality standards, and GDPR requirements.
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#6
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#1
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Defining RequirementsEstablishing the decisions that data has to support and how fresh it needs to be.
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#3
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Technology SelectionComparing platforms and services on cost, workload fit, and whether you can staff them.
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#5
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Roadmap & HandoverSequencing implementation by business value, with costs and required roles per stage.
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Group Manager, Guesty
CIO at AB Stenströms Skjortfabrik
CEO at The African Touch
Big data consulting covers the decisions that come before a platform gets built: assessing current volumes, growth, and query patterns, defining what the business needs from the data, designing the target architecture, selecting storage and processing technologies, setting up governance and security rules, and sequencing implementation with costs per stage. The output is a documented plan any competent team can execute, including your own.
Consulting answers what to build and why. Data engineering builds it: the pipelines, the warehouse, the reporting layer. Clients with a clear technical direction usually skip straight to engineering. Clients choosing between platforms, or facing a costly commitment they cannot easily reverse, start with consulting. Both work as standalone engagements.
There is no threshold in terabytes, and volume alone is a poor trigger. The signals that matter are cost and capability: infrastructure bills growing faster than usage, queries too slow to run on the full dataset, or use cases your current architecture cannot support at any price. Plenty of companies asking about big data need a properly designed warehouse rather than a big data platform, and we would rather tell you that in week two than after implementation.
We work across Google Cloud including BigQuery, Microsoft Azure, and AWS, plus on-premises and hybrid setups where a full cloud move is not realistic. Selection weighs three things: how the platform handles your specific workload, what it costs at your projected volume rather than today’s, and whether your team can operate it. A platform your people cannot run becomes your problem after the engagement ends.
Yes, and it forms part of every engagement rather than an add-on. That means data ownership, role-based access, retention periods, quality standards, and the documentation GDPR requires. For regulated sectors we map the requirements against the proposed architecture early, since compliance constraints often decide where data can physically live, and that decision is expensive to revisit later.