Big Data Architecture
Designing systems that scale with your data.
Big Data Services
Big Data Services
Trusted across 20+ countries by Fortune 500 companies and growth-stage brands
We engineer systems that handle data at massive scale, high volume, high velocity and many formats, so growth in your data becomes an advantage, not a bottleneck. Over a decade of experience, 250+ digital solutions delivered.
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What are big data services?
Big data services are the engineering of systems that store and process data at a scale traditional tools cannot handle, across high volume, high velocity and varied formats. They cover big data architecture, distributed processing, and platforms that scale elastically. Noseberry designs and builds big data systems on modern cloud and lakehouse platforms, so large-scale data powers analytics and AI rather than slowing you down.
Key takeaways
- Big data services handle scale that traditional databases and tools cannot.
- The three challenges are volume, velocity and variety of data.
- Modern big data runs on cloud, Spark and lakehouse platforms, not legacy Hadoop.
- The goal is turning large-scale data into analytics and AI value.
2M+Lives touched
15+Fortune 500 clients
20+Countries served
250+Digital solutions delivered
What we do
Our big data services
Distributed Processing
Spark-based processing for volume and speed.
Data Lake and Lakehouse Build
Scalable storage for all data types.
Streaming and Batch
Handling both real-time and large batch workloads.
Big Data Migration
Modernising legacy big data setups to the cloud.
AI and Analytics Enablement
Making large-scale data ready for models and BI.
Where it delivers value
Where big data services deliver value
Handling rapidly growing data volumes
Processing many data types together
Powering AI and analytics with large datasets
Modernising legacy or on-premise big data clusters
Scaling elastically without over-provisioning
How we work
Our five-phase process
We design for your real scale and prove it before rolling out fully.
1
Discovery and Audit
We assess your data scale, sources and systems.
2
Strategy and Roadmap
We design the big data architecture and plan.
3
Rapid Proof of Concept
We prove it at your real scale.
4
Build and Integrate
We build storage and processing, and connect BI and AI.
5
Deploy and Optimize
We roll out fully and tune cost and performance.
Technology we use
Processing
- Apache Spark
- Kafka
- Structured streaming
Platforms
- Databricks
- Snowflake
- Cloud data lakes
Storage
- Delta Lake
- AWS S3
- Azure Data Lake
- Google Cloud Storage
Cloud
- AWS
- Azure
- Google Cloud
Security and compliance
Governed at any scale
Built with encryption, access controls and governance. We align to GDPR, HIPAA and SOC 2, on AWS, Azure and Google Cloud.
GDPRHIPAASOC 2
Real success storiesFinTech · Digital Insurer PropTech · Real-estate marketplace E-Commerce · Retail leader
Outcomes we have driven
Challenge
Data volumes outgrew the existing systems.
Solution
A modern big data platform on Spark and lakehouse, elastically scaled.
Impact
Scaled processing behind 93% of fraud caught pre-payout.
Challenge
Many data types couldn't be processed together.
Solution
A unified lakehouse handling structured and unstructured data.
Impact
40% faster valuations across markets.
Challenge
Large datasets slowed analytics and personalization.
Solution
Distributed processing feeding BI and the recommendation engine.
Impact
+28% lift in conversion rate.
Sector-anonymised outcomes shown until named clients are approved.
Why Noseberry
Why choose Noseberry for big data
Specialist
AI, Cloud and Data is our core, no generalist dilution.
Modern approach
Cloud and lakehouse, not legacy Hadoop overhead.
AI-ready
Big data engineered to feed analytics and models.
Proven at scale
250+ solutions delivered across 20+ countries.
Big data, answered.
Data that is too large, too fast or too varied for traditional tools to handle efficiently, requiring distributed systems.
Usually not. Modern big data runs on cloud, Spark and lakehouse platforms, which are more efficient and easier to manage than legacy Hadoop.
A warehouse handles structured, modelled data. Big data systems also handle high-velocity and unstructured data at larger scale, often feeding the warehouse or lakehouse.
Yes. We migrate legacy and on-premise big data setups to modern cloud and lakehouse platforms.
Yes. Large, varied datasets are exactly what AI and machine learning models need, when engineered and governed properly.
Data outgrowing your systems?
Book your free 30-minute strategy session and we will design a big data solution.
Book nowRelated resources
Go deeper with our thinking
Case study
Modernizing Enterprise Reporting for Kent with Azure, Databricks, and Power BI
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Guide
The Complete Guide to Data Engineering
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Guide
Data Warehouse vs Lakehouse
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Business Intelligence vs. Data Analytics Services: What's the Difference?
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How Big Data Analytics Services Turn Raw Data Into Revenue
Read articleNot sure where you stand?
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