Lakehouse Architecture Design
We design the right lakehouse for your data and use cases.
Data Lakehouse Consulting Services
Data Lakehouse Consulting Services
Trusted across 20+ countries by Fortune 500 companies and growth-stage brands
We design lakehouse platforms that unify your structured and unstructured data in one place, giving you warehouse-grade analytics and AI-ready data without maintaining two systems. Over a decade of experience, 250+ digital solutions delivered.
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What is a data lakehouse?
A data lakehouse is a modern architecture that combines the low-cost, flexible storage of a data lake with the performance and governance of a data warehouse, in a single platform. It handles structured tables and unstructured data like text, images and logs, which makes it ideal for both BI and AI. Noseberry designs and builds lakehouses on platforms like Databricks and Delta Lake, so you run analytics and machine learning on one governed foundation.
Key takeaways
- A lakehouse unifies a data lake and a warehouse in one platform.
- It handles structured and unstructured data, ideal for BI and AI together.
- It removes the cost and complexity of maintaining separate lake and warehouse systems.
- Databricks and Delta Lake are the leading platforms for it.
2M+Lives touched
15+Fortune 500 clients
20+Countries served
250+Digital solutions delivered
What we do
Our data lakehouse services
Databricks and Delta Lake Build
We implement lakehouse platforms end to end.
Lake and Warehouse Consolidation
We unify separate systems into one lakehouse.
Streaming and Batch Unification
We support both real-time and batch on one platform.
AI and ML Enablement
We make the lakehouse ready to train and serve models.
Governance and Optimisation
We add governance and tune cost and performance.
Where it delivers value
Where a lakehouse delivers value
Running BI and AI on one governed platform
Handling unstructured data that a warehouse cannot
Cutting the cost of maintaining separate lake and warehouse systems
Feeding machine learning with rich, unified data
Scaling flexibly as data types and volumes grow
How we work
Our five-phase process
We prove the lakehouse on a priority workload, then consolidate and scale.
1
Discovery and Audit
We map your data types, systems and AI ambitions.
2
Strategy and Roadmap
We design the lakehouse architecture and plan.
3
Rapid Proof of Concept
We prove the lakehouse on a priority workload.
4
Build and Integrate
We build and consolidate onto one platform.
5
Deploy and Optimize
We govern, scale and tune cost and performance.
Technology we use
Lakehouse
- Databricks
- Delta Lake
- Apache Iceberg
Processing
- Apache Spark
- Structured streaming
Cloud storage
- AWS S3
- Azure Data Lake
- Google Cloud Storage
Cloud
- AWS
- Azure
- Google Cloud
Security and compliance
Unified governance across all your data
Lakehouses are built with unified governance, encryption, fine-grained access and audit trails. We build to GDPR, HIPAA and SOC 2, deployed on AWS, Google Cloud and Azure.
GDPRHIPAASOC 2
Real success storiesE-Commerce · Retail leader PropTech · Real-estate marketplace FinTech · Digital Insurer
Outcomes we have driven
Challenge
Separate lake and warehouse made BI and AI costly to run.
Solution
Consolidated onto a Databricks lakehouse for analytics and ML together.
Impact
+28% conversion from unified, AI-ready data.
Challenge
Unstructured documents and images were locked out of analytics.
Solution
A Delta Lake platform unifying structured and unstructured data.
Impact
40% faster valuations on one governed foundation.
Challenge
Fraud models needed rich, unified data at scale.
Solution
A lakehouse feeding real-time scoring and BI from one source.
Impact
93% of fraud caught pre-payout, ~$4.2M saved annually.
Sector-anonymised outcomes shown until named clients are approved.
Why Noseberry
Why choose Noseberry for lakehouse
Specialist
AI, Cloud and Data is our core, no generalist dilution.
AI-ready
We design lakehouses specifically to power machine learning.
Modern stack
Databricks, Delta Lake and Spark, not legacy tooling.
Proven at scale
250+ solutions delivered across 20+ countries.
Data lakehouse, answered.
A lake stores raw data cheaply but lacks structure. A warehouse gives structured, governed analytics. A lakehouse combines both in one platform, for analytics and AI together.
If you work mostly with structured data for BI, a warehouse may be enough. If you also have unstructured data and AI needs, a lakehouse is usually the better single platform. We advise on the fit.
Primarily Databricks with Delta Lake, and Apache Iceberg where it fits, on your cloud of choice.
Often yes. We can consolidate separate systems into one lakehouse to cut cost and complexity.
Yes. Because it holds unified structured and unstructured data with governance, it is an ideal foundation for training and serving AI models.
Unify your data for analytics and AI.
Book your free 30-minute strategy session and we will design your lakehouse.
Book nowRelated resources
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