Databricks Implementation
Lakehouse architecture and setup done right.
Databricks Consulting Services
Databricks Consulting Services
Trusted across 20+ countries by Fortune 500 companies and growth-stage brands
We build and optimise on Databricks, the lakehouse platform that unifies data engineering, analytics and machine learning in one place. One platform for your BI and your AI. Over a decade of experience, 250+ digital solutions delivered.
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What is Databricks consulting?
Databricks consulting is expert help to design, build and optimise on the Databricks lakehouse platform, which combines a data lake and warehouse on top of Apache Spark and Delta Lake. It covers implementation, migration, data engineering, and machine learning enablement. Noseberry builds Databricks platforms that handle structured and unstructured data and serve both analytics and AI from one governed foundation.
Key takeaways
- Databricks is a lakehouse platform unifying data engineering, analytics and ML.
- It is built on Apache Spark and Delta Lake, ideal for large and unstructured data.
- It is a strong choice when you need both BI and AI on one platform.
- Good design and cost governance keep Databricks efficient at scale.
2M+Lives touched
15+Fortune 500 clients
20+Countries served
250+Digital solutions delivered
What we do
Our Databricks services
Lakehouse and Delta Lake Build
Unified storage for structured and unstructured data.
Migration to Databricks
Moving legacy data platforms onto Databricks.
Data Engineering on Spark
Scalable pipelines and transformations.
Machine Learning Enablement
Preparing Databricks to train and serve AI models.
Cost and Performance Optimisation
Cluster tuning and governance to control spend.
Where it delivers value
Where Databricks delivers value
One platform for BI and AI, not two systems
Processing large-scale and unstructured data
A governed foundation for machine learning
Unifying separate lake and warehouse setups
Elastic Spark compute for heavy workloads
How we work
Our five-phase process
We prove a lakehouse workload, then consolidate and scale with cost controls.
1
Discovery and Audit
We assess your data, workloads and AI goals.
2
Strategy and Roadmap
We design the lakehouse architecture and plan.
3
Rapid Proof of Concept
We prove a lakehouse workload.
4
Build and Integrate
We consolidate onto Databricks and connect BI and AI.
5
Deploy and Optimize
We scale with cost controls in place.
Technology and integrations
Platform
- Databricks
- Delta Lake
- Unity Catalog
Processing
- Apache Spark
- Structured streaming
- MLflow
Cloud storage
- AWS S3
- Azure Data Lake
- Google Cloud Storage
BI & AI
- Power BI
- ML model pipelines
Security and compliance
Governed with Unity Catalog
Built with Unity Catalog governance, access controls and audit trails. 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
BI and AI ran on separate systems, doubling cost and effort.
Solution
A Databricks lakehouse serving analytics and real-time scoring from one source.
Impact
93% of fraud caught pre-payout, ~$4.2M saved annually.
Challenge
Unstructured docs and images were locked out of analytics.
Solution
A Delta Lake platform unifying all data types for BI and ML.
Impact
40% faster valuations on one governed foundation.
Challenge
Heavy data workloads slowed personalization.
Solution
Elastic Spark compute on Databricks feeding the recommendation engine.
Impact
+28% lift in conversion rate.
Sector-anonymised outcomes shown until named clients are approved.
Why Noseberry
Why choose Noseberry for Databricks
Specialist
AI, Cloud and Data is our core, no generalist dilution.
AI-ready
We build Databricks specifically to power machine learning.
Cost-governed
Cluster tuning and governance from day one.
Proven at scale
250+ solutions delivered across 20+ countries.
Databricks, answered.
For data engineering, analytics and machine learning on one lakehouse platform, especially with large or unstructured data.
Databricks is a lakehouse strong in ML and unstructured data. Snowflake is strongest in SQL analytics and warehousing. Many organisations use both, and we advise on the right split.
Yes. We migrate legacy lakes, warehouses and Spark workloads onto Databricks in phases.
Yes. Its lakehouse and MLflow make it a strong platform for training and serving AI models on governed data.
Through cluster right-sizing, autoscaling, and governance with Unity Catalog, so compute spend stays efficient.
Want one platform for data and AI?
Book your free 30-minute strategy session and we will scope your Databricks build.
Book nowRelated resources
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