A traditional data warehouse is primarily designed for structured data and business reporting. Databricks uses a lakehouse architecture that supports structured and unstructured data, streaming, data engineering, machine learning and AI on the same platform. Some organisations move workloads from Amazon Redshift or Snowflake to Databricks; others use the platforms together, with Databricks handling data engineering and AI while the warehouse continues to support particular reporting workloads. The right architecture depends on your data, workloads and existing investment.
Build trusted data and AI with Databricks
From migration and governed data pipelines to production AI and managed operations, Ardent’s certified Databricks engineers help organisations turn complex data into trusted reporting, reliable automation and AI built on strong governance.
TRUST AND PROOF POINTS
Since 2008
Engineering and operating data platforms for organisations worldwide.
80% faster reporting
Reduced reporting turnaround for a Fortune 500 broadcaster after migrating to Databricks.
Databricks partner
Certified engineers with practical delivery experience.
ISO 27001
Information security supported by independently certified processes.
WHY DATABRICKS
One platform for data, analytics and AI
Many organisations still run reporting, data engineering and AI across separate platforms. That creates duplicated pipelines, fragmented governance and teams working from different versions of the same information.
Databricks brings these workloads together through its lakehouse architecture, combining the flexibility of a data lake with the reliability and governance expected from a data warehouse. Unity Catalog provides consistent access controls, discovery and lineage across data and AI assets, including tables, files, functions and models.
That shared foundation supports BI and automation as well as machine learning and generative AI. Documents, PDFs, logs and other unstructured data can support RAG, AI Search and vector retrieval without building a separate data and governance layer for every new use case.
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Unified lakehouse architecture
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Governance with Unity Catalog
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Supports BI, automation and AI
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Structured and unstructured data together
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Designed to scale with your business
WHAT WE DELIVER
Data platform expertise for real business outcomes
Our Databricks services cover the full data lifecycle, from migration and data engineering to governance, AI and ongoing operations.
Data engineering and pipelines
Build automated, governed pipelines with validation and monitoring built in, catching data-quality issues before they reach reporting, operations or AI.
Platform and data migration
Plan phased migrations from legacy and cloud platforms, protecting critical workloads while right-sizing compute and using Serverless SQL where it adds value.
Data governance
Implement Unity Catalog for access control, lineage, discovery and governance across Databricks data and AI assets, creating a trusted foundation.
Generative AI and machine learning
Prepare vector-ready data for RAG, AI Search and machine learning, with MLOps and LLMOps support to move governed models into production.
Managed operations
Provide 24/7 monitoring, DBU and cost optimisation, performance tuning and incident response, freeing internal engineers to focus on development.
DATABRICKS MIGRATION PATHS
Move to Databricks from your current platform
A Databricks migration does not need to put live reporting or day-to-day operations at risk. We assess dependencies, prioritise the workloads that will create the most value and move them in controlled phases.
Amazon Redshift to Databricks
Move beyond reporting delays and traditional warehouse constraints, improving performance while creating a more flexible platform for analytics, data engineering and growing AI workloads.
SQL Server / Azure SQL to Databricks
Bring structured and unstructured data together on a governed lakehouse while protecting the reports and operational outputs your business depends on, without rebuilding the reporting layer from scratch.
Hadoop to Databricks
Retire the overhead of managing on-premises clusters and move data engineering workloads to an elastic, cloud-native lakehouse with modern tooling, managed infrastructure and scalable compute.
Snowflake to Databricks
Shift compute-intensive engineering, machine-learning and AI workloads onto Databricks while retaining Snowflake where it adds value, using migration, coexistence or a phased approach where appropriate.
Legacy Data Warehouse to Databricks
Move beyond ageing appliances, capacity constraints and inflexible infrastructure to a cloud lakehouse that can scale across reporting, data engineering and AI as your requirements grow.
FEATURED SUCCESS STORY
Automating data collection with OCR technology
Automating data collection with OCR technology
Read the Databricks success story →
80%
reduction in reporting turnaround
215M+
commercial records processed per hour
HOW WE WORK WITH YOU
A clear path to value
We follow a proven three-stage approach to help you get the most from Databricks, from initial architecture through to ongoing optimisation and support.
01
Assess and design
Assess your current architecture, workloads, governance needs and AI ambitions, then design the target Databricks architecture and phased roadmap around value, risk, dependencies and timeline.
02
Build and migrate
Build pipelines, implement Unity Catalog and move agreed workloads through controlled cutovers designed to protect live reporting, operational services and day-to-day business continuity.
03
Optimise and run
Tune performance, right-size compute and control Databricks costs, then establish monitoring and operational controls with 24/7 support and continuous improvement available through Ardent.
DATABRICKS FAQS
Frequently asked questions