Databricks consulting, end to end.
Fidan Consulting is a Databricks partner and builds lakehouse platforms that keep running after handover: architecture, Unity Catalog, pipelines, infrastructure as code and operations – on Azure, AWS or Google Cloud.
How we help with Databricks.
Architecture and workspace design
Medallion structure, catalog and schema design in Unity Catalog, domain boundaries and naming conventions – before the first pipeline runs.
- Workspace, catalog and environment structure (dev, test, prod)
- Decision paper with alternatives, risks and costs
- Review of existing platforms
Migration to Databricks
From Hadoop, Azure Synapse, classic data warehouses or on-premises servers – step by step, without stopping your reporting.
- Inventory of sources, load processes and reports
- Replacing the old jobs with ETL/ELT pipelines on Databricks
- Parallel run and sign-off with the business units
Data engineering
Pipelines that are traceable and testable: Lakeflow and Delta Live Tables, merge frameworks, historisation, Data Vault 2.0.
- Bronze, silver, gold with clean historisation
- Data quality with rules and tests
- Connecting SAP, CRM, APIs and files
Governance and permissions
Unity Catalog as the central place for access, lineage and approvals – with a role concept that business units understand.
- Role and permissions concept
- Data products for business units and end customers
- Lineage and documentation
Infrastructure as code and CI/CD
Workspaces, clusters, jobs and permissions from code: Terraform, Databricks Asset Bundles, Azure DevOps or GitHub Actions.
- Reproducible environments
- Deployments with tests and approvals
- No click-through configuration nobody can trace
Operations and enablement
After the build: new use cases on the existing platform, cost control, reviews and training for your team.
- Cluster strategy and cost control
- Training for developers and business units
- Handover instead of dependency
Azure, AWS or Google Cloud – Databricks runs on all three.
The platform is the same. The choice depends on what you already run, on your team and on your contracts – not on our preference.
Azure
Most common in our projects: Entra ID, Azure DevOps, Data Factory and Power BI are usually already in place. Infrastructure with Terraform or Bicep.
AWS
Databricks on AWS with IAM, S3 and the services there – for example in the redesign of a sports federation's data platform.
Google Cloud
Databricks on GCP, for instance replacing a Hadoop landscape in e-commerce – including Looker and Tableau connections.
Databricks partner, certified team.
Three Databricks certifications
- Databricks Certified Data Engineer Professional
- Databricks Certified Data Engineer Associate
- Databricks Certified Data Analyst Associate
Four Databricks accreditations
- Databricks Platform Architect
- Databricks Platform Administrator
- Databricks Lakehouse Fundamentals
- Databricks Generative AI Fundamentals
Databricks partner
Fidan Consulting is a Databricks partner (Bronze tier). The credentials belong to the managing director, who works in every project himself – more on the About us page.
Six Databricks projects, six industries.
Group-wide data platform
Build of the enterprise-wide platform with a data warehouse based on Data Vault 2.0, permissions concept in Unity Catalog and connection of SAP sources.
- Role: lead data engineer
- Azure Databricks, Unity Catalog, Data Vault 2.0
- SAP Datasphere, SAP EAM, SAP S/4, Power BI
From Hadoop to Databricks on GCP
Implementation lead for infrastructure, governance and Unity Catalog, ETL/ELT pipelines replacing the Hadoop jobs, training for developers and business units.
- Role: lead data engineer
- Databricks on Google Cloud, Unity Catalog
- Looker, Tableau
Databricks for reporting
Cloud-based data platform from source to data product: lakehouse with integrated data warehouse, governance structure, data products for business units and end customers.
- Role: lead data engineer
- Azure Databricks, governance
- Power BI
Redesign as a data mesh
Analysis of the existing data processes, design and implementation of a data mesh architecture, alignment of domain boundaries and data ownership with the business units.
- Role: lead data engineer
- Azure, Databricks, Microsoft Fabric
- Data mesh
Operations and new use cases
Support of the existing Databricks data platform and implementation of business use cases on the platform.
- Role: consultant, data engineer
- Azure, Databricks, Unity Catalog
- Power BI, Azure DevOps
Databricks redesign on AWS
Analysis of the system landscape and its weaknesses, new target architecture aligned with stakeholders, technical concept and end-to-end implementation.
- Role: data engineer
- Databricks on AWS
- AWS
We do not name customers. All projects, the team and the credentials are on the About us page.
Databricks health check.
For platforms already in operation: we review structure, costs, security and pipelines and tell you what to tackle first – regardless of who built the platform.
Duration: three days
Price: time and materials – rate and rough estimate up front
Result: written report
What we look at
- Workspace, catalog and environment structure
- Clusters, jobs and costs of the past months
- Unity Catalog: roles, permissions, lineage
- Pipelines: historisation, tests, error handling, restart
- Infrastructure as code and deployments
- Security: network, secrets, external access
Databricks or Microsoft Fabric?
Databricks is the more robust choice for complex engineering workloads, multiple clouds and many teams. Microsoft Fabric is often the faster entry for Power BI-oriented teams in a Microsoft environment. Both together works as well: Databricks for engineering, Fabric and Power BI for reporting. We assess both for your case – more under Microsoft Fabric consulting.
How the collaboration starts.
Intro call
30 minutes: what should the platform deliver, what exists already, who works with it? Afterwards both sides know whether it fits.
Review or decision paper
Existing platform: health check. New platform: target architecture with alternatives, risks and costs – you decide based on facts.
Delivery with your team
Build or migration in stages, with your team on board. Code, documentation and knowledge belong to you – the goal is that you can carry on without us.
Frequently asked questions about Databricks consulting
Are you a Databricks partner?
Yes. Fidan Consulting is a Databricks partner (Bronze tier). On top of that, the managing director holds three Databricks certifications and four accreditations and works in every project himself.
Azure, AWS or Google Cloud?
Databricks works the same on all three clouds. We work most often on Azure but have also built platforms on AWS and Google Cloud. The choice depends on what you already run, on your contracts and on your team.
Do you migrate existing platforms to Databricks?
Yes – from Hadoop, Azure Synapse, classic data warehouses or on-premises servers. We start with an inventory of sources, load processes and reports, build the new pipelines in parallel and switch over once the business units have signed off.
Do you also take care of ongoing operations?
Yes. We look after existing Databricks platforms, implement new use cases on them, keep an eye on costs and clusters and train your team so it can develop the platform further itself.
Do you work with our team or on your own?
Both. Either we take on architecture and engineering with project responsibility, or we strengthen your team with reviews, pair programming and training. Code, documentation and knowledge stay with you in any case.
What does Databricks consulting cost?
Time and materials with a daily or hourly rate you know up front; a fixed price is possible for clearly scoped packages. You receive a concrete quote after the intro call.
Intro call
Let's talk about your Databricks platform.
30 minutes are enough to clarify whether a new build, a migration or a review is next – and whether we are the right people for it.