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Data platform modernisation services in Europe
Data platform modernisation services help businesses review and improve legacy data environments, cloud platforms and data architecture. Work can include migration planning, platform integration, data engineering and changes to analytics infrastructure.
The employer chooses the talent, agrees the systems, modernisation scope, deliverables, timeline and rate, then manages the collaboration directly.
What data platform modernisation services cover
Platform modernisation services can help organisations upgrade legacy data environments, migrate workloads, redesign architecture and improve how data is integrated, processed and used. Define the work around your existing systems and intended business outcomes.
Legacy data platform modernisation
Modernise older data platforms that need improvements to their architecture, integrations, maintainability or ability to support current business requirements.
Work may include:
- Reviewing legacy platforms
- Identifying technical dependencies
- Mapping existing data workloads
- Documenting system limitations
- Prioritising modernisation activities
Cloud data platform modernisation
Cloud modernisation can involve moving existing data workloads, storage and processing activities from older or fragmented environments into an agreed cloud-based platform.
Data platform migration
Plan and implement the movement of data, pipelines and workloads between platforms. The scope can include migration sequencing, technical dependencies and transition requirements.
Data estate modernisation
Review connected databases, data warehouses, pipelines and analytical systems as part of a wider modernisation job involving several platforms or business functions.
Data architecture redesign
Update the structure of data platforms, storage, integrations and processing components to support agreed reporting, analytics and operational requirements.
Data pipeline and integration modernisation
Improve or rebuild agreed data pipelines, integrations and transformation workflows where existing processes need updating or connection with newer platforms.
Data warehouse modernisation
Modernise existing warehouse environments through architecture improvements, platform migration or integration with newer data and analytics systems.
Data platform consolidation
Consolidate selected databases, analytical environments or overlapping data platforms where a more organised architecture supports the agreed business requirements.
Modernisation for analytics and AI
Platform improvements can support broader business intelligence, analytics and AI initiatives by addressing data availability, integration and technical infrastructure requirements.
When businesses need data platform modernisation
Data platform modernisation can help when older technology, fragmented data systems or new business requirements make existing data environments harder to maintain, integrate or extend.
Legacy systems need improvement
Older databases and processing platforms may still support important business workloads but require updates to accommodate newer applications, integrations or analytical requirements.
Data platforms are fragmented
Different departments may operate separate databases, warehouses and analytics environments. Modernisation can help define how these systems should connect or be consolidated.
Cloud migration needs technical planning
Moving existing data workloads to a newer environment may require architecture decisions, dependency mapping, pipeline changes and a defined transition sequence.
Prepare the current data environment
Useful starting information includes:
- Existing data platforms and databases
- Connected source systems
- Current data pipelines and integrations
- Reporting and analytical workloads
- Known legacy system limitations
- Proposed target platforms
- Expected modernisation outcomes
Do not replace your employees. Reinforce them.
Data platform modernisation deliverables
A data platform modernisation job can cover current-state assessment, architecture planning, platform migration, implementation, validation and technical handover.
Current data environment review
At the beginning of the job, the employer and talent can review:
- Existing data architecture
- Current platforms and databases
- Important business and analytical workloads
- Data pipelines and integrations
- Known technical constraints
- Expected modernisation outcomes
Platform modernisation and migration
The talent carries out the agreed architecture, migration or implementation work.
Deliverables may include modernisation plans, updated architecture designs, migrated workloads, redesigned pipelines, platform configurations or agreed technical improvements.
Testing and technical validation
Agree how migrated data, workloads, integrations and redesigned components will be checked against the defined requirements.
Record known limitations, outstanding dependencies and any additional transition activities identified during the work.
Documentation and handover
Where relevant, include architecture diagrams, migration records, data pipeline documentation, platform configuration information and operational guidance for the employer's team.
Skills involved in data platform modernisation
The relevant technical expertise depends on your current data estate, target platform and whether the work involves assessment, architecture redesign, migration or data engineering.
Data platform consulting
Data platform consulting experience can support current-state assessments, modernisation priorities, platform migration planning and coordination across connected data systems.
Data architecture experience
Data architects can help define the target platform structure, data flows, storage arrangements and technical dependencies involved in modernisation.
Data engineering experience
Data engineers can support hands-on pipeline development, data transformations, integrations and technical data migration activities.
Data warehouse experience
Data warehouse specialists can contribute to warehouse migration, architecture improvements and integration with newer analytics platforms.
Relevant platform experience
Modernising one database may require different expertise from a wider data-estate transformation involving several platforms, pipelines and technical teams.
Compare talent experience with your existing technologies and target environment.
How to describe your platform modernisation job
A clear data platform modernisation job identifies the current data systems, technical limitations, intended improvements and expected deliverables without prescribing every implementation decision.
Describe the modernisation outcome
Explain what the data platform work should achieve.
For example:
- Upgrade an existing legacy data platform
- Migrate data workloads to the cloud
- Consolidate databases and analytical systems
- Modernise data warehouses and pipelines
- Improve data infrastructure for analytics or AI
Identify the current systems
Describe the databases, warehouses, processing platforms, integrations and analytical workloads included in the job.
Share the technical requirements
Useful details include:
- Existing data architecture
- Source applications and systems
- Current pipelines and integrations
- Data volumes and processing needs
- Legacy platform limitations
- Proposed target environment
- Technical migration dependencies
Define the modernisation work
Explain whether the job requires:
- A defined platform assessment
- Architecture redesign and migration planning
- Platform migration or implementation
- A larger job divided into several projects
The employer and talent can discuss the technical requirements and agree the scope, timeline, deliverables and rate directly.
How to evaluate platform modernisation proposals
Compare experience with your existing and target data platforms, then discuss how the talent would approach architecture changes, migration dependencies, technical validation and handover.
Relevant modernisation experience
Review previous work involving legacy data systems, platform migration, cloud data environments or architecture improvements similar to your requirements.
Understanding the current environment
Ask how the talent would assess existing systems, workloads, integrations and technical constraints before defining the modernisation approach.
Data migration and transition planning
Discuss how workloads will be grouped, which components need redesign and how technical dependencies will influence migration activities.
Data architecture decisions
Ask how the proposed architecture will support agreed data processing, reporting, analytics and operational requirements.
Technical documentation and handover
Agree how architecture decisions, migration activities, remaining dependencies and operational requirements will be documented for your team.
The VirtualMasst team reviews and approves talent profiles. The employer assesses suitability and chooses the talent for the work.
Cost, timeline and engagement factors
The employer and talent agree the rate directly. Data volumes, legacy systems, migration complexity and platform requirements affect the work.
Existing data platform scope
Modernising one database or data platform can require different work from improving an estate involving several warehouses, systems and analytical environments.
Number of data workloads
The amount of reporting, analytics and data-processing work involved can affect the discovery, migration and validation requirements.
Data migration requirements
Moving databases, pipelines and dependent applications can introduce technical planning, implementation and transition activities.
Legacy platform constraints
Older systems, incomplete documentation and connected applications can increase the work required to understand and update the existing environment.
Target data platform
The destination architecture, cloud environment, data services and integration requirements can influence the modernisation work.
Pipelines and technical dependencies
The number of data flows, integrations, transformations and existing data-quality issues can affect implementation and migration sequencing.
Additional platform improvements
If further data engineering, analytics, warehousing or AI-readiness requirements emerge, the employer and talent can discuss and agree the additional work separately.
VirtualMasst facilitates pre-funding and payment through Stripe. Current platform charges are listed on Pricing.
Define your data platform modernisation job
Describe your current data platforms, legacy systems, workloads, technical dependencies and intended modernisation outcome. Post a job and discuss the architecture, migration or implementation requirements with relevant talent.
You choose the talent, agree the scope, timeline, deliverables and rate, manage the collaboration and approve the completed work.