Common signs you need a stronger data foundation
- Every report connects directly to source systems
- The same transformation logic is rebuilt in multiple places
- Excel files are used as hidden staging layers
- Reports break when a source changes
- Different teams maintain separate versions of the same data
- Refreshes are slow, fragile, or difficult to monitor
- AI projects cannot access a trusted data layer
- Historical reporting is limited because source systems overwrite data
- Data-quality checks happen manually after reporting problems appear
What Datazeb can deliver
Data Warehouse Design
Create a structured analytical store designed around reporting and business history.
Lakehouse / Modern Data Platform
Design an appropriate modern platform where scale, semi-structured data, or broader analytics needs justify it.
ETL / ELT Pipelines
Automate ingestion and transformation from databases, APIs, cloud apps, files, and business systems.
Curated Data Models
Create reusable business-ready layers for finance, sales, operations, customers, products, inventory, and other domains.
Historical Data
Preserve analytical history where source systems only expose current state.
Data Quality & Validation
Add automated checks for missing data, duplicates, mapping gaps, schema changes, and reconciliation issues.
Orchestration & Monitoring
Schedule dependencies, retries, alerts, logging, and failure handling for production pipelines.
Analytics & AI Integration
Provide trusted data for Power BI, dashboards, AI assistants, automation, and downstream applications.
The platform flow
- Source Systems
- Ingest
- Transform & Validate
- Trusted Data Layer
- BI / AI / Automation
The exact technology can vary. The architecture should follow the organisation’s data volume, latency needs, source systems, team capability, governance requirements, and budget.
Data warehouse vs lakehouse vs direct reporting
Use the simplest architecture that supports the business
Not every organisation needs a large data platform. Datazeb should assess whether the right pattern is:
- Direct reporting for a small stable use case
- A central relational data warehouse
- A cloud warehouse
- A lakehouse architecture
- A hybrid approach
Source integration
Datazeb can integrate approved data from:
- ERP and accounting systems
- CRM platforms
- POS and ecommerce
- Operational databases
- APIs
- SaaS applications
- CSV / Excel files
- Cloud storage
- Legacy systems where accessible
Each source should have clear ownership, refresh expectations, and a documented ingestion method.
Transformation and business logic
Move repeated logic into a controlled layer
- Data cleaning
- Standardisation
- Joins and matching
- Reference-data mapping
- Business rules
- Currency / calendar handling where required
- Derived fields
- Historical tracking
Centralising stable transformation logic reduces duplicated calculations across reports and downstream systems.
Curated business data models
Curated layers can organise data around business domains such as:
- Finance
- Sales
- Customers
- Products
- Inventory
- Operations
- Projects
- Suppliers
- Locations / entities
These models should be designed around the questions the business needs to answer, not around source-system table names.
Historical reporting
Preserve the context that operational systems often lose
- Status history
- Customer / product changes
- Point-in-time balances
- Snapshot reporting
- Entity / hierarchy changes
- Historical KPI reconstruction where feasible
Historical design should be deliberate because storing every change can add complexity without business value.
Data quality and reconciliation
- Missing records
- Duplicate identifiers
- Unmapped reference values
- Schema changes
- Unexpected row-count changes
- Control-total differences
- Late source feeds
- Invalid dates or relationships
Validation should run as part of the pipeline so data issues are visible before users discover them in reports.
Pipeline orchestration
- Scheduling
- Dependency management
- Retry logic
- Incremental loads
- Failure alerts
- Logging
- Run history
- Recovery procedures
Production pipelines should be observable and supportable, not a collection of scripts that only one person understands.
Security and access
- Least-privilege source access
- Service-account controls
- Credential management
- Environment separation
- Restricted sensitive fields
- Role-based downstream access
- Documented ownership
Security design should follow client requirements and the sensitivity of each data domain.
Power BI integration
Build reporting on a stable analytical layer
A stronger data platform can simplify Power BI by moving repeated data preparation upstream.
- Cleaner semantic models
- Faster refresh patterns
- Shared business dimensions
- Consistent historical logic
- Reduced Power Query duplication
- Simpler report maintenance
AI and automation readiness
Reliable AI starts with reliable access to business data
A curated data layer can support:
- AI assistants over governed business data
- Conversational analytics
- Agentic workflows
- Automated exception detection
- Workflow decisions based on trusted records
- Management-summary generation
AI should use approved curated sources rather than unrestricted access to every raw system.
Microsoft Fabric and cloud-platform options
Where appropriate, Datazeb can assess platform options such as Microsoft Fabric or other cloud data services based on:
- Current Power BI footprint
- Data volume
- Engineering needs
- Warehouse / lakehouse requirements
- Governance
- Team skills
- Operating cost
- Future AI / analytics roadmap
Platform choice should follow the architecture, not lead it.
Migrating from spreadsheet or report-driven data preparation
- Identify repeated Power Query / Excel logic
- Move stable transformations upstream
- Replace manual staging files
- Automate source ingestion
- Standardise mappings
- Preserve necessary user-owned inputs
The objective is to remove fragile dependencies while keeping practical business ownership where it belongs.
Existing data platform review
Improve what already exists before deciding to rebuild
Datazeb can review:
- Current warehouse / lakehouse
- ETL / ELT pipelines
- Data models
- Performance
- Source integrations
- Data quality
- Monitoring
- Security
- Power BI dependencies
- Documentation and ownership
The result may be targeted optimisation, pipeline stabilisation, model redesign, or a phased platform modernisation.
Why Datazeb for modern data platforms
- Data engineering and Power BI capability in one delivery model
- Strong SQL, Python, APIs, databases, cloud, and integration experience
- Architecture sized to the actual business need
- Focus on data quality, monitoring, and maintainability
- Ability to connect the platform to analytics, AI, and automation
- Flexible project and managed-support models
- Senior-led global delivery
Engagement options
- Data architecture review
- Data warehouse implementation
- Modern data platform build
- ETL / ELT pipeline development
- Platform modernisation
- Historical data design
- Data-quality framework
- Managed data engineering support
How we work
- Understand
Clarify reporting, analytics, AI, source systems, data volume, users, latency, governance, and support requirements. - Assess
Review existing architecture, data flows, transformations, pain points, technical debt, and platform constraints. - Design
Define target architecture, source ingestion, storage, transformation, curated models, security, monitoring, and ownership. - Build
Develop pipelines, data layers, validation, orchestration, documentation, and downstream integrations. - Validate & Support
Reconcile outputs, stabilise production loads, document operations, and improve the platform over time.
Frequently asked questions
Do we need a data warehouse before using Power BI?
Not always. A warehouse becomes more valuable when multiple sources, historical reporting, reuse, data quality, or complex transformation justify a central analytical layer.
Do we need Microsoft Fabric?
Not necessarily. Fabric can be considered where it fits the architecture, current Microsoft environment, engineering needs, governance, and cost model.
Can you modernise an existing warehouse instead of rebuilding it?
Yes. Datazeb can assess the current platform and improve pipelines, models, performance, quality, or monitoring without replacing useful components.
Can the same platform support AI?
Yes. A curated governed data layer can support selected AI and automation use cases where appropriate access and controls are defined.
Can you integrate APIs and legacy data sources?
Often, yes. The method depends on available APIs, database access, files, connectors, and technical constraints.
Do you provide ongoing data engineering support?
Yes. Managed support can cover pipeline monitoring, source changes, new integrations, quality issues, optimisation, and platform growth.