Modern Data Platform & Data Warehouse
Build one reliable data foundation for analytics, AI and automation
Datazeb helps organisations bring data from ERP, CRM, finance, operational systems, APIs, files, and cloud applications into a structured, trusted data platform.
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
What Datazeb can deliver
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Data Warehouse Design
Create a structured analytical store designed around reporting and business history.
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Lakehouse / Modern Data Platform
Design an appropriate modern platform where scale, semi-structured data, or broader analytics needs justify it.
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ETL / ELT Pipelines
Automate ingestion and transformation from databases, APIs, cloud apps, files, and business systems.
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Curated Data Models
Create reusable business-ready layers for finance, sales, operations, customers, products, inventory, and other domains.
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Historical Data
Preserve analytical history where source systems only expose current state.
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Data Quality & Validation
Add automated checks for missing data, duplicates, mapping gaps, schema changes, and reconciliation issues.
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Orchestration & Monitoring
Schedule dependencies, retries, alerts, logging, and failure handling for production pipelines.
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Analytics & AI Integration
Provide trusted data for Power BI, dashboards, AI assistants, automation, and downstream applications.
Platform layers
A data platform sized to the reporting it supports
Sources
- Operational systems
- Files
- APIs
Ingestion
- Scheduled and incremental loads
Lake or warehouse
- Raw and history layers
- Modelled data
Transformation
- Business rules
- Quality checks
Semantic layer
- Governed KPIs
- Security
BI and AI
- Power BI
- AI and automation
In detail
Explore the detail
Data warehouse vs lakehouse vs direct reportingUse 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: Each source should have clear ownership, refresh expectations, and a documented ingestion method.
- ERP and accounting systems
- CRM platforms
- POS and ecommerce
- Operational databases
- APIs
- SaaS applications
- CSV / Excel files
- Cloud storage
- Legacy systems where accessible
Transformation and business logicMove repeated logic into a controlled layer
Centralising stable transformation logic reduces duplicated calculations across reports and downstream systems.
- Data cleaning
- Standardisation
- Joins and matching
- Reference-data mapping
- Business rules
- Currency / calendar handling where required
- Derived fields
- Historical tracking
Curated business data models
Curated layers can organise data around business domains such as: These models should be designed around the questions the business needs to answer, not around source-system table names.
- Finance
- Sales
- Customers
- Products
- Inventory
- Operations
- Projects
- Suppliers
- Locations / entities
Historical reportingPreserve the context that operational systems often lose
Historical design should be deliberate because storing every change can add complexity without business value.
- Status history
- Customer / product changes
- Point-in-time balances
- Snapshot reporting
- Entity / hierarchy changes
- Historical KPI reconstruction where feasible
Pipeline orchestration
Production pipelines should be observable and supportable, not a collection of scripts that only one person understands.
- Scheduling
- Dependency management
- Retry logic
- Incremental loads
- Failure alerts
- Logging
- Run history
- Recovery procedures
Security and access
Security design should follow client requirements and the sensitivity of each data domain.
- Least-privilege source access
- Service-account controls
- Credential management
- Environment separation
- Restricted sensitive fields
- Role-based downstream access
- Documented ownership
AI and automation readinessReliable AI starts with reliable access to business data
A curated data layer can support: AI should use approved curated sources rather than unrestricted access to every raw system.
- AI assistants over governed business data
- Conversational analytics
- Agentic workflows
- Automated exception detection
- Workflow decisions based on trusted records
- Management-summary generation
FAQ
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.
Learn it yourself · Datazeb Training
Learn Microsoft Fabric with Datazeb Training
Teach your team how Fabric fits together – lakehouse, warehouse, pipelines and semantic models – in a small live group.
training.datazeb.com
Build the data foundation once – then reuse it across reporting, AI and automation
If your reporting depends on direct system connections, repeated transformations, manual staging files, or duplicated data logic, tell us how the environment works today. Datazeb can help design a practical central data layer that is easier to trust, maintain, and extend.