Big data architecture, enterprise data architecture, and data management architecture shape how organisations store, move, and use data. This guide explains core patterns and how they connect to strategy.
Big data architecture, enterprise data architecture, and data management architecture are terms used to describe how organisations structure their data environments — where data is stored, how it flows between systems, and how it is made available for analytics, reporting, and operations.
This page explains the main concepts and patterns in plain language. It is written for leadership teams who need to evaluate architecture decisions — not for technical implementation.
Enterprise data architecture defines the overall design of an organisation’s data landscape. It covers storage patterns, integration approaches, and how different data domains connect. It answers questions such as: Where does transactional data live? Where does analytical data come from? How do systems share data?
Data management architecture extends this to the operational layer — the processes, pipelines, and controls that move data from source to destination. It includes data ingestion architecture (how data is captured and brought into central systems) and data pipeline architecture (how data is transformed, validated, and delivered to consumers). Without a coherent data management architecture, organisations build point-to-point integrations that become unmanageable as systems multiply.
Data governance architecture sits alongside technical architecture. It defines how governance — ownership, quality standards, lineage, and access controls — is embedded in the design. Governance cannot be retrofitted. It must be considered from the outset.
Organisations typically adopt one or more of these patterns, often in combination.
Data warehouse — A centralised store of cleansed, structured data optimised for reporting and analytics. Data is extracted from source systems, transformed to a common model, and loaded (ETL). The warehouse remains the dominant pattern for business intelligence and structured reporting.
Data lakehouse — A hybrid pattern that combines the flexibility of a data lake (raw and semi-structured storage) with the query performance and governance of a data warehouse. Platforms such as Databricks and Snowflake support lakehouse-style architectures. The lakehouse is increasingly used where organisations need both analytics on structured data and exploration of raw event or sensor data.
Data mesh — A decentralised approach. Data ownership stays with business domains (sales, finance, operations). Each domain exposes data as products with defined contracts. A data mesh reduces bottlenecks at a central team but requires mature governance and domain ownership.
Data fabric — A logical layer that connects heterogeneous data sources without physically centralising them. It provides discovery, access, and governance across distributed systems. Vendors offer fabric-style capabilities; implementation depends on the specific environment.
Cloud platforms such as AWS and Azure provide the underlying storage, compute, and integration services that support these patterns. Architecture decisions are about which pattern fits the organisation — not which cloud to use.
Data engineering architecture describes the technical design of pipelines, transformation logic, and orchestration. It determines how quickly data can be refreshed, how errors are handled, and how changes in source systems are accommodated. Poor data engineering architecture leads to fragile pipelines that break when sources change.
Master data management architecture refers to how master data — customer, supplier, product, location — is stored, synchronised, and distributed across the organisation. It may use a dedicated MDM hub or a governed layer within a data warehouse or lakehouse. Master data management architecture must align with master data governance — governance defines the rules; architecture implements them.
Architecture decisions are not purely technical. They have cost, risk, and organisational implications.
Fit for purpose. Does the architecture support the decisions the organisation needs to make? A data warehouse may suffice for standard reporting. Advanced analytics, real-time use cases, or multi-domain exploration may require a lakehouse or mesh approach.
Proportionate to maturity. A data mesh assumes strong domain ownership and governance. Organisations without that maturity will struggle. Enterprise data architecture should match organisational capability.
Governance from the start. Data governance architecture — how ownership, quality, and lineage are designed in — determines whether the system can be trusted. Architecture that ignores governance creates technical debt.
Connection to strategy. Architecture should serve enterprise data strategy — not drive it. Strategy defines intent; architecture enables delivery. Independent data strategy advisory helps leadership evaluate whether proposed architecture aligns with business goals, risk tolerance, and operating model.