Data Lakehouse Architecture That Connects Governed Data, Analytics and AI

Data lakehouse architecture is a design approach that brings scalable object or lake storage together with data-management capabilities associated with analytical warehouses, such as governed tables, transactional consistency, metadata, security, quality and reliable SQL or AI access. The architecture still needs explicit decisions for ingestion, table formats, compute, modelling, governance, observability and workload placement.

A data lake primarily provides flexible storage for diverse data, while a warehouse is optimised around structured analytical workloads and managed query performance. A lakehouse aims to support multiple analytical and AI workloads on governed lake-based data. The practical boundary depends on platform capabilities, workload requirements, operating model, performance, cost and existing investments.

No. Transactional systems, latency-critical operational applications, specialised databases, regulatory boundaries or established warehouse workloads may be better left in their existing platforms. DataConsultant evaluates workload characteristics and integration needs before recommending what should move, coexist, remain federated or be retired.

Scope can include current-state assessment, workload and non-functional requirement profiling, target logical and physical architecture, ingestion and orchestration patterns, table-format and storage decisions, modelling and data-layer conventions, governance, security, metadata, quality, observability, resilience, platform options, migration sequencing, operating model and implementation guardrails. Final scope is agreed during discovery.

Typical outputs can include a current-state findings pack, workload catalogue, architecture principles, target-state diagrams, platform and format decision records, ingestion and serving patterns, governance and security control design, migration roadmap, operating-model responsibilities, cost and FinOps assumptions, implementation standards, risks, dependencies and an executive decision summary.

The design can evaluate relevant capabilities across environments such as Databricks, Microsoft Fabric and OneLake, Snowflake, AWS analytics services, Google Cloud data services and open technologies where appropriate. Platform selection remains requirements-led and vendor-neutral unless a specific technology has already been mandated by the client.

The decision should consider the selected platform and engines, interoperability requirements, write and maintenance patterns, schema and partition evolution, transactional behaviour, governance integration, streaming needs, operational tooling and migration constraints. A table format should not be selected solely because it is popular or supported by one product.

Architecture can define identity and access patterns, data classification, encryption expectations, metadata and lineage, data-quality controls, lifecycle and retention requirements, environment separation, audit evidence, monitoring, third-party boundaries and accountable decision rights. Applicable legal, regulatory and sector obligations must be confirmed for the client’s jurisdictions and use cases.

Yes, when the business case and constraints justify it. The design can address distributed sources, cloud and on-premises connectivity, data movement, federation, interoperability, identity, encryption, residency, network boundaries, operating responsibility and the cost consequences of cross-environment data transfer.

Useful inputs include source-system and workload inventories, existing architecture diagrams, data volumes and growth, latency and availability requirements, security and privacy constraints, cloud or platform standards, current spend information, data-quality and governance findings, active transformation plans, key stakeholders and known migration deadlines.

A reliable duration is confirmed after scoping. Timing depends on the number of source systems and workloads, current-state evidence, platform options, stakeholder availability, security and governance depth, whether proof-of-concept work is included, and the level of migration or implementation detail required.

DataConsultant does not publish a fixed fee for this service. Pricing is scope-led and depends on assessment depth, workload and source count, platform options, hybrid or multi-cloud complexity, governance and control requirements, detailed design, workshops, proof-of-concept or migration support, documentation and implementation assurance. Public comparable India-market references are shown on this page only as scoping guidance, not as DataConsultant pricing.

Yes. Implementation support can be scoped separately for design assurance, migration planning, engineering guidance, governance enablement, quality and metadata controls, platform operating practices, testing, cutover readiness and architecture governance. Responsibilities and acceptance criteria are documented before implementation begins.

Yes. The engagement can work with internal data and cloud teams, platform vendors, systems integrators, security and risk functions, governance teams and managed-service providers. Decision rights, evidence ownership, dependencies, interfaces and escalation paths are clarified during mobilisation.