A traditional data warehouse stores processed, structured data in a proprietary format optimised for SQL queries and BI reporting. A lakehouse combines the low-cost storage of a data lake (raw files in object storage like S3 or GCS) with warehouse-style query engines (Spark, Trino, BigQuery Omni) — giving you the flexibility to store any data format while still running performant analytical queries. For most modern organisations, a cloud data warehouse like Snowflake or BigQuery with a lakehouse extension layer is the practical answer. We'll recommend the right architecture during your discovery session.

A production-ready data warehouse with 5–10 source integrations, a core dbt transformation layer, and initial BI dashboards typically takes 6–10 weeks from discovery kickoff to first live dashboard. The exact timeline depends on the number of source systems, data volume, access complexity, and your team's availability for requirements review. We break delivery into fortnightly milestones so you see working output throughout the engagement, not just at the end.

Yes — warehouse migration is one of our most common engagements. We've migrated from on-premises SQL Server, Oracle, Teradata, and legacy Redshift environments to modern cloud platforms. Our approach uses a parallel-run strategy: new and old systems produce the same outputs simultaneously until data parity is validated, then traffic is switched with zero downtime. We also use this as an opportunity to rearchitect data models that have accumulated technical debt, rather than simply lifting and shifting legacy complexity.

Data quality is enforced at three layers. At ingestion, we implement row counts, null checks, and schema validation before data is written to the warehouse. In the transformation layer, we embed dbt tests — uniqueness, not-null, referential integrity, and custom threshold assertions — that run on every model rebuild. At the BI layer, we implement data freshness monitoring and automated alerting so anomalies are caught before users report them. Every pipeline we build includes a data quality dashboard showing test pass rates and freshness status across all critical models.

We start with a data audit — a structured 2-week assessment of your current stack, data models, pipeline architecture, and analytics maturity. We identify what's working well and should be retained, what needs to be refactored, and what's missing entirely. From there we produce a prioritised roadmap. Many clients find the audit alone is high-value: it surfaces trust issues, undocumented sources, and quick wins they weren't aware of. We don't assume everything needs to be replaced — we recommend the minimum change required to achieve the desired outcome.

We offer three post-delivery models. A knowledge-transfer model where we train your internal team to own and extend the platform, with documentation and a handover sprint. A hybrid model where your team owns day-to-day operations and we're on retainer for architecture decisions, major new integrations, and incident escalations. A fully managed model where All7Day runs your data platform end-to-end — monitoring, pipeline maintenance, schema updates, and feature development — on an agreed monthly retainer. Most clients start with knowledge transfer and optionally add retainer support once they understand their operational load.