Smart Data Warehousing
Fast, Scalable Access to the Data That Matters
A data warehouse centralises your company’s data so every team queries the same numbers and gets the same answer. I build and optimise warehouses on Snowflake, Databricks, Redshift, and BigQuery handling modelling, ingestion, and performance tuning end to end. Properly built, a warehouse cuts report generation from hours to seconds.
Your data warehouse should empower decisions, not slow them down. I build and optimize modern, cloud-based warehouses that unify your data, speed up queries, and support accurate, real-time reporting.
What You Get
- Warehouse audit report with ranked optimisation opportunities
- dbt project with incremental models, schema tests, and documentation
- Ingestion layer (Fivetran, custom ELT, or Kafka consumer)
- Semantic layer / metrics definitions connected to your BI tool
- Cost governance setup (auto-suspend, query timeouts, budget alerts)
- Runbook covering model refresh cadence, SLA, and failure recovery
Solving Key Warehousing Challenges
Reports that take 30+ minutes to run
Full table scans on unpartitioned, unoptimized data are slow and expensive. I implement clustering keys, materialized views, and incremental dbt models that cut typical query times from minutes to seconds.
Five teams, five different numbers for the same metric
Conflicting revenue definitions are a governance failure, not a tooling failure. I define a single dbt semantic layer where "ARR" has one definition, one owner, and one certified dataset.
Warehouse bill spiking every month with no visibility
Uncontrolled warehouse sizes, full refreshes, and forgotten scheduled queries can triple cloud costs in a quarter. I implement warehouse-level spend monitoring and query optimization that typically reduces compute costs 30–50%.
Core Principles of My Warehousing Approach
Single source of truth via dbt semantic layer
All KPIs are defined once in dbt, tested against contracts, and exposed to BI tools via a consistent semantic layer. No more conflicting numbers.
Incremental-first modelling
I model the warehouse bottom-up as a series of incremental, idempotent transforms. A failed nightly run can be retried in minutes, not hours.
Cost-governed compute
Every Snowflake virtual warehouse or Databricks SQL warehouse has an auto-suspend policy, query timeout, and monthly budget alert. Cost is a first-class concern.
My Approach
Warehouse audit (weeks 1–2)
I analyse your current schema, slowest queries, and cost breakdown. You get a ranked list of optimization opportunities.
Modelling redesign (weeks 2–6)
I rewrite key models as incremental dbt models with schema contracts and data quality tests. Each model ships with documentation.
Ingestion layer (weeks 4–10)
I build or migrate ingestion pipelines (Fivetran, custom ELT, Kafka) to land raw data in a consistent Bronze layer.
BI handover (final week)
I connect the Gold layer to your BI tool, document data lineage, and hand over a runbook.
Glossary
- OLAP (Online Analytical Processing)
- A class of database workload optimised for complex aggregations and historical analysis across large datasets, as opposed to OLTP which handles high-frequency transactional writes.
- Star schema
- A dimensional modelling pattern where a central fact table (events, transactions) is surrounded by dimension tables (customers, products, dates). The standard structure for analytical queries in a warehouse.
- ELT vs ETL
- ETL transforms data before loading it into the target; ELT loads raw data first and transforms it inside the warehouse using tools like dbt. ELT is now dominant in cloud warehouses.
- Delta Lake
- An open-source storage layer that adds ACID transactions, schema enforcement, and time-travel queries to data stored in object storage (S3, ADLS). Native to Databricks; also supported by open-source Spark.
- Data mart
- A subject-specific subset of a warehouse (e.g. a marketing mart, a finance mart) that exposes only the tables and metrics relevant to a particular team improving query performance and access control.
Common Questions
How long does it take to build a data warehouse?
A greenfield warehouse build typically takes 6–12 weeks: 1–2 weeks for audit and design, 4–8 weeks for modelling and ingestion, 1 week for BI layer handover. Migrating from an existing warehouse (e.g. Redshift to Snowflake) takes 8–14 weeks depending on schema complexity.
Which cloud data warehouse should I use Snowflake, BigQuery, Redshift, or Databricks?
Each platform has genuine strengths: Snowflake offers predictable pricing and strong SQL tooling; BigQuery fits well in Google Cloud-native environments; Databricks SQL is a natural choice when you need a warehouse and lakehouse on the same platform; Redshift remains a solid option in AWS-heavy stacks. The right choice depends on your existing infrastructure, team expertise, and workload profile. I help you evaluate the options and make a recommendation based on your specific situation.
What does a data warehousing project cost?
Pricing depends on scope a focused optimisation engagement (tuning an existing warehouse) is at the lower end; a full build with ingestion, modelling, and BI layer is at the upper end. I work on a project or retainer basis and provide a fixed estimate after the discovery call.
Ready to Build Better Data Systems?
Let's discuss how I can help you modernize your data infrastructure and unlock the full potential of your data.
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