Data Warehouse Development

Data Source Discovery and Inventory

Every data warehouse project starts with understanding what data exists and where it lives. We conduct a comprehensive data source inventory to map your organization's data landscape, identifying operational databases, SaaS applications, file systems, and external data feeds that contain valuable information. This discovery phase documents data schemas, update frequencies, data volumes, and potential quality issues. This foundation ensures we build a warehouse that captures the right data to support your business objectives.

Enterprise-Grade Architecture Design

With a clear understanding of your data sources, we design the technical architecture that will house and organize your data. This includes selecting the optimal platform (Snowflake, BigQuery, Redshift, or others) based on your requirements, designing dimensional models using star or snowflake schemas, and establishing data organization strategies through partitioning and clustering. We architect solutions that balance performance, cost, and maintainability while supporting both current needs and future growth. Our architecture documentation provides the blueprint that guides all subsequent development work.

Tailored Analytics Layer for Your Needs

We work with stakeholders to understand specific use cases and access patterns, then design analytics layers that make data accessible and performant for those needs. This includes creating aggregation tables for common reporting metrics, building subject-oriented data marts for different business functions, and implementing slowly changing dimension strategies to track historical changes. By understanding how analysts and dashboards will query the data, we structure the warehouse to deliver fast, efficient results for the questions your business actually asks.

Reliable ETL Pipeline Implementation

With the architecture and analytics layer defined, we build the ETL (Extract, Transform, Load) pipelines that move data from source systems into the warehouse. These pipelines handle data extraction on appropriate schedules, apply transformations and business logic, and load data into the dimensional models we've designed. Our pipelines include data quality validation at each step, error handling and alerting to catch issues quickly, and audit logging to track data lineage. We implement incremental loading patterns to minimize processing time and costs while ensuring your analytics teams always have access to current, accurate data.

Performance Optimization and Cost Management

Once operational, we continuously optimize the warehouse for both performance and cost efficiency. This includes analyzing query patterns to identify optimization opportunities, implementing materialized views for frequently accessed aggregations, and tuning partitioning and clustering strategies based on actual usage. We configure workload management to prioritize critical queries and right-size compute resources to prevent unnecessary expenses. The result is a warehouse that delivers fast query performance for dashboards and ad-hoc analysis while maintaining cost efficiency.