Building a data warehouse is a complex task that requires careful consideration of business requirements, technology stack, and governance. Understanding the business's performance and tracking key processes and KPIs are crucial to comprehensively grasp their unique perspectives and priorities. Orchestration functions as the central control mechanism, overseeing and coordinating the execution of diverse data workflows, with popular options including Airflow, Prefect, Dagster, Mage, and Kestra. Ingestion is also a critical component, with custom pipelines often being built due to vendor limitations or specific business needs, while tools like Census and Hightouch can simplify this process. Data transformation using dbt and SQLMesh can help structure raw data into a usable format, with reverse ETL being essential for pushing data via APIs. Dashboards and their usage paradigms vary depending on the tool, with ROLAP playing a pivotal role in leveraging star schemas. Finally, data stack governance is crucial, with strategies including version control, early alert setup, streamlined workflows and CI/CD, assumption testing, goal-oriented KPI definition, and implementing lineage for faster troubleshooting.