What are the best practices for designing a final target table in a data pipeline, especially when integrating with dashboards?

A final target table is the destination where processed data is stored for downstream consumption, such as dashboards or analytics. Best practices include: 1) Use a clear, descriptive schema with surrogate keys and natural keys to avoid ambiguity. 2) Partition the table on a high‑cardinality column (e.g., date or region) to improve query performance and enable efficient pruning. 3) Store data in a columnar format (e.g., Parquet or ORC) if the data warehouse supports it, to reduce storage and improve scan speed. 4) Implement incremental loading by tracking change‑data‑capture (CDC) or using a watermark column, so only new or updated rows are processed. 5) Enforce data quality rules at load time (e.g., NOT NULL constraints, value ranges, referential integrity). 6) Keep the table idempotent: if the same batch is re‑run, it should not duplicate rows. 7) Add audit columns (load_timestamp, source_system) for traceability. 8) Document the schema and lineage so that dashboard developers understand the source of each column. 9) If the target is a star‑schema fact table, de‑duplicate or aggregate as needed before loading. 10) Finally, monitor query performance and adjust partitioning or clustering as the data grows.