Data Solutions Architect

Dimensional Modeling

Vì sao: Lets you model slowly changing dimensions and shared conformed dimensions across several fact tables, so two teams report the same customer count.

Normalization Patterns

Vì sao: Lets you justify a denormalized serving table with measured query cost, instead of denormalizing by habit and inheriting inconsistent copies.

ETL/ELT Patterns

Vì sao: Lets you pick ELT on a cloud warehouse for a new domain and design idempotent loads, so a rerun after an outage does not duplicate rows.

Orchestration Tools

Vì sao: Lets you structure cross-team dependencies and backfills in the orchestrator so a late upstream dataset does not trigger a chain of manual reruns.

Lakehouse Architecture

Vì sao: Lets you lay out bronze, silver and gold layers on an open table format, so raw files become reliable, queryable tables with ACID guarantees.

Warehouse Design

Vì sao: Lets you size warehouses, separate workloads and tune a slow query, so a heavy month-end job stops starving the executive dashboards.

Data Lineage

Vì sao: Lets you trace a wrong dashboard number back to the source column and list which reports a schema change would break before it ships.

Data Cataloging

Vì sao: Lets you set up a catalog and business glossary so an analyst finds the certified revenue table instead of building a third unofficial copy.