Building a Data Warehouse: Architecture, Storage Selection, Dimensional Modeling, and ETL with Airflow
This article describes the design and implementation of a data warehouse, covering storage engine choices, dimensional modeling techniques, ETL processes using Python scripts, and workflow management with Apache Airflow to address data integration, scalability, and maintenance challenges.
The article begins by outlining the motivation for constructing a data warehouse to centralize data from MySQL, MongoDB, and Elasticsearch, using Python scripts for ETL managed by Airflow, and storing the processed data in MySQL for analytical consumption.
Storage Selection discusses criteria such as data volume, growth rate, SQL/SQL‑like capabilities, and team technology stack. After evaluating MySQL, Oracle, and Hive, the author selects MySQL with the MyISAM engine, explaining why MyISAM’s table‑level locking and lack of foreign‑key constraints suit a read‑heavy, write‑light warehouse.
Data Modeling emphasizes abstracting business requirements into a suitable data model. The author favors dimensional modeling (Kimball) over strict normalization, describing fact tables, dimension tables, star and snowflake schemas, and the process of defining themes, dimensions, grain, and measures. An example of a recruitment analytics star schema with six dimensions is provided.
ETL explains the incremental update mechanism: a temporary table records the last update time per ETL job, which is used to extract only changed data for processing. Successful runs insert a new timestamp record.
Airflow Task Flow Management details the limitations of using crontab for scheduling and how Airflow, written in Python, offers DAGs, tasks, and operators, visual monitoring, retry policies, and email notifications. The article includes screenshots of Airflow’s UI and DAG concepts.
The author concludes that the current simple warehouse meets present needs but anticipates future challenges in modeling, data utilization, and analysis efficiency as data volume and business complexity grow.
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Big Data Technology & Architecture
Wang Zhiwu, a big data expert, dedicated to sharing big data technology.
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