In Part 5, we developed a dbt project that that targets Apache Iceberg where transformations are performed on Amazon Athena. Two dimension tables that keep product and user records are created as Type 2 slowly changing dimension (SCD Type 2) tables, and one transactional fact table is built to keep pizza orders. To improve query performance, the fact table is denormalized to pre-join records from the dimension tables using the array and struct data types. In this post, we discuss how to set up an ETL process on the project using Apache Airflow.

In Part 1 and Part 3, we developed data build tool (dbt) projects that target PostgreSQL and BigQuery using fictional pizza shop data. The data is modelled by SCD type 2 dimension tables and one transactional fact table. While the order records should be joined with dimension tables to get complete details for PostgreSQL, the fact table is denormalized using nested and repeated fields to improve query performance for BigQuery. Open Table Formats such as Apache Iceberg bring a new opportunity that implements data warehousing features in a data lake (i.e. data lakehouse) and Amazon Athena is probably the easiest way to perform such tasks on AWS. In this post, we create a new dbt project that targets Apache Iceberg where transformations are performed on Amazon Athena. Data modelling is similar to the BigQuery project where the dimension tables are modelled by the SCD type 2 approach and the fact table is denormalized using the array and struct data types.

We'll discuss how to implement data warehousing ETL using Iceberg for data storage/management and Spark for data processing. A Pyspark ETL app will be used for demonstration in an EMR local environment. Finally the ETL results will be queried by Athena for verification.