Batch Processing (Airflow + dbt + PostgreSQL + Python + S3) overview

A batch data processing stack: Apache Airflow orchestrates DAGs of dependent tasks on schedules or triggers, dbt transforms raw data into clean analytical models using SQL with version control and testing, PostgreSQL (or a warehouse like Snowflake/BigQuery) serves as the target data store, Python implements custom extraction and loading logic, and S3-compatible object storage stages intermediate files and raw data. Airflow schedules and monitors the full pipeline: extract from APIs or databases, load raw data to staging, run dbt transformations, and trigger downstream consumers. dbt's ref() macro builds a dependency graph of models, enabling incremental builds and automated data tests. This stack powers business intelligence pipelines, reporting systems, data warehouse loading, and regulatory data submissions. The primary tradeoff is latency: batch processing introduces inherent delay between data generation and availability, making it unsuitable for real-time use cases but excellent for correctness-critical analytical workloads.