Part 3 - Station Sampling and Cache Building 🗂️

This part continues from the data source overview and focuses on the bootstrap script that prepares the station list used by ingestion.

Why this script exists

The pipeline needs a stable list of stations before Airflow starts fetching readings. Rather than hard-coding stations manually, scripts/build_station_sample.py discovers them from OpenAQ and stores the result in a cached JSON file.

That gives the project a real-world bootstrap pattern:

• discover reference data,
• normalize it,
• cache it,
• and reuse it from the DAG.

How the script works

The script is organized into a few focused functions:

• resolve_country_id() finds the OpenAQ country id for Egypt.
• _fetch_locations() retrieves station records with retry handling.
• enrich_station_sample() adds normalized country metadata.
• load_or_fetch_station_sample() prefers the local cache when it is already valid.
• save_to_storage() writes the sample either to local disk or to S3.

That structure is easy to follow because each function has one responsibility.

The caching behavior

Caching is important here because the station set does not need to be rebuilt every time the pipeline runs. The script checks whether a local cache already exists and whether it is large enough. If the cache is too small, it is discarded and rebuilt.

This is a small but useful pattern to study:

• bootstrap data is cached,
• stale cache can be refreshed,
• and the rest of the pipeline depends on a predictable reference file.

Why this matters for the rest of the pipeline

The ingestion DAG reads the sample from the same location every time. That keeps the flow deterministic. It also means the downstream Spark job can read the same station metadata file and join readings back to the same station definitions.

In other words, this script is not just a setup helper. It is part of the data contract for the whole repository.

Continue

Next, I will explain the shared configuration module and show how one file controls local paths, environment selection, and warehouse connection settings across the project.

Continue to Part 4: Airflow Runtime and Shared Config.

Tag: #dataengineeringzoomcamp