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docs: clarify pre-installed duckdb/pyiceberg and target injected packages in walkthrough

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syahril
2026-10-21 17:44:38 +07:00
parent 4223a3f1c5
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docs/panduan_integrasi_existing_airflow.md
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@@ -237,17 +237,17 @@ docker ps --filter "name=airflow" --format "table {{.Names}}\t{{.Image}}\t{{.Sta
*Catat nama kontainer scheduler dan dag processor serta port host webserver (misal: `0.0.0.0:12082->8080/tcp`).*
#### 2. Injeksi Pustaka Engine ke Kontainer Berjalan (Tanpa Restart)
Gunakan `docker exec` untuk memasang dependensi engine (`astronomer-cosmos`, `dbt-postgres`, `duckdb`, `pyiceberg`) langsung ke worker/scheduler tanpa mematikan layanan:
Gunakan `docker exec` untuk memasang dependensi engine (`astronomer-cosmos`, `dbt-postgres`, `apache-airflow-providers-git`, dan `dlt`) langsung ke worker/scheduler tanpa mematikan layanan. *(Catatan: Pustaka `duckdb` dan `pyiceberg` sudah tersedia bawaan image)*:
```bash
# Injeksi ke Scheduler
docker exec -u airflow <nama_kontainer_scheduler> pip install --no-cache-dir \
astronomer-cosmos==1.15.1 dbt-postgres==1.11.0 apache-airflow-providers-git==1.0.0 \
duckdb==1.1.3 pyiceberg[s3fs,pyarrow]==0.8.0 dlt[duckdb,filesystem]==1.6.1
dlt[duckdb,filesystem]==1.6.1
# Injeksi ke DAG Processor
docker exec -u airflow <nama_kontainer_dag_processor> pip install --no-cache-dir \
astronomer-cosmos==1.15.1 dbt-postgres==1.11.0 apache-airflow-providers-git==1.0.0 \
duckdb==1.1.3 pyiceberg[s3fs,pyarrow]==0.8.0 dlt[duckdb,filesystem]==1.6.1
dlt[duckdb,filesystem]==1.6.1
# Verifikasi instalasi berhasil
docker exec <nama_kontainer_scheduler> python -c "import cosmos, duckdb, pyiceberg; print('✅ Airflow Engine Ready!')"