{
  "id": "stack-profile:batch-processing",
  "_kind": "StackProfile",
  "_file": "domain/stack-profiles/deep-stacks-2.yaml",
  "_cluster": "domain",
  "attributes": {
    "displayName": "Batch Processing (Airflow + dbt + PostgreSQL + Python + S3)",
    "description": "A batch data processing stack: Apache Airflow orchestrates DAGs of\ndependent tasks on schedules or triggers, dbt transforms raw data\ninto clean analytical models using SQL with version control and\ntesting, PostgreSQL (or a warehouse like Snowflake/BigQuery) serves\nas the target data store, Python implements custom extraction and\nloading logic, and S3-compatible object storage stages intermediate\nfiles and raw data.\n\nAirflow schedules and monitors the full pipeline: extract from APIs\nor databases, load raw data to staging, run dbt transformations, and\ntrigger downstream consumers. dbt's ref() macro builds a dependency\ngraph of models, enabling incremental builds and automated data tests.\nThis stack powers business intelligence pipelines, reporting systems,\ndata warehouse loading, and regulatory data submissions. The primary\ntradeoff is latency: batch processing introduces inherent delay\nbetween data generation and availability, making it unsuitable for\nreal-time use cases but excellent for correctness-critical analytical\nworkloads.\n",
    "composes": [
      "tool:airflow",
      "language:python",
      "language:sql",
      "library:sqlalchemy",
      "library:pandas",
      "library:boto3",
      "library:pydantic",
      "tool:docker"
    ]
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    {
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      "kind": "used_by_role"
    },
    {
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      "kind": "follows_workflow"
    },
    {
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