Update smma/grant_starting.md
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@@ -292,3 +292,97 @@ Based purely on **ease of initial implementation for someone with zero experienc
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5. **Confidence Building:** Getting a working script to extract, filter, and output a clean CSV from Grants.gov will be a massive confidence booster for you. It proves your core skills translate into a valuable deliverable.
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5. **Confidence Building:** Getting a working script to extract, filter, and output a clean CSV from Grants.gov will be a massive confidence booster for you. It proves your core skills translate into a valuable deliverable.
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**Immediate next step recommendation: Focus exclusively on downloading the Grants.gov Data Extract ZIP and successfully running the DuckDB script to filter it into a CSV.** Don't worry about selling until you've done that. That success will be your first step in building confidence.
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**Immediate next step recommendation: Focus exclusively on downloading the Grants.gov Data Extract ZIP and successfully running the DuckDB script to filter it into a CSV.** Don't worry about selling until you've done that. That success will be your first step in building confidence.
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---
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**Raw Data Ingestion Layer:**
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```python
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# Base ingestion interface
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class RawDataIngester:
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def fetch_data(self, date_range=None):
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"""Download raw data from source"""
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pass
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def validate_data(self, raw_data):
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"""Check file integrity, format"""
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pass
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def store_raw(self, raw_data, metadata):
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"""Store exactly as received with metadata"""
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pass
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# Source-specific implementations
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class GrantsGovIngester(RawDataIngester):
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def fetch_data(self, date_range=None):
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# Download XML extract ZIP
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# Return file paths + metadata
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pass
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class USASpendingIngester(RawDataIngester):
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def fetch_data(self, date_range=None):
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# Download CSV files (Full/Delta)
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# Handle multiple file types
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pass
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class SAMGovIngester(RawDataIngester):
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def fetch_data(self, date_range=None):
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# API calls or file downloads
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pass
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```
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**Raw Storage Schema:**
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```sql
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-- Metadata tracking
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raw_data_batches (
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id, source, batch_type, file_path, file_size,
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download_timestamp, validation_status, processing_status
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)
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-- Actual raw data (JSONB for flexibility)
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raw_data_records (
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id, batch_id, source, record_type,
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raw_content JSONB, created_at
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)
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```
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**File Management:**
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- Store raw files in object storage (S3/MinIO)
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- Database only stores metadata + file references
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- Keep raw files for reprocessing/debugging
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**Ingestion Orchestrator:**
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```python
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class IngestionOrchestrator:
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def run_ingestion_cycle(self):
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for source in self.active_sources:
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try:
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# Fetch, validate, store
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# Track success/failure
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# Trigger downstream processing
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except Exception:
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# Alert, retry logic
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pass
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```
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**Key Features:**
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- **Idempotent**: Can re-run safely
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- **Resumable**: Track what's been processed
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- **Auditable**: Full lineage from raw → processed
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- **Flexible**: Easy to add new data sources
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**Configuration Driven:**
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```yaml
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sources:
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grants_gov:
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enabled: true
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schedule: "weekly"
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url_pattern: "https://..."
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usa_spending:
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enabled: true
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schedule: "monthly"
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```
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This layer just moves bytes around. Zero business logic. Want me to detail the validation layer next?
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