Data Ingestion Pipeline

Extract, validate, and load data from diverse sources into target systems.

Pipeline Architecture

Sources → Extract → Validate → Transform → Stage → Load → Verify
  │          │          │          │          │       │        │
  │          │          │          │          │       │        └─ Row counts match
  │          │          │          │          │       └─ Write to target
  │          │          │          │          └─ Staging table/file
  │          │          │          └─ Normalize, enrich, deduplicate
  │          │          └─ Schema validation, business rules
  │          └─ Pull from source
  └─ APIs, files, databases, streams

Source Extraction

File-Based Sources

from pathlib import Path
import json
import csv
import yaml

class FileExtractor:
    PARSERS = {
        ".json": lambda p: json.loads(p.read_text()),
        ".yaml": lambda p: yaml.safe_load(p.read_text()),
        ".yml": lambda p: yaml.safe_load(p.read_text()),
        ".csv": lambda p: list(csv.DictReader(p.open())),
    }

    def extract(self, path: Path) -> list[dict]:
        parser = self.PARSERS.get(path.suffix)
        if not parser:
            raise ValueError(f"Unsupported format: {path.suffix}")
        data = parser(path)
        return data if isinstance(data, list) else [data]

API Extraction with Pagination

import httpx

async def extract_paginated(base_url: str, params: dict = {}) -> list[dict]:
    all_records = []
    page = 1
    async with httpx.AsyncClient() as client:
        while True:
            response = await client.get(base_url, params={**params, "page": page, "per_page": 100})
            response.raise_for_status()
            data = response.json()
            records = data.get("items", data.get("results", data))
            if not records:
                break
            all_records.extend(records)
            page += 1
    return all_records

Database Extraction

import asyncpg

async def extract_from_db(dsn: str, query: str, batch_size: int = 1000):
    conn = await asyncpg.connect(dsn)
    try:
        async for batch in conn.cursor(query, prefetch=batch_size):
            yield dict(batch)
    finally:
        await conn.close()

Validation

Schema Validation

from dataclasses import dataclass

@dataclass
class ValidationResult:
    valid: list[dict]
    invalid: list[tuple[dict, str]]  # (record, error_message)

def validate_records(records: list[dict], schema: dict) -> ValidationResult:
    result = ValidationResult(valid=[], invalid=[])
    required_fields = schema.get("required", [])

    for record in records:
        errors = []
        for field in required_fields:
            if field not in record or record[field] is None:
                errors.append(f"Missing required field: {field}")

        for field, rules in schema.get("fields", {}).items():
            if field in record and record[field] is not None:
                value = record[field]
                if "type" in rules and not isinstance(value, rules["type"]):
                    errors.append(f"{field}: expected {rules['type'].__name__}")
                if "max_length" in rules and len(str(value)) > rules["max_length"]:
                    errors.append(f"{field}: exceeds max length {rules['max_length']}")

        if errors:
            result.invalid.append((record, "; ".join(errors)))
        else:
            result.valid.append(record)

    return result

Business Rule Validation

def apply_business_rules(records: list[dict]) -> ValidationResult:
    result = ValidationResult(valid=[], invalid=[])
    for record in records:
        errors = []

        # Example: organ must be valid
        if record.get("organ") not in {"I", "II", "III", "IV", "V", "VI", "VII", "META"}:
            errors.append(f"Invalid organ: {record.get('organ')}")

        # Example: status must follow promotion state machine
        valid_statuses = {"LOCAL", "CANDIDATE", "PUBLIC_PROCESS", "GRADUATED", "ARCHIVED"}
        if record.get("status") not in valid_statuses:
            errors.append(f"Invalid status: {record.get('status')}")

        if errors:
            result.invalid.append((record, "; ".join(errors)))
        else:
            result.valid.append(record)
    return result

Deduplication

def deduplicate(records: list[dict], key_fields: list[str]) -> list[dict]:
    seen = set()
    unique = []
    for record in records:
        key = tuple(record.get(f) for f in key_fields)
        if key not in seen:
            seen.add(key)
            unique.append(record)
    return unique

Merge Strategy

from enum import Enum

class MergeStrategy(str, Enum):
    KEEP_FIRST = "keep_first"
    KEEP_LATEST = "keep_latest"
    MERGE_FIELDS = "merge_fields"

def merge_duplicates(records: list[dict], key_fields: list[str], strategy: MergeStrategy) -> list[dict]:
    groups: dict[tuple, list[dict]] = {}
    for record in records:
        key = tuple(record.get(f) for f in key_fields)
        groups.setdefault(key, []).append(record)

    merged = []
    for key, group in groups.items():
        if strategy == MergeStrategy.KEEP_FIRST:
            merged.append(group[0])
        elif strategy == MergeStrategy.KEEP_LATEST:
            merged.append(group[-1])
        elif strategy == MergeStrategy.MERGE_FIELDS:
            result = {}
            for record in group:
                for k, v in record.items():
                    if v is not None:
                        result[k] = v
            merged.append(result)
    return merged

Staging Pattern

from pathlib import Path
from datetime import datetime

class StagingArea:
    def __init__(self, base_dir: str):
        self.base = Path(base_dir)

    def stage(self, batch_id: str, records: list[dict]) -> Path:
        stage_dir = self.base / batch_id
        stage_dir.mkdir(parents=True, exist_ok=True)

        data_path = stage_dir / "data.json"
        meta_path = stage_dir / "metadata.json"

        data_path.write_text(json.dumps(records, indent=2, default=str))
        meta_path.write_text(json.dumps({
            "batch_id": batch_id,
            "record_count": len(records),
            "staged_at": datetime.now().isoformat(),
            "status": "staged",
        }))
        return stage_dir

    def promote(self, batch_id: str) -> list[dict]:
        stage_dir = self.base / batch_id
        data = json.loads((stage_dir / "data.json").read_text())
        meta = json.loads((stage_dir / "metadata.json").read_text())
        meta["status"] = "promoted"
        meta["promoted_at"] = datetime.now().isoformat()
        (stage_dir / "metadata.json").write_text(json.dumps(meta, indent=2))
        return data

Pipeline Orchestration

class IngestionPipeline:
    def __init__(self, extractor, validator, transformer, loader):
        self.extractor = extractor
        self.validator = validator
        self.transformer = transformer
        self.loader = loader

    async def run(self, source: str) -> dict:
        # Extract
        raw = await self.extractor.extract(source)

        # Validate
        validation = self.validator.validate(raw)
        if validation.invalid:
            log.warning("validation_failures", count=len(validation.invalid))

        # Transform
        transformed = self.transformer.transform(validation.valid)

        # Deduplicate
        unique = deduplicate(transformed, key_fields=["id"])

        # Load
        loaded = await self.loader.load(unique)

        return {
            "extracted": len(raw),
            "valid": len(validation.valid),
            "invalid": len(validation.invalid),
            "loaded": loaded,
        }

Anti-Patterns

• No validation gate — Always validate before loading; corrupt data is worse than missing data
• Loading directly from source — Stage first; staging enables inspection and rollback
• No deduplication — Sources often contain duplicates; handle at ingestion
• Silent data loss — Log and report every skipped/invalid record
• Monolithic pipeline — Break into composable stages for testing and reuse
• No idempotency — Pipeline re-runs should produce the same result