AI Data Pipeline Patterns: Intelligent ETL and Processing

Integrating AI into data pipelines enables intelligent processing, classification, and enrichment. Let’s explore the key patterns.

AI-Enhanced Pipeline Architecture

from dataclasses import dataclass
from typing import Callable, Any

@dataclass
class PipelineStage:
    name: str
    processor: Callable
    ai_enabled: bool = False

class AIPipeline:
    def __init__(self, ai_client):
        self.ai_client = ai_client
        self.stages = []

    def add_stage(self, stage: PipelineStage):
        self.stages.append(stage)

    async def run(self, data):
        current = data
        for stage in self.stages:
            if stage.ai_enabled:
                current = await stage.processor(current, self.ai_client)
            else:
                current = stage.processor(current)
        return current

# Example: Classification pipeline
pipeline = AIPipeline(ai_client)

pipeline.add_stage(PipelineStage("extract", extract_data))
pipeline.add_stage(PipelineStage("classify", ai_classify, ai_enabled=True))
pipeline.add_stage(PipelineStage("enrich", ai_enrich, ai_enabled=True))
pipeline.add_stage(PipelineStage("load", load_to_warehouse))

result = await pipeline.run(raw_data)

AI-powered pipelines transform raw data into intelligent, enriched datasets. Design for both batch and streaming scenarios.