Claude Agent SDK for Data Pipelines: ETL, Validation, Transform

#etl #validation #transformation #python

Originally published at claudeguide.io/claude-agent-data-pipeline

Claude Agent SDK for Data Pipelines: ETL, Validation, and Transformation Agents

The Claude Agent SDK fits data pipelines when the logic is too variable for rigid rules: schema drift, inconsistent source formats, validation that requires judgment, and transformation logic that adapts to data shape in 2026. This guide builds three pipeline agents: a schema validation agent that explains failures in plain English, an ETL orchestrator that routes records based on content, and a data quality agent that generates and runs its own checks.

When Claude Agents Make Sense in Data Pipelines

Use an agent when:

Source schema changes unpredictably — the agent interprets what changed vs what broke
Validation requires context — "is this address valid?" is different from "does this field match a regex?"
Transformation logic needs judgment — merging records with conflicting fields
You need readable failure reports — for non-engineers to act on

Don't use an agent when:

Schema is stable and transforms are deterministic — use dbt, Airflow, pandas
You need sub-second throughput — LLM calls add 0.5-2s per invocation
Cost is a concern — at scale, LLM validation per row gets expensive fast

The sweet spot: batch validation and orchestration, not row-level transformation.

Setup

import anthropic
import json
from typing import Any
from dataclasses import dataclass

client = anthropic.Anthropic()

Agent 1: Schema Validation Agent

Validates incoming data against an expected schema, returns structured failures with plain-English explanations.


python
VALIDATION_TOOLS = [
    {
        "name": "validate_field",
        "description": "Validate a single field value against its schema definition",
        "input_schema": {
            "type": "object",
            "properties": {
                "field_name": {"type": "string"},
                "value": {},
                "expected_type": {"type": "string"},
                "constraints": {
                    "type": "object",
                    "description": "e.g., {min: 0, max: 100} or {enum: ['A', 'B']} or {pattern: '...'}"
                }
            },
            "required": ["field_name", "value", "expected_type"]
        }
    },
    {
        "name": "report_validation_result",
        "description": "Report the final validation result for the record",
        "input_schema": {
            "type": "object",
            "properties": {
                "is_valid": {"type": "boolean"},
                "errors": {
                    "type": "array",
                    "items": {
                        "type": "object",
                        "properties": {
                            "field": {"type": "string"},
                            "issue": {"type": "string"},
                            "action": {"type": "string", "description": "Recommended fix"}
                        }
                    }
                },
                "warnings": {
                    "type": "array",
                    "items": {"type": "string"}
                }
            },
            "required": ["is_valid", "errors"]
        }
    }
]


def execute_validation_tool(tool_name: str, tool_input: dict, record: dict) -

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