Building Data Pipelines with Python: A Complete Guide

Data pipelines are the backbone of modern data infrastructure. This guide covers everything you need to know to build production-ready pipelines with Python.

What is a Data Pipeline?

A data pipeline is a series of data processing steps that move data from source systems to destination systems, transforming it along the way.

Pipeline Architecture

ETL vs ELT

Building Your First Pipeline

import pandas as pd
from sqlalchemy import create_engine

class DataPipeline:
    def __init__(self, source_conn, target_conn):
        self.source = create_engine(source_conn)
        self.target = create_engine(target_conn)

    def extract(self, query):
        return pd.read_sql(query, self.source)

    def transform(self, df):
        # Apply transformations
        df['processed_at'] = pd.Timestamp.now()
        return df

    def load(self, df, table_name):
        df.to_sql(table_name, self.target, if_exists='append')

Data Validation

Always validate your data. For comprehensive testing strategies, see our Data Quality Testing: Ensuring Trust in Your Data guide.

Schema Validation

from pydantic import BaseModel

class SalesRecord(BaseModel):
    id: int
    amount: float
    customer_id: str
    timestamp: datetime

Error Handling

Robust pipelines need proper error handling:

1. Retry logic for transient failures
2. Dead letter queues for failed records
3. Alerting for critical failures

See Monitoring Data Pipelines: Observability Best Practices for alerting best practices.

Scheduling with Apache Airflow

For orchestration, consider Apache Airflow. We cover this in our Apache Airflow: Data Pipeline Orchestration Basics tutorial.

Conclusion

Building data pipelines requires understanding both the technical and business aspects. Start simple, then add complexity as needed.

Happy data engineering!