Batch jobs are the backbone of many data engineering pipelines. Whether you are aggregating logs, transforming large datasets, or pulling data from APIs at scheduled intervals, batch jobs are the reliable, repeatable processes that run behind the scenes. Python, with its simplicity and rich ecosystem of libraries, is one of the most popular languages for building robust batch data pipelines.

In this comprehensive guide, we’ll explore:

• What are batch jobs?

• Use cases in data engineering

• Core components of a Python batch job

• Scheduling tools (cron, Airflow, AWS Step Functions)

• Handling errors and retries

• Logging and monitoring

• Real-world example with code

• Best practices

Let’s get started.

What Are Batch Jobs?

A batch job is a scheduled program that runs without user interaction. These jobs are usually triggered at fixed intervals (daily, hourly, weekly) or based on conditions (like a new file in a directory).

Examples:

• Pulling data from an API every day at midnight

• Cleaning and transforming data from a CSV file

• Loading data into a database

• Generating daily reports

Batch Jobs in Data Engineering

As a data engineer, batch jobs are your daily tools. Typical batch job tasks include:

• ETL (Extract, Transform, Load): Pulling data from a source, transforming it, and loading it into a data warehouse.

• Data cleaning: Removing duplicates, handling null values, formatting fields.

• File processing: Reading and transforming large CSV, JSON, or Parquet files.

• Scheduled analytics: Running predefined queries or reports every morning.

• Backfilling data: Reprocessing historical data.

Core Components of a Python Batch Job

A good batch job typically includes:

• Scheduler/trigger – Defines when the job runs

• Job script – The Python script that executes the logic

• Error handling – Try/except blocks and retries

• Logging – Track execution and failures

• Notification – Email or Slack alerts

• Monitoring – Track job status over time

Scheduling Tools

1. Cron (Linux/macOS)

Most simple batch jobs are scheduled using cron:

0 2 * * * /usr/bin/python3 /home/user/scripts/daily_etl.py

Runs the job every day at 2 AM.

2. Apache Airflow

A production-grade workflow orchestrator. Write workflows as DAGs (Directed Acyclic Graphs).

from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime

def process_data():
    # Your batch logic here
    pass

dag = DAG('daily_job', start_date=datetime(2023, 1, 1), schedule_interval='@daily')
run_task = PythonOperator(task_id='run_batch', python_callable=process_data, dag=dag)

3. AWS Step Functions / EventBridge / Lambda

Cloud-native solution to schedule and chain batch processes.

• Use Lambda to run small batch tasks

• Use Step Functions to orchestrate multiple steps

• Use EventBridge to schedule Lambda jobs

Writing a Batch Script in Python

import logging
from datetime import datetime
import pandas as pd
import requests

logging.basicConfig(filename='batch_job.log', level=logging.INFO)

URL = 'https://api.example.com/data'
FILENAME = f"data_{datetime.today().strftime('%Y%m%d')}.csv"

try:
    logging.info("Starting batch job")
    res = requests.get(URL)
    res.raise_for_status()
    data = res.json()
    df = pd.json_normalize(data)
    df.to_csv(FILENAME, index=False)
    logging.info(f"Data saved to {FILENAME}")
except Exception as e:
    logging.error("Batch job failed", exc_info=True)

Packaging and Dependencies

Use requirements.txt to keep your dependencies organized:

pandas
requests
boto3

Run your job inside a virtual environment:

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Handling Errors and Retries

Wrap key blocks in try/except and optionally retry:

import time

retries = 3
for i in range(retries):
    try:
        response = requests.get(URL)
        response.raise_for_status()
        break
    except requests.RequestException as e:
        if i < retries - 1:
            time.sleep(5)
        else:
            raise e

Logging and Monitoring

Always log:

• Start and end time

• Any warnings or errors

• Rows processed or files written

Use tools like:

• CloudWatch Logs (AWS)

• Datadog or Prometheus

• ELK Stack (Elasticsearch + Logstash + Kibana)

Notifications

Alert on failure or success:

• Email – Use SMTP

• Slack – Use Slack Webhooks

• AWS SNS – For cloud-native notifications

Best Practices

• Use parameterized configs (YAML or JSON)

• Keep your code modular and testable

• Avoid hardcoded file paths or credentials

• Retry and fail gracefully

• Monitor job history and failures

• Store logs and outputs in S3 or a shared directory

• Use version control (Git) for scripts

Real-World Example: Batch Job for Weather Data

import requests
import pandas as pd
import time
from datetime import datetime

API_KEY = 'your_openweather_api_key'
CITY = 'New York'
URL = f"http://api.openweathermap.org/data/2.5/weather?q={CITY}&appid={API_KEY}"

try:
    print("Starting weather job")
    res = requests.get(URL)
    weather = res.json()
    df = pd.json_normalize(weather)
    df['timestamp'] = datetime.now()
    df.to_csv(f"weather_{CITY}.csv", index=False)
    print("Data saved.")
except Exception as e:
    print("Error fetching weather:", e)

Conclusion

Python makes it easy to build reliable batch jobs for your data engineering pipelines. Whether you’re working with cron, Airflow, or AWS services, the key is writing clean, modular code with solid error handling and monitoring. With this guide, you now have a strong foundation to build and scale your own batch processing workflows.