A Survey of 21 ETL Tools for Python

Here are summaries of each of the tools you’ve mentioned along with examples of how to implement the ETL (Extract, Transform, Load) process using each tool within a Python workflow:

  1. Apache Spark: Apache Spark is a powerful open-source cluster-computing framework that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. It’s commonly used for processing large-scale data and running complex ETL pipelines. Example Implementation:
from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("ETLExample") \

# Load data from source
source_data = spark.read.csv("source_data.csv", header=True, inferSchema=True)

# Apply transformations
transformed_data = source_data.select("column1", "column2").filter(source_data["column3"] > 10)

# Write data to destination

  1. Apache Airflow: Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. It allows you to define complex ETL workflows as directed acyclic graphs (DAGs) and manage their execution. Example Implementation: Define a DAG in a Python script:
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime

def etl_process():
    # Your ETL logic here

default_args = {
    'start_date': datetime(2023, 8, 1),
    'schedule_interval': '0 0 * * *',  # Run daily at midnight

dag = DAG('etl_workflow', default_args=default_args)

etl_task = PythonOperator(
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