Open in app Search Write Five Common Python Libraries for ETL Processing Gen. David L. · Follow 5 min read · Dec 10, 2024 130 3 ETL is a critical concept in data warehousing technology, representing three main processing steps: Extract, Transform, and Load. ETL is commonly used for data integration, merging data from various sources into a unified view for analysis and reporting. ETL Detailed Process 1. Extract (E): This step involves extracting data from various sources, which could include relational databases, file systems, cloud storage, or APIs. The extraction process may involve copying or moving data and recognizing and parsing its format. 2. Transform (T): Once the data is extracted, it usually needs to be transformed into a format suitable for analysis. This process may include: Data cleansing (e.g., removing duplicates, correcting errors, and resolving inconsistencies). Data aggregation (e.g., calculating sums, averages, maximum/minimum values). Data normalization (e.g., converting data into a unified format or unit). Data enrichment (e.g., adding additional contextual information). The transformation step is the most critical part of ETL as it directly affects the quality and usability of the data in the final data warehouse. 3. Load (L): The loading step involves writing the transformed data into the target system, which could be a data warehouse, data lake, data mart, or another form of data storage. During this step, the data may be indexed, partitioned, or archived to optimize query performance and storage efficiency. ETL processes can be either batch-based or real-time. Batch ETL typically runs at scheduled intervals, such as daily, weekly, or monthly, whereas real- time ETL requires the system to continuously process and update data. Common Python ETL Libraries 1. Pandas Advantages: Pandas provides data structures and analytical tools that are highly suitable for data cleaning and transformation. It simplifies data manipulation tasks in the ETL process, such as adding new columns and filtering data. Disadvantages: Pandas may encounter performance bottlenecks when handling large datasets, as it primarily operates on in-memory data. Use Cases: Suitable for small to medium-sized datasets and for rapid prototyping. Code Example: import pandas as pd

Read data from a CSV file

df = pd.read_csv('my_example.csv')

Data transformation, such as adding a new column

df['new_A_column'] = df['existing_A_column'].apply(lambda x: x * 5.4)

Save the transformed data to a new CSV file

df.to_csv('transformed_example.csv', index=False) 2. Apache Spark (PySpark) Advantages: Apache Spark is a unified analytics engine designed for large- scale data processing. The PySpark API makes it simple to handle Spark jobs within Python workflows. Disadvantages: Compared to Pandas, Spark has a steeper learning curve and requires more resources for setup and operation. Use Cases: Ideal for processing large datasets, especially in distributed computing environments. Code Example: from pyspark.sql import SparkSession

Initialize a Spark session

spark = SparkSession.builder.appName('etl_example').getOrCreate()

Read data from a CSV file

df = spark.read.csv('input_example_data.csv', header=True, inferSchema=True)

Data transformation

df = df.withColumn('new_A_column', df['existing_A_column'] * 5.4)

Write the data to a new CSV file

df.write.csv('output_example_data.csv') 3. Haul Advantages: Haul is a lightweight and modular Python library designed specifically for ETL tasks. It focuses on simplifying data extraction, transformation, and loading by providing clean, reusable components that integrate seamlessly into modern workflows. Disadvantages: Haul is relatively new and less feature-rich compared to more established ETL frameworks like Airflow or Luigi. It may not be suitable for highly complex ETL pipelines or large-scale distributed systems. Use Cases: Ideal for small to medium-sized ETL workflows, rapid prototyping, and scenarios requiring flexibility and simplicity. Code Example: from haul import Extractor, Transformer, Loader

Define an extractor to load data from a CSV file

extractor = Extractor.from_csv('input_example_data.csv')

Define a transformer to add a new column

class MultiplyTransformer(Transformer): def transform(self, row): row['new_A_column'] = row['existing_A_column'] * 5.4 return row transformer = MultiplyTransformer()

Define a loader to save the data to a new CSV file

loader = Loader.to_csv('output_example_data.csv')

Execute the ETL process

etl_pipeline = extractor | transformer | loader etl_pipeline.run() 4. Luigi Advantages: Luigi is a Python library designed for building complex batch job pipelines. It handles dependency resolution, workflow management, visualization, and failure handling. Disadvantages: Luigi does not automatically synchronize tasks to worker nodes and lacks built-in scheduling, alerting, or monitoring features. Use Cases: Suitable for automating simple ETL workflows, such as log processing. Code Example: from luigi import Task, ExternalTask, Parameter, LocalTarget
import pandas as pd

Define a task to extract data from a source URL

class ExtractData(Task): source_url = Parameter() # Define a parameter for the source URL of the dat def run(self): # Read data from the source URL into a pandas DataFrame df = pd.read_csv(self.source_url) # Write the DataFrame to a CSV file at the specified output location self.output().open('w').write(df.to_csv(index=False)) def output(self): # Define the output target for this task (a local CSV file) return LocalTarget('output_data_example.csv')

Define a task to transform the data

class TransformData(ExternalTask):
source_url = Parameter() # Define a parameter for the source URL of the inp def run(self): # Read data from the source URL into a pandas DataFrame df = pd.read_csv(self.source_url) # Perform a hypothetical transformation: adding a new column df['new_A_column'] = df['existing_A_column'] * 5.4 # Write the transformed DataFrame to a CSV file at the specified output self.output().open('w').write(df.to_csv(index=False)) def output(self): # Define the output target for this task (a transformed local CSV file) return LocalTarget('transformed_example_data.csv')

Main entry point for Luigi tasks

if name == 'main': # Run the TransformData task, which depends on 'example_data.csv' as input luigi.build([TransformData('example_data.csv')]) 5. Airflow Advantages: Apache Airflow is a powerful workflow orchestration tool, allowing users to programmatically author, schedule, and monitor workflows. Its flexibility and modular architecture make it ideal for creating complex ETL pipelines that can handle dependencies and integrate with various data sources. Disadvantages: Airflow has a steep learning curve, especially for users unfamiliar with its DAG (Directed Acyclic Graph) paradigm. Additionally, it requires careful setup and maintenance, as scaling can introduce performance and resource challenges. Use Cases: Best suited for orchestrating ETL workflows in environments with multiple steps or dependencies. It is particularly effective for managing workflows in distributed or cloud-based systems. Code Example: from airflow import DAG from airflow.operators.python import PythonOperator from datetime import datetime

Define the ETL functions

def extract(): print("Extracting data...") print("more code here to do extract job") def transform(): print("Transforming data...") print("more code here to do transform job") def load(): print("Loading data...") print("more code here to do load job")

Initialize the DAG

default_args = { 'owner': 'airflow', 'start_date': datetime(2024, 1, 1), 'retries': 1, } dag = DAG( 'example_etl', default_args=default_args, schedule_interval='@daily', )

Define the tasks

extract_task = PythonOperator(task_id='extract', python_callable=extract, dag=da transform_task = PythonOperator(task_id='transform', python_callable=transform, load_task = PythonOperator(task_id='load', python_callable=load, dag=dag)

Set task dependencies

extract_task >> transform_task >> load_task Summary These libraries and tools each have their strengths, and the choice of which to use depends on the specific project requirements, data scale, and the familiarity of the development team. The ETL process forms the foundation of data management and analysis, ensuring data consistency, accuracy, and availability. This enables data analysis and business intelligence (BI) tools to deliver valuable insights and support decision-making. With technological advancements, the ETL process continues to evolve, giving rise to variations like ELT (Extract, Load, Transform), where data is first loaded into the target system before transformation. This approach can improve efficiency when dealing with large-scale datasets. Thanks for your reading. Python Etl Etl Tool Etl Pipeline Python Data Preprocessing Written by Gen. David L. Follow 453 Followers · 4 Following AI practitioner & python coder to record what I learned in python project development Responses (3) What are your thoughts? Respond david libert (“dadoo”) 29 days ago Hello thank you. You forgot Mage.ai, very cool and strong solution 😉 4 Reply Dinu Gherman 27 days ago Missing prefect.io. 2 Reply R. Ganesh 16 days ago these frameworks not libraries Reply More from Gen. David L. Gen. David L. Gen. David L. Advanced Pandas Features: ETL-PIPES: An Efficient Python ETL Enhance Your Data Processing… Data Processing Library The essence of data analysis lies in ETL-pipes is a powerful and flexible Python uncovering the stories behind the data. In thi… library specifically designed for ETL (Extract… Dec 5, 2024 105 Nov 30, 2024 45 6 Gen. David L. Gen. David L. How to Map Column Values in a Must-Know Python Pandas Pandas DataFrame? Functions for Effortless Data… Mapping column values refers to replacing The key point of data analysis lies in specific values in a column with other values,… uncovering the stories behind the data. To… Dec 24, 2024 81 Nov 27, 2024 19 See all from Gen. David L. Recommended from Medium Harold Finch Vijay Gadhave Top 25 Python Scripts To Automate Must-Have Data Engineering Your Daily Tasks Certifications Python is an excellent tool for automating Note: If you’re not a medium member, CLICK daily tasks, thanks to its simplicity and a wid… HERE Nov 19, 2024 337 5 Dec 3, 2024 300 5 Lists Staff picks Stories to Help You Level-Up at Work 798 stories · 1568 saves 19 stories · 916 saves Self-Improvement 101 Productivity 101 20 stories · 3214 saves 20 stories · 2716 saves In Wren AI by Howard Chi Varun Singh How Uber is Saving 140,000 Hours Python 3.14 Released — Top 5 Each Month Using Text-to-SQL — … Features You Must Know Discover how Uber’s Text-to-SQL technology Faster Annotations & Mind-Blowing Updates streamlines data queries and learn how to… You NEED to Know! Jan 2 405 7 Dec 31, 2024 613 4 Sai Parvathaneni Esra Soylu Data Quality Checks (DQCs): A Advanced SQL Techniques Guide for Data Engineers In this article, we will focus on advanced SQL As a data engineer in financial services, I’ve techniques. I will try to explain the methods… learned one critical lesson: the quality of you… Dec 8, 2024 62 2 Nov 23, 2024 263 3 See more recommendations