ETL automation with Python: Benefits and tools

Extract, transform, load (ETL) processes are a significant part of data warehousing and data analytics. Automating these critical data-driven processes can impact how you leverage your data’s value, both internally and externally.

Python, a versatile and powerful programming language, complements ETL automation with numerous tools and libraries.

In this article, we explore why you might choose Python for building ETL automation and look at the pros and cons of popular ETL tools and a full stack workload automation solution.

What is ETL automation?

ETL automation is the process of automating the extraction, transformation and loading of raw data from multiple data sources into a data warehouse or other storage system. Using software tools and scripts, you can streamline these processes and reduce errors by eliminating manual intervention.

Before automation became widely available, ETL processes were performed manually and, therefore, quite time-consuming. Now, organizations of all sizes can leverage ETL tools and frameworks to automate repetitive tasks and manage complex datasets. These tools not only save time and improve resource allocation but also enhance data quality, consistency and integrity. 

What are ETL pipelines?

ETL pipelines are workflows that define steps and dependencies involved in ETL processes. These pipelines specify the order in which data is extracted, transformed and loaded to enable a seamless flow of information. ETL pipelines often involve directed acyclic graphs (DAGs) to represent dependencies between tasks.

Each task in the data pipeline performs a specific ETL operation. This could include data extraction from one or more data sources, data aggregation, transformations or loading the transformed data into a target system (data warehouse, data lake or similar).

By organizing tasks into an ETL pipeline, data engineers can automate the entire process while maintaining data consistency and integrity.

What is Python for ETL?

Python is a programming language widely adopted for building ETL workflows due to its flexibility, simplicity and extensive library ecosystem. It allows data engineers and analysts to create custom ETL processes tailored to specific business needs, ensuring that data is processed efficiently and accurately.

Python offers a range of built-in and third-party libraries like Pandas, NumPy and PySpark, which are specifically designed for handling and transforming data. These tools enable users to extract data from various sources, apply complex transformations and load it into databases or data warehouses seamlessly. Additionally, Python’s robust integration capabilities allow it to interact with APIs, cloud platforms and other systems, making it a versatile choice for modern ETL workflows.

By leveraging Python, organizations can build scalable, reusable ETL solutions that align with their evolving data management requirements.

Why is Python used for ETL?

Python is favored for ETL because of its powerful data manipulation capabilities, extensive library support and community-driven ecosystem. Combined with the right automation tools, Python enables sophisticated scheduling and orchestration of ETL pipelines, ensuring timely data delivery.

Python is also open-source, making it a budget-friendly option for organizations seeking to implement powerful ETL solutions without high licensing fees.

Benefits of Python for ETL automation

When it comes to ETL automation specifically, Python offers several advantages:

• Clean and intuitive syntax: Python syntax is easy to learn and read, making it accessible for beginners and well-liked by experienced programmers. The syntax allows developers to write concise and readable code in less time and maintain it with ease.
• Data integration capabilities: Python integrates easily with multiple data sources, data streams and formats, including CSV files, JSON, XML, SQL databases and more. Python also offers connectors and APIs to interact with popular big data tools like Hadoop and data storage systems like PostgreSQL and Microsoft SQL Server.
• Ecosystem of Python libraries: Python has a vast collection of open-source libraries for data manipulation. These include Pandas, NumPy and petl. Python libraries provide powerful tools for analysis and transformation.
• Scalability and performance: Python’s scalability is enhanced by libraries like PySpark that enable distributed data processing for big data analytics. Python also supports parallel processing for more efficient resource utilization.

Other programming languages that can be used for ETL processes include Java, SQL, HTML and Scala. 

Best Python ETL tools: Pros and cons

There are a number of Python ETL tools and frameworks available to simplify and automate ETL processes. Here, we’ll cover the pros and cons of the most popular tools. 

Apache Airflow

Apache Airflow is a Python-based workflow orchestration tool designed to manage and automate complex ETL pipelines through the use of DAGs.

Key features

• Airflow operators: Templates that can handle tasks such as data orchestration, transfer, cloud operations and even SQL script execution
• ‍Scheduling for data pipeline workflows tailored to your needs using cron expressions, custom triggers or intervals
• ‍Visualization for complex data pipeline workflows to make data more accessible to technical and non-technical users

Pros

• Open-source tool
• Support for plugins and custom operators
• Works well with major cloud platforms, APIs and databases

Cons

• Can be overly complex for small projects
• May be resource-intensive

Great Expectations

Great Expectations is a Python-based data validation framework that ensures data quality by enabling automated testing and profiling within ETL pipelines.

Key features

• A huge library of predefined expectations for various data types, such as textual, numerical and date/time data
• Customizable expectation suites that allow you to create your own expectations for applying with specific data sets
• Support for various data sources and formats like Databricks and relational databases
• The ability to integrate with an existing data pipeline to add data validation and quality checks to existing workflows

Pros

• Clear, human-readable documentation
• Specializes in validating and profiling data
• Built-in integrations for popular ETL tools

Cons

• Not a full ETL solution
• Works best for batch validations instead of real-time data checks

Pandas

Pandas is a popular Python library for data manipulation and data analysis. It provides data structures like DataFrames, which are highly efficient for handling structured data.

Key features

• Built-in functions for analyzing, cleaning, exploring and manipulating data
• Suited for different data types: tabular data, time series, arbitrary matrix data or any other form of statistical or observational data sets
• Easy implementation of all kinds of ETL practices, including data extraction, transformation, handling, cleaning, validating, data type conversion and exporting.

Pros

• Rich functionality
• Extensive documentation
• Wide adoption within the data science community

Cons

• Not ideal for processing extremely large datasets because of in-memory nature
• May require tools like PySpark for big data processing

pETL

pETL is a lightweight Python library for ETL tasks and automation. It provides simple and intuitive functions for working with tabular data for fast data manipulations. 

Key features

• Extract functions
  • fromcsv(): Extracts data from a CSV file and returns a table
  • fromjson(): Extracts data from a JSON file and returns a table
  • fromxml(): Extracts data from an XML file and returns a table
  • fromdb(): Does the same task of returning a table but from an SQL database
• Transform functions
  • select(): Filters rows from a table based on the condition you provide
  • cut(): Selects specific columns from the table you provide
  • aggregate(): Performs aggregation tasks such as adding, counting and averaging in one or more tables
  • join(): Combines two or more tables based on common keys
• Load functions
  • tocsv(): Loads the provided table in a CSV file
  • tojson(): Loads the provided table in JSON file
  • toxml(): Loads the provided table in an XML file
  • todb(): Loads the provided table in the SQL database

Pros

• Ease of use
• Memory efficiency
• Compatibility with various sources

Cons

• Lacks advanced features compared to other ETL tools
• May not be sufficient for complex ETL workflows

PySpark 

PySpark is a Python library for Apache Spark, a distributed computing framework for big data processing. It provides a high-level API for scalable and efficient data processing.

Key features

• The flexibility to create custom ETL pipelines, unlike many GUI-based ETL tools
• Advanced scalability due to its distributed computing framework that allows it to scale to handle large datasets
• Easy code automation using tools such as Apache Airflow or Prefect
• Optimal performance due to the ability to take advantage of multiple cores and processors

Pros

• Versatile interface that supports Apache Spark’s features, including machine learning and Spark Core
• Fault tolerance
• Easy integration with other Spark components

Cons

• Harder to learn for beginners compared to other Python ETL tools
• Requires a distributed cluster environment to leverage all capabilities

The workload automation approach to ETL 

Instead of limiting your data team to a Python-specific solution or ETL testing tool, consider how much greater efficiency you can achieve with a platform built to develop your true automation fabric.

RunMyJobs by Redwood is a workload automation solution that can effectively manage and schedule ETL jobs, but it’s also designed to orchestrate complex workflows, monitor job executions and handle dependencies between tasks for any type of process. While not a Python-specific tool, Redwood can seamlessly integrate with Python scripts and other ETL tools — it’s an end-to-end automation solution.

Teams can easily automate repetitive tasks with Redwood’s no-code connectors, sequences and calendars and execute workflows in real time based on source files, events, messages from apps and more. Plus, you can engage in custom workflow management using consumable automation services and native SOA APIs and formats.

RunMyJobs expands as your DevOps activities evolve to support new business requirements. By coordinating resource management in hybrid environments, your team can use it to automate common ETL and testing, data warehousing and database tasks. Access real-time dashboards to manage big data, business intelligence tools and more, all through an interactive, drag-and-drop interface.

Integration with a variety of web services and microservices allows your team to use the tools and technologies they prefer. RunMyJobs makes it easy to automate tasks between services, including Apache Airflow, Google TensorFlow, GitHub, Microsoft Office 365, ServiceNow, DropBox and more.

Developers can choose from more than 25 supported scripting languages, including Python code and PowerShell, and can work from a command-line user interface with built-in parameter replacement and syntax highlighting.

Your team will have the resources they need for quick adoption in Redwood University, which offers tutorials for countless use cases and ETL jobs. Demo RunMyJobs to explore how to enhance your Python-driven ETL processes.