6 Best Python ETL Tools to Learn in 2024

Python is the most used programming language in the data integration space. And why not? It is easy to use, modern and has a large community and cutting-edge features. 

However, one of the key reasons for its popularity is the tools it has to offer. Python has many tools acting as frameworks, libraries, and applications for streamlining the ETL (Extract, Load, Transform) process. These ETL tools allow you to use Python for different tasks, including workflow management, data transformation, and moving and processing data. 

In this article, you will learn about some of the top Python ETL tools to look at for making the ETL process much easier. 

Suggested Read: Python for ETL

Best Python ETL Tools

Here is a list of the top 6 tools to use while using Python for ETL: 

1. Python ETL Tool: PyAirbyte

PyAirbyte is a Python library developed by Airbyte that enables you to build ETL pipelines effortlessly. It allows you to interact with Airbyte connectors directly within your Python environment, enabling you to effortlessly extract and load data. For transformation, you can use Python’s in-built capabilities or third-party libraries like Pandas and NumPy.

Key features of PyAirbyte are:

• Connectors: PyAirbyte reduces the need for extensive custom ETL coding by providing simple syntax for connectors to various data sources. You can easily extract data from hundreds of sources and load it into different SQL caches, including DuckDB, Snowflake, Postgres, and BigQuery.
• ‍Data Orchestration: You can quickly integrate PyAirbyte with popular data orchestration frameworks like Airflow and Dagster. This facilitates seamless workflow and management.‍
• Interoperability: PyAirbyte cached data is compatible with various Python libraries (Pandas) and AI frameworks (LangChain, LlamaIndex). This enables you to develop LLM-powered applications efficiently.‍
• Version Control: It allows you to integrate data pipelines with version control systems, enhancing collaboration and reliability. This helps you to track changes easily and ensure consistency across different environments.

Ready to experience the ease of data integration using Python with Airbyte? Install PyAirbyte using pip and explore a quick demo here.

2. Python ETL Tool: Apache Airflow 

Airflow is an open-source workflow management tool created using Python to programmatically author, schedule, and monitor workflows. Its highly customizable tool allows you to visualize your data pipelines and everything it include, like its dependencies, logs, trigger tasks, and success status. With Airflow, you can also create Directed Acyclic Graphs (DAGs). A DAG defined in a Python script is a collection of all the tasks you want to run in an organized manner within the platform. 

Overall, Airflow is the best Python ETL tool if you want to manage complex ETL jobs and keep track of everything within the pipeline. 

Key features of Airflow are:

• Airflow Operators: Airflow offers a vast library of pre-built operators. These operators are templates that can handle tasks such as data orchestration, transfer, cloud operations, and even SQL script execution. 
• ‍Scheduling: With Airflow, you can schedule your data pipeline workflows and determine their frequency and timing. It allows you to create schedules tailored to your needs using cron expressions, custom triggers, or intervals. 
• ‍Visualization: Airflow allows you to visualize your complex data pipeline workflows, which makes data more accessible to technical as well as non-technical users. 

3. Python ETL Tool: pETL

pETL (Python Extract, Transform, Load) is a Python package. It is a lightweight library designed for working with tabular data such as Excel spreadsheets, CSV files, and SQL databases. pETL provides many built-in functions for extracting, transforming, and loading data from disparate sources to destinations. These functions streamline the ETL process by streamlining most integration tasks by default without having to write custom code. 

Here are some of the ETL functions of pETL: 

Extract Functions

• fromcsv(): This function extracts data from a CSV file and returns a table.
• fromjson(): This function extracts data from a JSON file and returns a table. 
• fromxml(): It extracts data from an XML file and returns a table. 
• fromdb(): This function does the same task of returning a table but from an SQL database. 

Transform Functions

• select(): With this function, you can filter rows from a table based on the condition you provide. 
• cut(): This function selects specific columns from the table you provide. 
• aggregate(): It performs aggregation tasks such as adding, counting, and averaging in one or more tables. 
• join(): This function 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.

4. Python ETL Tool: Pandas

Pandas is one of the most popular open-source Python libraries for working with data sets. The tool provides built-in functions for analyzing, cleaning, exploring, and manipulating data. Pandas is built on top of two core Python libraries - NumPy for mathematical operations and matplotlib for data visualization. It acts as a wrapper for these libraries and allows you to access both library's methods and functions with less code. 

Pandas are well-suited for several kinds of data, including tabular data, time series, arbitrary matrix data, or any other form of statistical or observational data sets. It offers a rich set of functionalities in all these data formats, including data indexing, filtering, aggregation, grouping, merging, joining, analysis, and visualization. 

Pandas offers two data structures for tasks like transformation: Series and DataFrames. A series is like a single column of data where each value has an index. Below is an example: 

0 11

1 22

2 33

3 44

While series are very useful, analysts mostly use DataFrames. DataFrames store data in familiar table formats within rows and columns, much like relational databases. Pandas make a lot of analytical tasks more accessible by providing in-built functions to work with DataFrames. These functions include finding averages per column, the sum of values in a column, and other analytical tasks.

With Pandas, you can implement all kinds of ETL practices, including data extraction, transformation, handling, cleaning, validating, data type conversion, and exporting. 

Suggested Read: Data Extraction Tools

5. Python ETL Tool: PySpark

PySpark is a Python API for Apache Spark. Apache Spark is a distributed processing system that performs machine learning and big data tasks on large datasets. 

Using PySpark, you can write about Python and SQL-like commands to analyze and manipulate data in a distributed processing environment. It also provides a shell for interactively analyzing your data. PySpark combines Python's ease of use and adaptability with the power of Apache Spark to enable the analysis and processing of data at any size. 

Organizations use a framework like PySpark mainly because of the efficiency of processing big data. It is faster than popular libraries like Pandas and Dask. Beyond its efficiency, PySpark supports all Spark features, such as Spark DataFrames, SQL, Structured Streaming, Machine Learning (MLlib), and Spark Core. Learn more about these features from here. 

You just have to install Python and Apache Spark on your local machine to get this tool. Then, install the PySpark library just as you would any other library.

6. Python ETL Tool: Great Expectations 

Great Expectation (GX) is an open-source Python library that enhances data quality and testing. The tool provides a robust framework for validating, documenting, and profiling your data. GX accomplishes high data quality by providing an easy and intuitive way to define and manage expectations, validate data against those expectations, and alert you to violations of expectations. 

GX offers two tools: GX OSS and GX Cloud. The former is an open-source version of GX that you can install and set up within the data stack to fit your needs. However, GX Cloud is a cloud-based service that offers an easy way to use Great Expectations without having to manage infrastructure. 

Some of the features of GX include: 

• A huge library of predefined expectations allows you to easily define expectations for various data types, such as textual, numerical, and datetime data.
• Flexible and customizable expectation suites 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 for adding data validation and quality checks to existing workflows. 

Airbyte + dbt: A Perfect ETL Setup

Regarding ETL with Python, there's no tool that fits every requirement you have in one place. However, combining Airbyte and dbt can give you a robust setup to make a powerful environment. 

Airbyte is an ELT tool (not ETL) that follows a modern approach to data integration to synchronize multiple data sources and destinations. The platform automates the tasks of data ingestion using its extensive library of over 350+ pre-built connectors and intuitive user interface without writing a single line of code. 

In its extensive library, if you still haven't found your specific connector to automate synchronization, you can use its connector development kit to build a custom one.

While Airbyte streamlines the extraction and loading process, dbt (data build tool) takes care of your advanced data transformation needs. 

dbt is a data transformation tool that you can use to turn raw data into a structured format to deliver it to downstream applications. The platform offers two tools for advanced data transformation: dbt cloud and dbt core. As the name suggests, the cloud is a cloud platform, and the core is a command line tool for managing transformation workflows. In both tools, you'll get support for Python to custom code your advanced data transformation logic. 

Airbyte allows you to integrate with dbt to fulfill your data transformation needs. Using a hybrid approach and combining both tools for data integration, you can make a setup that efficiently serves all your data integration requirements. 

Suggested Read: Data Ingestion Tools

Conclusion

In this article, you have learned the six best Python tools to perform ETL. Every tool has its use case and advantages. PyAirbyte in streamlined data ingestion,  Airflow in orchestration, pETL in basic ETL, Pandas in transformation, PySpark in data processing, and GX in data quality. 

Airbyte+dbt in data integration is a powerful combination if you don’t want to go through extensive programming. You can use these tools to streamline your Python ETL process. 

If you are looking for alternatives to custom coding in Python for data pipelines, we suggest using the ETL tool Airbyte. Airbyte takes care of all your scalability, automation, and efficiency needs without much effort. Join a vibrant community of developers today, and sign up or log in with Airbyte.

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