Top 10 Python-Based ETL Tools to Learn in 2023

The top Python-based ETL tools in 2023 are hundreds in number including frameworks, libraries and software

Python has dominated the ETL space for several years. There are easily over a hundred Python-based ETL Tools that serve as ETL Frameworks, Libraries, or Software. ETL is a critical component of Data Stack processes. It enables data transfer between systems. A good ETL tool defines the workflows for Data Warehouse on its own.

Organizations use extract, transform, and load (ETL) tools to transfer, format, and store data between systems in order to obtain high-performance data. Python ETL frameworks aid in the automation of the ETL development process and serve as the foundation for creating Python-written ETL software. They allow businesses to customize and control their pipelines, as well as improve their data source. Users can use Python to define, schedule, and execute data pipelines when using a top ETL framework. With the help of ETL tools, organizations use techniques such as data normalization, integration, and aggregation. And here are the top 10 python-based ETL tools in 2023:

1. Apache Airflow

Apache Airflow is an open-source, Python-based workflow automation tool for building and managing large data pipelines. It enables to collect data from various sources, transform it into useful information, and load it into destinations such as data lakes or data warehouses. This service handles all of the necessary steps prior to using prepared, clean data for business needs.

2. Bonobo

Bonobo is a lightweight, open-source Python-based ETL framework pipeline tool that aids in data extraction and deployment. Bonobo is concerned with semi-structured data schemas. It is distinct in that it uses Docker containers to execute ETL jobs. Its unique selling point, however, is the parallel data-source processing and SQLAlchemy extension

3. Hadoop

Apache Hadoop is an ETL framework that supports and processes large datasets by distributing computational load across multiple computer clusters. The Hadoop library is intended to detect and handle application and hardware layer defects. When the computing power of multiple machines is combined, Apache Hadoop provides high performance and resource availability.

4. Luigi

Luigi is an open-source Python ETL tool that can be used to build more complex pipelines. It provides benefits such as failure recovery via checkpoints, CLI, and visualisation tools. It allows users to express their dependencies in a variety of ways. When a task is completed, they can use the newly created target for another task. Luigi is the ideal solution for businesses looking to overcome ETL tasks such as data logging.

5. mETL

mETL is a Python ETL tool designed for loading CEU elective data. It is a web-based ETL tool that allows developers to create custom components that can be run and integrated in accordance with an organization's Data Integration requirements. It can load any type of data and supports a wide range of file formats, as well as data migration and data migration packages.

6. Odo

Odo is a Python tool that converts data from one format to another while delivering high performance when loading large datasets into different datasets. It includes memory structures such as NumPy arrays, data frames, lists, and so on. It also accepts data from sources other than Python, such as CSV/JSON/HDF5 files, SQL databases, data from remote machines, and the Hadoop File System.

7. Pandas

Pandas is a batch-processing ETL library that includes Python-based data analysis and structure tools. It has the potential to speed up the processing of semi-structured or unstructured data. Pandas works with small, structured datasets that were previously unstructured or semi-structured.

8. Riko

Riko is a Python-based stream processing engine that analyses and processes structured data streams. Riko is ideal for dealing with RSS feeds because it supports parallel execution via its synchronous and asynchronous APIs. It is modelled after Yahoo pipes and has replaced it. It can assist many companies in developing Business Intelligence Applications that interact with customer databases on demand when connected to Data Warehouses.

9. Petl

Python ETL, also known as pETL, is useful for processing, extracting, or loading data tables from sources such as CSV or XML. It is a programming language that can be used for a variety of purposes. The ETL functionality allows for the flexible application of transformations such as joining, aggregating, and sorting data in tables.

10. Skyvia

Skyvia is a data platform in the cloud that aids in code-free backup, data integration, access, and management. It comes with and supports an ETL solution for a variety of data integration scenarios such as CSV files, cloud data warehouses, cloud applications, and databases. It also includes a data backup tool for the cloud, an OData server, and an online SQL client.