ETL, or short for extract, transform, load, is the core of every project that requires extraction and/or migration of data. Some of the data points won’t be correctly formatted for the database of their destination. But don’t worry – the Python community has developed a large variety of tools to make ETL significantly easier and faster.
Read on to find out which are the best Phyton ETL tools that will help you collect, clean, and upload the data without much of a hassle.
The hottest open-source solution will let you plan, organize, and keep track of ETL data processes using Phyton. Its core technology features DAGs (Directed Acyclic Graphs) that let its scheduler spread the tasks across many workers without needing you to outline exact relationships between the data-flows. Airflow is very scalable and expandable and will help you move on to the next level.
Technically, Spark isn’t a Phyton tool but comes with PySpark API. It has all kinds of built-in data processing tools and can run parallel computations, allowing for even substantial data jobs to be run exceptionally quickly. It lets you write very clear and readable code so it’s a great solution if you’re looking for size and speed in your operations.
If you didn’t guess from its name, it’s an ETL Python package. You can build tables by extracting from different data sources (xls, html, csv, txt, and so on) and migrating to a database. petl is specifically designed for ETL and doesn’t have built-in features for analysis.
Panoply is a full solution that can handle each stage of the job – from CSVs to Google Analytics. It definitely makes ETL as straightforward and smooth as possible.
You’re most probably familiar with pandas if you’ve ever used Phyton to work with data. By adding R-style data-frames to Phyton, it makes using, cleaning, and analyzing the data much more simple. It handles each stage of the process so you can extract your data and manipulate it quickly and easily.
A popular ETL framework, Bubbles makes building ETL pipelines easy. It works with data objects for maximum freedom in the ETL pipeline. Keep in mind though that there hasn’t been any active development since 2015, so some of it might be outdated.
A very light ETL framework with a simple design. You can build data pipelines and connect straight to the SQL databases. There is a graph visualizer option in the library so you can easily follow your process.
Designed by Spotify, Luigi is an open-source package that makes managing long batch operations effortless. It has a web interface so you can visualize tasks and is the perfect tool if you have to deal with large data jobs.
With Odo it’s easier to migrate data between containers such as in-memory systems and remote databases. It’s significantly faster and the right tool if you frequently load large batches of data from CSV’s to SQL databases.
A light Phyton package designed to make the moving of SQL databases fast and easy. Keep in mind though that you can’t use it on Windows and has difficulty loading to MSSQL, so it might not be useful if your work process includes these.
A Python ETL solution that works with lots of data sources and targets. It might need a bit more work than the other tools on the list but it can be the right solution if you’re looking for speed.
Open Semantic ETL
This open-source framework allows the building of pipelines that crawl whole files’ directories, analyze them, and move them into your chosen database. It’s particularly useful if you have to deal with a large number of single documents.
With Mara, as with most of the other tools on this list, you can build pipelines to extract and migrate data. It combines the ETL framework with a web UI and uses PostgreSQL for data processing. The only downside – you can’t currently use it if you’re working on Windows.
riko isn’t a truly ETL solution, but you can use it for data extraction, especially if you process lots of stream data. It has a native RSS/Atom support and a Phyton library which together with its small computational footprint offers quite the advantage compared to other stream processing tools.
With Carry you can simultaneously migrate multiple tables between CSVs and databases. Its most differentiating feature is the ability to generate and keep views of the relocated data for future reference.
A Phyton library, locopy uses Snowflake and Redshift and handles ETL tasks quite easily, especially if you have to upload and download from and to S3 buckets.
A Phyton library developed to simplify an ETL pipeline and provide a graphical interface so you can design web crawlers and data cleaning tools. It’s a good solution for the extraction of large data by using a graphical interface, but keep in mind that most of its documentation is in Chinese.
With pygrametl, you can build an entire ETL stream in Phyton but it works with Jython and CPython so use it if your ETL pipeline already has Java code or JDBC driver.