6 PYTHON BASED ETL TOOLS TO LEARN
Regardless of size and type, companies are dealing with more and more data. This huge amount of data is too complex to understand manually. need an effective solution for processing, evaluating, and retrieving valuable data which is managed by data hidden in data. ETL, which stands for data extraction, transformation and loading, collects and processes data from multiple sources in one data warehouse, which can then be analyzed. It is a key component of data warehousing.
Pandas is one of the most popular Python libraries, offering Python data structure and analysis tools. An R-style data framework was added which makes editing, cleaning and analyzing data much easier than in raw Python. Panda can perform every step of the process so that users can pull data from most storage formats and manipulate the data in memory quickly and easily. After finishing pandas, saving data frames in a CSV, Microsoft Excel, or SQL database is that easy.
Luigi is developed by Spotify and is an open source Python package that simplifies the management of long-running batch processes. As a result, it can handle tasks outside the scope of the ETL and performs quite well with ETLs. Luigi offers dependency management with excellent visualization and failover via checkpoints. It has command line interface integration. This Python ETL based tool is conceptually similar to GNU Make, but not only for Hadoop, it also makes working with Hadoop easier. Luigi is currently used by most companies, including Stripe and Red Hat.
Bonobo is a lightweight ETK configuration framework with code like Python configurations. It is very easy to use and allows you to quickly deploy and run pipelines in parallel. By learning Bonobo, anyone can take advantage of multiple sources such as CSV, JSON, XML, XLS, SQL, etc. All transformations follow the UNIX atomic principle. This ETL tool does not require new users to learn new APIs. You are only familiar with Python.
petl, a Python ETL package that allows users to create Python tables and extract data from various sources such as CSV, Xls, HTML, txt, json and more. This ETL tool has the same functionality as Pandas, but is specifically designed to work with ETLs and does not contain built-in analysis functions. Hence, it is best for users interested only in ETL.
Apache Airflow, an open source tool for automating Python workflows, setting up and managing data pipelines. Apache Airflow has an important role in today’s digital era, where users need powerful and flexible tools to plan and monitor their workplaces. Apache Airflow is a great addition to existing consumer ETL tools as it is very useful for management and organizations. Its open source character facilitates the creation and maintenance of data pipelines.
Bubbles is another Python framework you can use to run ETL. It’s written in Python, but it’s technologically agnostic. Balloons are set up to work with data objects. Data sets are served via the ETL to maximize flexibility in the user’s ETL pipeline. It uses metadata to describe pipelines as opposed to script-based ones. This Python-based ETL tool hasn’t been actively developed since 2015, so some of its features may be out of date. devu.in.