How to automate ETL testing using Python – a tool-based guide
ETL is an integral part of different data warehouse requirements. It is recognized to be the spring that helps activate the transfer of data between outstanding ETL tools and systems capable of handling different data warehouse workflows single-handedly. A wide array of ETL tools is available in the market, which accomplishes different needs. Python is a popular Test automation software which is dominating the ETL space constantly.
A wide assortment of python tools is available in the market, serving as the libraries, frameworks, and software for the ETL. There is a variety of reasons why Python is used for the automation of ETL testing. Automation of ETL testing becomes extremely comfortable with the aid of Python. It is possible to meet the hyper-specific and unique needs through Python. In this write-up, you can learn about different Python tools which are useful in the automation of ETL testing:
It is an open-source python tool that provides the opportunity to develop different complicated pipelines. It plays an integral role in the automation of excessive workloads, such as terabytes of data every day. At present, major companies such as Red Hat and Stripe are using this python-based tool. Also, there are different advantages to using Luigi.
It provides a dependency management solution along with stellar visualization. It offers failure recovery through various checkpoints. Besides this, it possesses command-line interface integration. If you are looking for a suitable choice for the automation of different ETL processes, Luigi offers a helping hand in handling them faster without any setup.
It is an open-source workflow tool based on Python, which helps create and maintain different types of data pipelines. It offers a helping hand in the management, organization and structuring of different ETL pipelines with the aid of DAGs. DAGs or Directed Acyclic Graphics play an integral role in forming different dependencies without defining different tasks.
It offers sufficient space to execute each branch several times. Whether ETL has several steps or you have been performing ETL jobs for a prolonged period, Apache Airflow provides the opportunity to restart from a specific point during the execution of different ETL processes.
The metadata database will be storing the workflows/tasks. Also, the scheduler will make the right use of different DAG definitions for choosing the tasks. Here, the executor will be determining the worker who will execute the task. Workers are recognized to be the processes that will perform the workflow logic.
Apache Airflow is the most suitable addition to the already-present ETL toolbox. It is known to be the best choice for the organization and management. Since this python tool is not any library, it is the best choice to perform different small ETL jobs.
If you are looking for an ideal solution to automate ETL testing, Petl is the one. It allows you to develop the tables in the Python. Besides this, it provides the suitable choice for data extraction from a plethora of resources. It allows you to handle different complicated datasets. It helps in making the right use of the system memory.
Bubbles is a Python framework which is beneficial for the execution of ETL. This python-based tool makes the right use of the metadata for the description of the pipelines. Since it is known to be agnostic technologically, you do not need to worry about data access. As you require a faster ETL setup, agnostic technology, it provides the freedom to solely emphasize different ETL processes.
It is a python tool that is equipped with the library. It offers different types of analysis and data structure tools. Besides this, it helps add different R-style data frames that play an integral role in making different ETL processes easy. Speaking of ETL, it is possible to achieve anything with the Panda python working tool.
The best thing about this tool is that it can be run without any challenges. All you need to do is execute the simple script for the data loading from the Postgre table, after which the data is transformed and cleaned. Next to this, the data is written to the Postgre table. Speaking of scalability and in-memory, this python ETL tool does not showcase poor performance. It is possible to scale different pandas with the aid of parallel chunks.
It is a lightweight framework with the aid of the native Python. It comes with extensive features, such as iterators and functions for achieving different ETL tasks. They are known to be related in DAGs together, after which it gets executed parallelly. This Python-based tool is meant to write atomic, simple and diverse transformations which are easy to monitor and test.
It is another Python-based tool that is effective for automation testing companies. It offers ETL functionality in the code. It can be integrated into different Python applications easily. Besides this, it is inclusive of different integrations with different CPython and Jython libraries. It also provides a suitable choice to programmers to work with different tools. In addition to this, it offers flexibility to showcase throughput and ETL performance.
In addition to the tools, as mentioned above, various Python tools are available in the market, including Blaze, PyQuery, BeautifulSoup, Dask, Ecosystem, and DyND Datashape, Joblib, Odo, Riko, Retrying, lxml, to name a few. Riko contributes to being a suitable python ETL tool, which is beneficial for streaming data. This tool is useful in making the extraction of different data streams easily. Joblib is a popular ETL tool that makes the right use of different Python functions for the pipelines.
Speaking of different Python ETL tools, libraries and frameworks, a bunch of options are available in the market. The tools, as mentioned above, play an integral role in handling different demanding workloads. It is possible to set different robust pipelines in no time with the aid of these python tools.
You must log in to post a comment.