Data is continuously evolving thanks to accessible and cheap storage. Because of this, smart businesses build their own systems to adequately process and take advantage of data. Businesses load data to storage repositories (known as data lakes) to keep it safe and ready to be analyzed.

In this process, programmers can use a Python data pipeline framework in order to create a scalable and flexible database.
A practical data pipeline built with Python helps businesses process data in real-time, make several changes without any data loss, and allow other engineers and data scientists to easily explore data. In this post, we will show you the right methods and tools for building great data pipelines in Python.

With a Pyhton´s data pipeline is somehow similar to a data processing sequence commonly used by the Python programming language in other arenas. Typically, data is processed at the start of Python pipelining. Then there are several stages, in which every step produces an output which is the input for the next stage.

This will happen in an ongoing flow until the pipeline is finished. However, there may be some autonomous steps conducted simultaneously in certain stages. Every Python data pipeline framework has three major components: Source, Processing steps and a Destination (sink or Data Lake).

It works like this: the framework lets data to move from a source application to a lake (data warehouse). Depending on the app type, the flow might also continue from a data sink to storage for business analysis or straight to a payment processing system.

Some frameworks are built with a similar source and sink, allowing programmers to focus on processing or modification steps. Because of this, Python pipelining mainly deals with data processing between two specific points. however, it is worth noting that more processing steps can happen between these two points. Data created by a single source process or website might feed numerous data pipelines. Those streams might be dependent on the results of numerous other pipelines or applications.

For example, let’s take some comments made by numerous Facebook users on the app. These comments may create data for a real-time analysis which tracks social media comments. The analysis, which starts from a source, may be used on a sentiment assessment application that reveals an unfavorable, good, or neutral outcome. On the other hand, it can be also used on an app that plots all comments on a globe map.

The application is completely different even though the data is almost the same. Each of these apps is based on its unique set of frameworks that must run in a highly efficient way for the user to see an adequate outcome.

Data processing, refinement, augmenting, grouping, screening, aggregation, and analytics application are all normal phases in a data pipeline. One of the most common type of data pipeline used by programmers is ETL (Extract, Transform, Load). ETL, which uses the Python framework, simplifies the data pipelining process.

In a Python pipeline, extraction is the first step, which means obtaining data from the source. Then, this data is processed in following stage, known as Transform. The third stage is Load, which implies loading the data.

Python is a smooth and flexible language with a wide environment of code libraries and modules, which is awesome for data pipelines. Understanding the required libraries and frameworks, like some workflow management tools, is helpful for drafting data in data pipelines. Programmers use support libraries and tools for extracting and accessing data to write ETL in Python.

Workflow management refers to altering, creating and tracking workflow applications which consecutively regulate corporate processes´ completion. It coordinates the engineering and maintenance of different tasks within the ETL framework. Workflow systems like Luigi and Airflow can perform ETL activities as well.

- Airflow: Apache Airflow uses directed acyclic graphs (DAG) to portray task interactions within the ETL framework. The tasks carried out in a DAG comprise both dependents and dependencies as they are directed. Note that visiting any phase with this system won´t make the task revisit or drift back a previous task as they are acyclic and not cyclic. Airflow has a graphical user interface (GUI) and a command-line interface (CLI) for tracking and viewing tasks.
- Luigi: Spotify programmers created Luigi to streamline and handle operations. This is done for creating weekly playlists and suggested mixes, for instance. Luigi is currently designed to operate with several workflow systems. Users should be aware that Luigi is no intended to work with several tens of thousands of programmed processes.
Regarding data movement and processing, Python may use libraries like pandas, Odo and Beautiful Soup to gather, transmit and modify data, as well as for overall workflow planning and management. These are powerful tools for data manipulation, among other awesome uses.