The Best Guide to Build Data Pipeline in Python

Data is constantly evolving thanks to cheap and accessible storage. As such, intelligent clients build their business systems to take and process more data.

Afterward, they load the results in a storage repository (data lake) to keep them safe and ready for analysis. Individuals use this python data pipeline framework to create a flexible and scalable database.

A functional data pipeline python helps users process data in real-time, make changes without data loss, and allow other data scientists to explore the data easily. In this post, you will discover the right tools and methods of building data pipelines in Python. 

Python Data Pipeline Framework

A data pipeline python framework is similar to a data processing sequence that uses the Python programming language. Usually, data that is yet to be on the centralized database is processed at the beginning of Python pipelining. 

Then there will be a sequence of stages, where every step now produces an output that becomes the input for the following location. This will be in a continuous flow till the pipeline is finished. 

However, autonomous steps may be conducted simultaneously in certain instances. Every python data pipeline framework contains three major components:

• Source
• Processing step (or steps)
• Destination (sink or Data Lake)

Here is how it works: the framework allows data to move from a source application to a sink (data warehouse). Depending on the type of application, the flow may also continue from a data lake to storage for analysis or directly to a payment processing system. 

Some frameworks are built with a similar sink and source, allowing programmers to focus on modification or processing steps. As such, Python pipelining deals mainly with data processing between two points. 

It is important to note that more processing steps can exist between these two points. 

Data created by a single source website or process may feed several data pipeline python programming. Those streams may be reliant on the results of several other applications or pipelines.

For instance, let’s take comments made by several Facebook users on the social media app. 

Such comments might create data for a real-time analysis that tracks social media comments. The analysis, which is from a source, can be used on a sentiment assessment application that reveals a good, unfavorable, or neutral outcome. Alternatively, it can be used on an app that plots each comment on a globe map. 

The application is different even though the data is similar. Each of these apps is based on its own set of python data pipeline frameworks that must run efficiently before the user sees the outcome. 

Data processing, augmenting, refinement, screening, grouping, aggregation, and analytics application to that data are all common phrases in data pipeline python. One major type of data pipeline utilized by programmers is ETL (Extract, Transform, Load). ETL, which works using the python framework, simplifies the process of data pipelining. 

The first step in the python ETL pipeline is extraction, i.e., getting data from the source. Then, the data is processed in the second stage, known as Transform. The final stage of the data pipeline framework is Load which involves putting the data to the last point.

 

Necessary Python Tools and Frameworks for Data Pipeline 

Python is a sleek, flexible language with a vast environment of modules and code libraries. Understanding the required frameworks and libraries, like workflow management tools, helps draft data in data pipelines. 

Developers use support tools and libraries used for accessing and extracting data to write ETL in Python. 

Workflow Management

Workflow management refers to creating, altering, and tracking workflow applications that sequentially regulate the completion of corporate processes. It coordinates the maintenance and engineering of tasks in the framework of ETL. 

Workflow systems like Airflow and Luigi can also perform ETL activities.

• Airflow: Apache Airflow utilizes directed acyclic graphs (DAG) to depict task interactions in the ETL framework. The activities carried out in a DAG include both dependencies and dependents as they are directed. 

Note that visiting any stage with this system will not make the task drift back or revisit a prior activity as they are not cyclic but acyclic. Airflow has a command-line interface (CLI) and a graphical user interface (GUI) for tracking and viewing tasks.

• Luigi: Spotify creators implemented Luigi to handle and streamline operations. An example is the creation of weekly playlists as well as suggested mixes. 

It’s now designed to operate with a plethora of workflow systems. Intended users should note that Luigi isn’t intended to work past tens of thousands of programmed processes.

Data Movement and Processing

Python may use libraries like pandas, Beautiful Soup, and Odo to gather, modify, and transmit data in addition to overall workflow management and planning.

• Pandas: Pandas is an analytical toolkit with powerful data manipulation, making it straightforward to use. You can use Panda for data manipulation and general data work that has links with other tasks. 

The Panda library can connect with other functions like manually designing and disseminating a machine learning approach(algorithm) within a research group. It can also build up autonomous programs that analyze information for a dynamic(real-time) dashboard. 

Developers frequently use Panda in conjunction with Scipy, sci-kit learns, and NumPy. These tools are mathematical, analytical, and statistical libraries that aid in data movement and processing.

• Beautiful Soup: Beautiful Soup is a popular online extracting and processing tool used for gathering data. It has tools for interpreting structured datasets, like JSON records and HTML pages that can be gotten on the internet. 

Beautiful Soup enables programmers to extract data sets from even the most disorderly online applications.

• Odo: Odo has a single, self-explanatory function that converts the data across formats.Odo (source, target) may be referred to as native Python data sets by Programmers. 

The data gathered can be instantly transformed and available for implementation by other codes in the ETL framework.

Self-Contained ETL Toolkits

Bonobo, petl, and pygrametl are among the subset of the Python libraries. These toolkits are the features that make up entirely the ETL framework.

• Bonobo: Bonobo is a simple toolkit that executes ETL tasks using basic Python abilities like functions and iterators. These features are interlinked in DAGs and can be launched simultaneously. 

Bonobo is developed for making alterations that are straightforward, spontaneous, yet convenient to evaluate and track.

• Petl: petl is a versatile ETL tool with an emphasis on convenience and accessibility. This program is not suitable for extensive or memory-intensive databases and pipelines, notwithstanding its flexibility. 

It’s best suited as a lightweight ETL tool that analyzes and monitors straightforward processes.

• Pygrametl: pygrametl also delivers ETL capability in Python script that may be easily integrated into similar Python programs. Interfaces with Jython & CPython modules are included in pygrametl, enabling coders to interface with other applications while increasing ETL efficiency and productivity.

Getting Started with Data Pipelines

Generally, website owners need data pipeline python to figure out the number of monthly or yearly users. 

Some people may need Python pipelining for weather predictions through a weather database. In the previous example, you will find that data pipelining can analyze comments on a site. 

Programmers build data pipelines using different techniques depending on several factors. Such factors may involve the type of compatible library tools, business goals, and the technical goals of the programmer. 

Python functions well when handling hierarchical database systems and dictionaries, both of which are crucial in ETL. When building data pipeline python for a web source, you will need two things:

1. The website’s Server Side Events (SSE) to get real-time streams. Some programmers develop a script to do this while others request or purchase the web’s API. After receiving the data, they use Python’s Pandas module to analyze them in groups of 100 items and then store the outcomes into a central database or data lake.
2. You’ll need a current version of Python downloaded to start Python pipelining. It’s typically ideal to commence a new task with a clean version of the program. 

Once the source application is ready, set up an SQLite Database using the sqlite3 C library. Note that the data obtained from the source web is now available in JSON format. You can save it to your current directory as a JSON document. 

You can also use the “dt” (DateTime) parameter to name each JSON file. Afterward, transfer the file to the dictionary before moving it to the Pandas library. 

Some programmers use the Moto library to run Python pipelining. This system simulates the  AWS (Amazon Web Services) architecture located in a host server. If you are working with Moto library, you must use SQS (Simple Queue System) to organize data from the web source. 

You will also need to use S3, a simple storage framework, as the sink (data lake). Note that S3 will store the outcomes as CSVs.

Python is flexible enough that programmers may utilize native database systems to code practically any ETL operation. For instance, using the in-built Python math package to remove null entries from a sequence is simple. Developers may also use category comprehensions to accomplish the same goal.

It is inefficient to design the complete ETL process from the start. As such, many ETL pipelining combines native Python code and well-defined procedures or classes. This includes those from the frameworks described above. 

Users may use pandas library to screen a whole DataFrame of values having nulls, for example:

filtered = data.dropna()

Many systems include Python SDKs (software development kits), APIs, and other tools, which are sometimes beneficial in ETL scripting. The Anaconda framework, for instance, is a Python compilation of data-related modules and frameworks. 

It comes with its own package management and cloud storage for programming notebooks and Python setups. Many of the information that pertains to regular Python programming is suitable for data pipeline frameworks. 

As a result, developers should adhere to several language-specific rules that make programs brief and clear while still expressing the programmer’s objectives. Visibility, an efficient runtime environment, and keeping an eye on dependencies are all essential.

Processing Data Streams With Python

A streaming data pipeline transmits data from source to destination instantaneously (in real-time), making it relevant to the data processing steps. Streaming data pipelines are used to feed data into data warehouses or disseminate to a data stream. 

The streaming data pipelines shown below are for analytical applications.

Kafka Messages to Amazon S3

The source and destination of data might rapidly emerge into a maze of intertwining streaming data pipelines. With the use of Kafka, developers can grow their application both horizontally and vertically. 

They can also manage numerous sources and destinations. Developers typically expand Kafka messages to S3 and accommodate enormous workflows.

Amazon’s Credit Card Data Protection

Amazon Kinesis is a dynamic streaming service that Amazon supports. It works well with Redshift, S3, and other analytical cloud platforms. The protection service utilizes an identified credit card type as a partitioning key in a file. 

After the partitioning, credit card types are then used for data disguising by Kinesis. It then disseminates the data to Amazon.

Tracking Twitter Mentions

A tweeter could be interested in following tweets of their beloved team on Twitter. Opinions about these teams might be employed in determining budget investment in advertisements. This attitude analysis pipeline allows developers to transfer data from Twitter to Apache Kafka. 

After this task, it then prepares the data for the Azure Sentiment Analysis before it is ultimately stored for accessibility in developers’ free time.

Tensorflow’s Machine Learning

Machine learning gathers insights from enormous, unstructured data sets by applying algorithms to them. For instance, data on breast cancer tumors might be analyzed and categorized as life-threatening or non-deadly. 

Programmers do this to fully understand the therapy and preventive measures in the environment and society. This data pipeline demonstrates how to use Tensorflow to consume data and generate predictions or classifications instantaneously.

Conclusion

All this in-depth information should be enough for interested developers to start on their python data pipeline framework. Most of the tutorials here can be used to practice and learn Python pipelining from small real-time data. 

However, a massive data pipeline requires careful planning and design for easy handling. Developers now need a high-level abstraction of how the data should move from source forms to the respective destination.