Ready to dive into the lake?
lakeFS is currently only
available on desktop.

For an optimal experience, provide your email below and one of our lifeguards will send you a link to start swimming in the lake!

lakeFS Community
Amit Kesarwani
Amit Kesarwani Author

Amit heads the solution architecture group at Treeverse, the company...

Last updated on June 2, 2024

Data pipelines are critical for organizing and processing data in modern organizations. A data pipeline consists of linked components that process data as it moves through the system. These components may comprise data sources, write-down functions, transformation functions, and other data processing operations like validation and cleaning. 

Pipelines automate the process of gathering, converting, and analyzing data. They’re a set of procedures that transform raw data, clean it, process it, and store it in a format that allows it to be readily analyzed or utilized in other applications. Pipelines are very effective when dealing with enormous volumes of data or working with data that must be continually updated, ensuring high data quality.

How does the world of data pipelines intersect with Python, one of the most commonly used programming languages today? Keep reading to find out the essentials of data pipelines in Python.

What are Data Pipelines in Python?

A data pipeline is a process that takes data from several sources and transfers it to a destination, such as an analytics tool or cloud storage. From there, analysts may convert raw data into useful information and generate insights that lead to corporate progress.

Here is an example of an Extract, Load, and Transform (ELT) pipeline:

Example of ETL pipeline
Source: https://docs.getdbt.com/terms/elt

Python Data Pipeline Frameworks

Data pipeline frameworks are specialized tools for creating, deploying, and maintaining data pipelines. They come with pre-built functionality for job scheduling, dependency management, error handling, and monitoring. Such Python libraries also provide an organized approach to pipeline creation, guaranteeing that the pipelines are strong, dependable, and effective.

Several Python frameworks are available to help with the process of developing data pipelines:

  • Luigi – a Python package that allows you to create complicated pipelines of batch operations. It manages dependency resolution and assists with workflow management, making it easy to specify jobs and their dependencies.
  • Apache Beam – it provides a single model that enables developers to create data-parallel processing pipelines. Beam supports both batch and streaming data, giving it a great degree of flexibility. It’s considered a versatile tool for tackling various data processing requirements.
  • Airflow – a systematic platform for defining, scheduling, and monitoring routines. It lets you design tasks and dependencies and orchestrate and monitor processes.
  • Dask – a flexible Python module that allows you to conduct parallel computing operations easily. It supports concurrent and larger-than-memory calculations and interfaces well with existing Python modules such as Pandas and Scikit-Learn.
  • Prefect – a workflow management system that promotes fault tolerance and makes it easier to create data pipelines. It offers a high-level, Python-based interface for specifying tasks and dependencies.

The above is just the tip of the iceberg. Here are more key Python libraries and frameworks that teams use to build data pipelines, divided into categories.

Pipelines for Streaming Data in Python

Real-time pipelines process data as it’s created. Using tools like Kafka-Python, Faust, and Streamz, teams can build streaming data pipelines that handle massive volumes of data in real time.

Streamz

While many Python pipeline tools are built for batch processing, Streamz is dedicated to creating efficient streaming pipelines for real-time data integration.

Its key capabilities include:

  • Asynchronous pipelines with Asyncio and threads 
  • Publish-subscribe pattern used for inter-process communication 
  • Flexible topologies, such as graphs and trees
  • High throughput with minimum serialization 
  • Integration of Kafka, Kinesis, and other data streams

By offering high-performance streaming primitives such as queues and topics, Streamz makes it easier to react to live data sources and power real-time analytics apps.

Faust

Faust is a Python-based stream processing package designed for use with streaming data platforms like Apache Kafka and Redpanda. With Faust, we can create pipelines that consume data from Kafka topics and perform computations on it. 

Since Faust is developed in Python, it can be easily integrated with other Python data libraries like Pandas, NumPy, and SciPy.

Faust’s storage engine is RocksDB, a C++-based key-value store designed for lightning-fast speed. Persisting state makes use of an abstraction known as a “table,” which is a named and distributed collection of key-value pairs that can be interacted with like standard Python dictionaries.

Kafka-Python

Apache Kafka is a distributed and scalable streaming infrastructure capable of processing billions of events per day. It was designed to manage massive volumes of real-time data while maintaining high throughput and low latency.

Kafka is widely used for real-time streaming data processing and is regarded as one of the quickest and most dependable streaming platforms available. Companies of all sizes use it to build real-time data pipelines and stream processing systems.

Pipeline Libraries for Data Processing

Python has a diverse ecosystem of libraries for creating data processing pipelines. Here are some essential libraries for data processing and analysis:

Pandas

Pandas is a solid library for data manipulation and analysis. Pandas allows data to be imported in a variety of formats, including CSV, Excel, and SQL tables, and then saved as data frames. Pandas also provides a variety of data manipulation operations, including filtering, grouping, and aggregation.

NumPy

A Python library for numerical computations. NumPy provides a range of functions for numerical calculations, including linear algebra, Fourier transformation, and random number generation. Many more data science libraries are built on top of NumPy.

Dask

A parallel computing package designed for large-scale data processing. Dask allows you to process big data sets in parallel over a cluster of machines. Dask additionally has tools for storing and analyzing huge datasets in distributed systems.

Data Pipelines for Machine Learning with Python

Python is commonly used to build data pipelines for machine learning. TensorFlow, Keras, and PyTorch are strong libraries for developing and training machine learning models, while Scikit-learn provides a complete set of machine learning algorithms and data preparation tools.

Scikit-learn

Scikit-learn is a Python-based machine learning and data mining library. It provides several machine learning techniques, including regression, classification, clustering, and dimensionality reduction. Scikit-learn additionally includes routines for data modeling, assessment, and selection.

TensorFlow

TensorFlow is a comprehensive deep learning framework. The Google Brain team created it to use internally at Google for research and production, but in 2015, they declared it open-source under the Apache License. 

TensorFlow can train models on various hardware platforms, including CPUs and GPUs, allowing for flexible and efficient calculations. TensorFlow offers distributed computing, which allows data and models to be processed simultaneously across numerous CPUs or GPUs, dramatically reducing training time.

TensorFlow’s visualization toolbox helps in understanding, debugging, and optimizing applications. TensorFlow isn’t confined to one device. Users may use the framework to create systems that operate just as well on edge devices as on other complicated machines.

Keras

François Chollet developed and released Keras in 2015, a well-known open-source high-level neural network API. Interestingly, Keras is described as “an API designed for human beings, not machines.”

In mid-2017, the framework was adopted and incorporated into TensorFlow, making it available to TensorFlow users via the tf.keras module. However, you can still use Keras without TensorFlow.

Keras provides a user-friendly API that makes deep learning straightforward to understand and use. It doesn’t need low-level calculations to function; instead, it is based on Microsoft CNTK, Theano, and TensorFlow. As a result, it encourages the use of backend systems.

Keras includes multiple pre-trained models out of the box and a high-level API so users can create models quickly with only a few lines of code.

PyTorch

The Linux Foundation now makes use of PyTorch, which Meta’s AI research division developed and open-sourced in 2016. PyTorch has earned a reputation as a framework that is easy to use, flexible, and efficient. It allows developers to quickly create complicated neural networks for applications like computer vision and natural language processing.

PyTorch has a multidimensional array called Tensor, which is comparable to NumPy’s ndarray but can operate on GPUs for quicker calculation. It employs dynamic computational graphs, allowing for the rapid creation and modification of models, which is advantageous for complicated designs.

PyTorch integrates easily into the Python environment, making it easier to use with Python modules while also giving a more Pythonic and user-friendly interface. PyTorch is widely regarded as having a simple learning curve, with some calling it one of the easiest deep learning packages to learn.

Key Components of Python Data Pipeline Frameworks

Any data pipeline contains five key components:

Component Description
Sources Where the data comes from. A source might be an application, a website, or a production database, such as MySQL or PostgreSQL.
Workflow The workflow determines the order of actions and operations inside a pipeline. It dictates when and how each task is completed.
Storage A storage system is a centralized repository for your data. It might refer to a data warehouse, lake, or lakehouse.
Transformation Data transformations are used to organize and make collected data more accessible. Data may be entered, modified, removed, and standardized here.
BI Tools Enterprises utilize business intelligence (BI) tools to process large amounts of data for queries and reports.

Python Data Pipeline Building Process with Examples

1. Installing the Required Packages

Before you start creating a data pipeline, you must install the essential Python packages using pip, Python’s package installer. If you want to use Pandas for data manipulation, use the command ‘pip install pandas’. If you’re using a specific framework, such as Airflow, install it using ‘pip install apache-airflow’.

2. Data Extraction

The initial stage is about obtaining data from multiple sources. This may include reading data from databases, APIs, CSV files, or web scraping. Python makes this process easier using libraries like ‘requests’ and ‘beautifulsoup4’ for web scraping, ‘pandas’ for CSV file reading, and ‘psycopg2’ for PostgreSQL database interface.

3. Data Transformation

Once the data has been extracted, it must often be translated into an analytically appropriate format. This may include cleaning the data, filtering it, aggregating it, or conducting additional computations. The Pandas library is especially handy for these activities. Specifically, you may use ‘dropna()’ to eliminate missing values and ‘groupby()’ to aggregate data.

4. Data Loading

After transformation, the data is fed into a system for analysis. It might be a database, a data warehouse, or a data lake. Python includes numerous libraries for interfacing with such systems, including Pandas and SQLAlchemy for publishing data to an SQL database, as well as Boto3 for smooth interaction with Amazon S3 if you use an AWS data lake.

5. Data Analysis

The final stage involves evaluating the supplied data to provide insights. This might include making infographics, developing machine learning models, or doing statistical analysis. Python has various libraries for similar tasks, including Matplotlib and Seaborn for visualization, Scikit-learn for machine learning, and Statsmodels for statistical modeling.

Throughout this process, promptly handling mistakes and failures is critical. This ensures data is handled consistently and gives insight into the pipeline’s status. Python’s data pipeline frameworks, including Luigi, Airflow, and Prefect, offer capabilities for defining tasks and their dependencies, scheduling and running tasks, and tracking task performance.

How to Design an ETL Pipeline in Python

When developing a Python data pipeline architecture, consider the following components:

  1. Data Ingestion – Determine the sources of your data and develop strategies for collecting and capturing it.
  2. Data Storage – Use appropriate storage systems, such as databases or data storage systems, to store raw and processed data.
  3. Data Processing – Create and implement data processing activities, including cleaning, validation, transformation, and enrichment.
  4. Data Analysis and Visualization – Implement data analysis and visualization tasks with Python tools like Matplotlib, Seaborn, and Plotly.
  5. Data Orchestration and Scheduling – Use data pipeline frameworks like Apache Airflow or Luigi to schedule and manage your data processing jobs.
  6. Data Pipeline Automation – this is the holy grail of efficiency.

Benefits of ETL Pipeline Development with Python

Gentle learning curve

Python’s syntax is clear and consistent, making it easy to write and comprehend ETL code. Python also has a REPL (read-eval-print loop) that enables interactive ETL code testing and debugging.

Furthermore, Python follows a “batteries included” concept, which includes built-in modules and methods for common ETL activities such as data extraction, manipulation, processing, and loading.

For instance, you may use the CSV module to read and write CSV files, the JSON module to work with JSON data, the SQLite3 module to connect to SQLite databases, and the urllib module to access online resources. As a result, if you need a straightforward solution to create data pipelines, creating an ETL using Python might be a viable option.

Flexibility

Python’s flexible and dynamic type system enables ETL developers to work with a variety of data sources and formats, including CSV, JSON, SQL, and XML. Python provides a variety of programming paradigms and styles, including object-oriented, functional, and procedural, allowing ETL developers to select the appropriate method for their ETL logic and architecture.

Python’s modular and scalable structure enables ETL developers to organize their code into reusable and manageable components like functions, classes, and modules. For instance, you can create a simple data pipeline by using the Pandas library to create and manipulate DataFrames, the NumPy library to execute numerical computations, the SciPy library to perform scientific and statistical operations, and the Matplotlib library to create and show data visualizations. 

If you’re searching for a versatile and adaptable solution to create data pipelines, Python-based ETL is your best option.

Vast ecosystem

Python offers a large and diversified collection of third-party modules and frameworks that can handle many components of the ETL process, including data extraction, transformation, loading, and workflow management. Pandas, Beautiful Soup, Odo, Airflow, Luigi, and Bonobo are some popular Python tools and frameworks for ETL.

These tools and frameworks include features and functionalities that can improve the ETL process’s performance and efficiency, such as data cleaning, aggregation, merging, analysis, visualization, web scraping, data movement, workflow management, scheduling, logging, and monitoring.

For example, you can use the Beautiful Soup library to extract data from HTML and XML documents, the Odo library to move data between different formats and sources, the Airflow framework to create and run ETL pipelines, the Luigi framework to build complex Python data pipelines, and the Bonobo framework to build ETL pipelines using a functional programming method. And here’s your basic data pipeline.

Challenges of Building Data Pipelines in Python

Complexity

ETL configuration in Python is complex and difficult to design, develop, and debug, especially when working with vast and diverse data sources and formats, including CSV, JSON, SQL, and XML. 

Python ETL developers must have a thorough grasp of data sources, business logic, and data transformations, as well as the Python tools and frameworks that can manage them. Python ETL developers must also create a large number of unique programs and scripts to connect, extract, process, and load data, which can lead to mistakes and issues.

For example, if you wish to extract data from a web page in Python, you may need to use a library such as Beautiful Soup to parse the HTML, queries to perform HTTP queries, and LXML to handle XML data. As a result, implementing ETL using the language and troubleshooting Python data pipelines may require a significant amount of time and effort.

Maintenance

Maintaining and upgrading ETL using Python may be challenging and costly, especially if the data sources, business needs, or destination systems change. Python ETL developers must regularly monitor and test their ETL pipelines, deal with mistakes and exceptions, log and track the ETL process, and improve ETL performance.

Python ETL developers must also verify data quality and correctness, as well as security and compliance with data transmission protocols. 

For example, suppose you want to load data into a data warehouse with Python. In that case, you may need to utilize a package like SQLAlchemy to design and manage the database schema, Pandas to modify and validate the data, and pyodbc to run SQL queries. 

As a result, if you are not attentive and vigilant, you may have a cluttered and unreliable ETL pipeline that jeopardizes the quality and integrity of your data.

Performance

Python is an interpreted language that performs slower than compiled languages like C or Java. Python also has a global interpreter lock (GIL), which prohibits several threads from running Python code at the same time, restricting the ETL process’s concurrency and parallelism.

In addition, Python has high memory usage and trash collection costs, which might have an impact on the ETL process’s scalability and reliability. If you work with massive and complicated data sets, utilizing Python to configure ETL may have an impact on your system’s performance.

Code vs. No-Code Approach

As the process of creating and managing data pipelines has grown more complex, no-code solutions are on the rise. They provide a degree of flexibility and adaptability that standard coding methods don’t, making data management more inclusive, flexible, and efficient.

While Python remains a viable option, enterprises are increasingly embracing no-code data pipeline solutions. The goal of this strategy move is to empower data practitioners at all levels by democratizing data management, fostering a data-driven culture, and streamlining pipeline development.

Benefits of No-Code Data Pipeline Solutions

Choosing an automated no-code method to design data pipelines has various advantages, such as:

Advantage Description
Efficiency No-code solutions speed up the process of creating data pipelines. They provide pre-built connections and transformations that may be configured without requiring any coding. This allows data workers to focus on extracting insights from data rather than developing pipelines.
Accessibility No-code solutions are intended to be user-friendly, especially for non-technical users. They often include straightforward graphical interfaces that let users create and maintain data pipelines using a simple drag-and-drop approach. This democratizes the process of creating data pipelines, allowing business analysts, data scientists, and other non-technical users to build their own pipelines without having to learn Python or another programming language.
Management & Monitoring Functionality Most no-code systems provide built-in functionality for monitoring and managing data pipelines. These may include pipeline failure alarms, pipeline performance dashboards, and pipeline versioning and deployment tools.

Data Pipelines & Python: Best Practices

Designing Modular and Reusable Pipeline Components

Breaking up a data pipeline into modular Python scripts makes it easier to maintain and reuse. Modularizing makes it easier to design reusable component libraries, which speeds up development. 

Some recommended practices are:

  • Placing data input, transformation, and loading logic into independent modules
  • Parameterizing essential settings such as data sources, destinations, and schemas 
  • Avoiding hardcoding 
  • Designing modules that can be imported and reused across pipelines 
  • Sharing common ETL logic 
  • Using open-source data pipeline frameworks that promote modularity (think Dagster) 

Ensuring Quality with Testing & Validation in Python Pipelines

Make sure to unit test each pipeline module using tools such as PyTest in your data testing environment. Mock inputs and outputs and validate the entered data structure/schema before converting. 

Always check the quality of the output data using statistical tests for completeness, validity, and other factors. Create a separate validation module that performs SQL tests on the output data. Also, consider using visual data profiling tools to examine output data samples.

Advanced Monitoring & Alerting Techniques

To monitor pipeline runs, use workflow tools such as Apache Airflow. Send custom stat measurements to Graphite/StatsD to plot performance and set up early failure alerts using Slack or email notifications.

Monitor the use of resources such as CPU and memory across all components. Set up custom alerts for SLAs, data quantities, task durations, and other parameters. Finally, ensure you have insight into all the systems that make up the data pipeline.

Leveraging Advanced Python Pipeline Tools and Frameworks

Python provides diverse tools and frameworks for creating, managing, and deploying data pipelines. As pipelines become more sophisticated, these specialized libraries may improve development productivity, enforce best practices, and ease deployment.

Kedro

Kedro is an open-source Python framework for developing repeatable, maintainable, modular data pipelines. It automatically manages pipeline dependencies based on the data flow specified in the pipeline code.

Kedro allows developers to focus on business logic while the framework manages workflow structure and instrumentation. This makes it easy to create strong pipelines that evolve naturally over time.

Dagster

Dagster is a data orchestrator that creates, schedules, and monitors pipelines. Dagster allows you to:

  • Define pipelines and individual components as reusable assets 
  • Schedule pipeline runs according to intervals or external triggers 
  • Visualize pipeline graphs and monitor run metadata 
  • Integrate orchestration with Apache Spark, Apache Airflow, and other technologies 
  • Deploy pipelines to production using containerization

These features aid in managing pipelines at scale, allowing for more consistent and frequent execution of data transformation tasks. Dagster also makes testing data pipelines and monitoring them more efficient.

Troubleshooting Python Data Pipelines with lakeFS

lakeFS brings version control to data lakes using Git-like semantics. It allows you to use concepts like: 

  • Branch to establish an isolated version of the data 
  • Commit to create a repeatable point in time 
  • Merge to combine your changes in a single atomic operation

Using lakeFS together with one of the pipeline management tools is a smart move: it allows you to create an isolated data environment in which to execute production pipelines, ensuring that only high-quality data is exposed to production. 

Instead of running your data pipelines directly on production data, lakeFS makes testing data pipelines simpler by creating an isolated zero clone copy of the data, allowing you to securely run production pipelines and debug any failures or quality concerns immediately.

Here are two examples of how you can run lakeFS with Prefect:

Option 1: Run the pipeline in isolation

Run pipeline in isolation

Using this method, you can generate a branch from the production environment (with lakeFS, this is a zero-copy operation that takes a few moments) and run a Prefect pipeline on it. Once done, you can combine the pipeline’s final output and bring it back into production.

This strategy is the best pick for a quick win since you can wrap any current Prefect pipeline in minutes.

Pros of this approach:

  • Very quick setup for any existing Prefect pipeline
  • Isolated “staging” area where data is promoted atomically only once a pipeline is completed
  • You only send high-quality data to production; consumers are unaware of “in-process” data
  • In the event of a mistake, the data assets may be easily rolled back to before the pipeline was begun

Option 2: Execute Git actions within Flows

Execute git actions with flows

This approach doesn’t just execute the pipeline on an isolated branch. You can also perform Git actions within the Flows, meaning you’ll get a commit history of modifications across all steps performed within the pipeline.

This solution takes more code but adds greater value to the prior one since you get:

  • Complete, in-context version of the data for every step performed inside a Flow
  • Isolated “staging” area. Data is automatically promoted only when a pipeline finishes
  • The ability to only send high-quality data to production; consumers are unaware of “in-process” data
  • In the event of a mistake, the data assets may be easily rolled back to before the pipeline was started

Conclusion

Building efficient data pipelines in Python is a key skill for data professionals. Using Python’s vast ecosystem of libraries, frameworks, and tools, you can create data pipelines that convert raw data into meaningful insights, allowing you to make data-driven choices and drive your organization’s success.

Ready to take your pipelines to the next level? Check out this guide that will help you Troubleshoot Data Pipelines and Reproduce Data with lakeFS and Prefect.

Git for Data – lakeFS

  • Get Started
    Get Started
  • Who’s coming to Data+AI Summit? Meet the lakeFS team at Booth #69! Learn more about -

    lakeFS for Databricks
    +