11 Best Python Data Orchestration Libraries 2024

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by Kanika Maheshwari dot icon Updated: Feb 15, 2024

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Python Data Orchestration Libraries includes Data Integration and Transformation, Analysis and Visualization, ML, cleaning and preparation, and Storage.   


Here are some best Python Data Orchestration Libraries. Python Data Orchestration Libraries use cases include Data Integration and Transformation, Data Analysis and Visualization, Machine Learning, Data cleaning and preparation, and Data Storage. 


Python orchestration libraries are software libraries that enable developers to create automated workflows and complex systems using Python. They are designed to allow developers to define tasks, create jobs, and manage the workflow of tasks, allowing for the automation of complex processes that would otherwise require manual intervention. 


Let us look at the libraries in detail below. 

pandas 

  • Has powerful capabilities for dealing with missing data. 
  • Provides tools for plotting and visualizing data with various plotting libraries.  
  • Supports integration with popular databases such as MySQL, Oracle, and PostgreSQL.  

pandasby pandas-dev

Python doticonstar image 38689 doticonVersion:v2.0.2doticon
License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

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            pandasby pandas-dev

            Python doticon star image 38689 doticonVersion:v2.0.2doticon License: Permissive (BSD-3-Clause)

            Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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                      dask

                      • Is fast and efficient, allowing for parallel execution of computations. 
                      • Provides a flexible and extensible framework for customizing distributed computing solutions.  
                      • Supports a variety of languages, including Python, R, and Julia. 

                      daskby dask

                      Python doticonstar image 11106 doticonVersion:Currentdoticon
                      License: Permissive (BSD-3-Clause)

                      Parallel computing with task scheduling

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                                daskby dask

                                Python doticon star image 11106 doticonVersion:Currentdoticon License: Permissive (BSD-3-Clause)

                                Parallel computing with task scheduling
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                                          airflow 

                                          • Can be broken down into individual tasks, making tracking progress easier. 
                                          • Is fault tolerant and can handle errors gracefully.  
                                          • Offers an intuitive web UI for monitoring and managing workflows. 

                                          airflowby apache

                                          Python doticonstar image 30593 doticonVersion:2.6.1doticon
                                          License: Permissive (Apache-2.0)

                                          Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

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                                                    airflowby apache

                                                    Python doticon star image 30593 doticonVersion:2.6.1doticon License: Permissive (Apache-2.0)

                                                    Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
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                                                              sqlbucket 

                                                              • Allows users to switch between different data sources easily.   
                                                              • Many of the tedious tasks associated with data orchestration can be automated. 
                                                              • Uses encryption to ensure that data remains secure. 

                                                              sqlbucketby socialpoint-labs

                                                              Python doticonstar image 54 doticonVersion:Currentdoticon
                                                              License: Permissive (MIT)

                                                              Lightweight library to write, orchestrate and test your SQL ETL. Writing ETL with data integrity in mind.

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                                                                        sqlbucketby socialpoint-labs

                                                                        Python doticon star image 54 doticonVersion:Currentdoticon License: Permissive (MIT)

                                                                        Lightweight library to write, orchestrate and test your SQL ETL. Writing ETL with data integrity in mind.
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                                                                                  arbalest 

                                                                                  • Provides an intuitive and user-friendly web-based UI for managing data pipelines. 
                                                                                  • Handle data orchestration needs of various workloads, from big data to machine learning and analytics. 
                                                                                  • Supports multiple data sources and targets, including databases, cloud services, and file systems. 

                                                                                  arbalestby BRL-CAD

                                                                                  C++ doticonstar image 14 doticonVersion:Currentdoticon
                                                                                  License: Others (Non-SPDX)

                                                                                  The project aims to create a geometry editor for BRL-CAD

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                                                                                            arbalestby BRL-CAD

                                                                                            C++ doticon star image 14 doticonVersion:Currentdoticon License: Others (Non-SPDX)

                                                                                            The project aims to create a geometry editor for BRL-CAD
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                                                                                                      dbnd

                                                                                                      • Has a simple syntax and clear documentation. 
                                                                                                      • Offers a unified interface for data-related tasks. 
                                                                                                      • Offers built-in support for cloud data platforms. 

                                                                                                      dbndby databand-ai

                                                                                                      Python doticonstar image 239 doticonVersion:Currentdoticon
                                                                                                      License: Permissive (Apache-2.0)

                                                                                                      DBND is an agile pipeline framework that helps data engineering teams track and orchestrate their data processes.

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                                                                                                                dbndby databand-ai

                                                                                                                Python doticon star image 239 doticonVersion:Currentdoticon License: Permissive (Apache-2.0)

                                                                                                                DBND is an agile pipeline framework that helps data engineering teams track and orchestrate their data processes.
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                                                                                                                          raydp

                                                                                                                          • Enables data scientists to build complex pipelines quickly and easily with minimal code. 
                                                                                                                          • Supports both batch and streaming data processing. 
                                                                                                                          • Offers a rich set of features such as dynamic task scheduling, fault tolerance, and scalability. 

                                                                                                                          raydpby oap-project

                                                                                                                          Python doticonstar image 222 doticonVersion:v1.5.0doticon
                                                                                                                          License: Permissive (Apache-2.0)

                                                                                                                          RayDP provides simple APIs for running Spark on Ray and integrating Spark with AI libraries.

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                                                                                                                                    raydpby oap-project

                                                                                                                                    Python doticon star image 222 doticonVersion:v1.5.0doticon License: Permissive (Apache-2.0)

                                                                                                                                    RayDP provides simple APIs for running Spark on Ray and integrating Spark with AI libraries.
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                                                                                                                                              SmartSim 

                                                                                                                                              • Provides a comprehensive set of APIs and tools for building and orchestrating. 
                                                                                                                                              • Its out-of-the-box data integration capabilities make it ideal for complex data integration projects. 
                                                                                                                                              • Offers a unique scheduling system for managing data pipelines. 

                                                                                                                                              SmartSimby CrayLabs

                                                                                                                                              Python doticonstar image 168 doticonVersion:v0.4.2doticon
                                                                                                                                              License: Permissive (BSD-2-Clause)

                                                                                                                                              SmartSim Infrastructure Library.

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                                                                                                                                                        SmartSimby CrayLabs

                                                                                                                                                        Python doticon star image 168 doticonVersion:v0.4.2doticon License: Permissive (BSD-2-Clause)

                                                                                                                                                        SmartSim Infrastructure Library.
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                                                                                                                                                                  icevision 

                                                                                                                                                                  • Makes it easier to explore data quickly and quickly develop models.   
                                                                                                                                                                  • The library allows users to create and customize their data orchestration pipelines easily. 
                                                                                                                                                                  • Is optimized for working with images, which makes it ideal for computer vision tasks.  
                                                                                                                                                                  • IceVision supports various data formats, making it compatible with various data sources. 

                                                                                                                                                                  icevisionby airctic

                                                                                                                                                                  Python doticonstar image 819 doticonVersion:0.12.0doticon
                                                                                                                                                                  License: Permissive (Apache-2.0)

                                                                                                                                                                  An Agnostic Computer Vision Framework - Pluggable to any Training Library: Fastai, Pytorch-Lightning with more to come

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                                                                                                                                                                            icevisionby airctic

                                                                                                                                                                            Python doticon star image 819 doticonVersion:0.12.0doticon License: Permissive (Apache-2.0)

                                                                                                                                                                            An Agnostic Computer Vision Framework - Pluggable to any Training Library: Fastai, Pytorch-Lightning with more to come
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                                                                                                                                                                                      bluesky 

                                                                                                                                                                                      • Designed to run on multiple processors and can be easily distributed across multiple machines. 
                                                                                                                                                                                      • Designed to be highly flexible, allowing users to customize the workflow and data orchestration process to meet their exact needs. 
                                                                                                                                                                                      • Designed to scale up and down depending on the size of the dataset and the complexity of the data orchestration process. 

                                                                                                                                                                                      blueskyby TUDelft-CNS-ATM

                                                                                                                                                                                      Python doticonstar image 264 doticonVersion:2022.12.22doticon
                                                                                                                                                                                      License: Strong Copyleft (GPL-3.0)

                                                                                                                                                                                      The open source air traffic simulator

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                                                                                                                                                                                                blueskyby TUDelft-CNS-ATM

                                                                                                                                                                                                Python doticon star image 264 doticonVersion:2022.12.22doticon License: Strong Copyleft (GPL-3.0)

                                                                                                                                                                                                The open source air traffic simulator
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                                                                                                                                                                                                          nile 

                                                                                                                                                                                                          • Provides an intelligent scheduling engine that can automatically detect and adjust data pipelines based on changes in the data. 
                                                                                                                                                                                                          • Nile is modular and allows users to develop their own tasks and components. 
                                                                                                                                                                                                          • Provides powerful integration capabilities for connecting to external systems. 

                                                                                                                                                                                                          nileby OpenZeppelin

                                                                                                                                                                                                          Python doticonstar image 317 doticonVersion:v0.14.0doticon
                                                                                                                                                                                                          License: Permissive (MIT)

                                                                                                                                                                                                          CLI tool to develop StarkNet projects written in Cairo

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                                                                                                                                                                                                                    nileby OpenZeppelin

                                                                                                                                                                                                                    Python doticon star image 317 doticonVersion:v0.14.0doticon License: Permissive (MIT)

                                                                                                                                                                                                                    CLI tool to develop StarkNet projects written in Cairo
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                                                                                                                                                                                                                              FAQ 

                                                                                                                                                                                                                              1. Do libraries have built-in support for popular data storage and processing technologies? 

                                                                                                                                                                                                                              Yes, Python data orchestration libraries offer built-in support for databases and cloud services. However, the specific level of support and compatibility may vary. It's essential to consult the documentation and resources. Confirm their capabilities and integration options with your chosen technologies. 

                                                                                                                                                                                                                               

                                                                                                                                                                                                                              2. Does some libraries specialize in real-time data orchestration or batch processing? 

                                                                                                                                                                                                                              Yes, there are specific Python Data Orchestration libraries that specialize in batch processing. Some libraries excel in real-time data processing scenarios. Thus ensuring low-latency and high-throughput data orchestration. Others are optimized for batch processing. They are suitable for tasks like processing large volumes of data at scheduled intervals. The choice of library will depend on your specific data orchestration requirements. They may involve real-time, batch, or a combination of both. Review the features and documentation of these libraries to find the suitable one. 

                                                                                                                                                                                                                               

                                                                                                                                                                                                                              3. What are the monitoring and error-handling best practices in Python Data Orchestration?  

                                                                                                                                                                                                                              Best practices for monitoring and error handling in Data Orchestration involve: - 

                                                                                                                                                                                                                              1. Implementing robust logging to record events and errors, 

                                                                                                                                                                                                                              2. Setting up automated monitoring for real-time performance tracking, 

                                                                                                                                                                                                                              3. Defining clear error-handling strategies, 

                                                                                                                                                                                                                              4. Incorporating data validation checks to maintain data quality and 

                                                                                                                                                                                                                              5. Conducting unit testing to ensure the reliability of your data orchestration workflows. 

                                                                                                                                                                                                                              These ensure the smooth operation of your data orchestration pipelines. 

                                                                                                                                                                                                                                

                                                                                                                                                                                                                              4. How can I manage and coordinate data pipelines using Python Data Orchestration libraries? 

                                                                                                                                                                                                                              To effectively manage and coordinate data pipelines using Python Data Orchestration libraries: - 

                                                                                                                                                                                                                              1. choose the right library, 

                                                                                                                                                                                                                              2. design your pipeline with clear task dependencies, 

                                                                                                                                                                                                                              3. implement error handling, 

                                                                                                                                                                                                                              4. validate data, 

                                                                                                                                                                                                                              5. monitor and log pipeline performance, 

                                                                                                                                                                                                                              6. schedule automation, and 

                                                                                                                                                                                                                              7. maintain comprehensive documentation. 

                                                                                                                                                                                                                               

                                                                                                                                                                                                                              5. Can you guide on handling data dependencies and scheduling in Data Orchestration?  

                                                                                                                                                                                                                              To handle data dependencies and scheduling, start by defining task dependencies clearly. Specifying which tasks rely on the successful completion of others. Utilize the Python Data Orchestration library to create a dependency graph. This represents the order in which tasks should run, ensuring no circular dependencies. Some libraries also support dynamic dependencies. This allows you to adjust them based on data conditions or runtime values. 

                                                                                                                                                                                                                               

                                                                                                                                                                                                                              For scheduling, leverage the library's scheduling capabilities. It will help you determine when and how often your data pipeline should execute. You can set up schedules using cron-like expressions or specify intervals between runs. Configure concurrency control, especially if tasks share data dependencies, to prevent conflicts. 

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