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src/content/podcast/2024/ep46_nextflow_vs_wdl.md

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 ## Summary of the episode
 
 ### Introduction
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 The comparison between Nextflow and WDL (Workflow Description Language) is a fascinating one for anyone involved in bioinformatics and data science workflows. Both languages have their unique strengths and offer different approaches to solving similar problems. This blog post delves into the intricacies of both languages, highlighting their differences, similarities, and areas of potential improvement.
 
 ### Meet the Contributors
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 Ben Sherman, a software engineer at Seqera and one of the main developers of Nextflow, brings a wealth of experience to the discussion. Geraldine, Lead Developer Advocate at Seqera, offers insights from her extensive past use of WDL. Together, they provide a comparative overview of these two powerful workflow languages.
 
 ### Introduction to WDL
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 WDL, or Workflow Description Language, was developed at the Broad Institute starting around 2014. This language emerged during a period when existing workflow solutions were struggling to scale with increasing data volumes and the need for greater reproducibility. The Broad Institute, a nonprofit research institution in Cambridge, MA, needed a more effective software stack for its genome analysis pipeline, which was moving to the cloud.
 Several workflow management tools were considered, including Queue (integrated with the GATK), Firehose, and Zamboni. The CWL (Common Workflow Language) which was emerging at the time was also reviewed but deemed too machine-oriented. This led to the development of WDL as a user-friendly, cloud-native DSL (domain-specific language) designed for high reproducibility. Today, WDL is an open-source project supported by two main engines: [Cromwell](https://github.com/broadinstitute/cromwell) (focused on high-scale execution) and [MiniWDL](https://github.com/mlin/miniwdl) (geared towards prototyping).
 
 ### WDL Development History
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 From its inception, WDL was intended to simplify the process of sharing genomic analysis pipelines. With WDL, researchers can share readable, user-friendly scripts, with some limitations. As it evolved, WDL introduced more flexibility while keeping complexity in check. This balance is crucial for maintaining an approachable yet powerful workflow language.
 
 ### Key Differences Between WDL and Nextflow
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 One of the major differences between Nextflow and WDL lies in their approach to parallelization and data handling. WDL uses explicit task invocation and a scatter function for batch processing. This approach is straightforward but can be limiting in complex scenarios.
 On the other hand, Nextflow relies on channels to handle data flow between processes. This abstraction simplifies the user’s code and allows for more compact, readable scripts. Channels can hold single values, lists, or result sets, enabling processes to consume and produce data efficiently. While this implicit handling of data flow can be challenging to learn, it offers significant rewards in process efficiency and code simplicity.
 
 ### Advanced Features of Nextflow
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 Nextflow processes and channels set it apart from most workflow managers. Inspired by Unix’s pipeline philosophy, Nextflow allows for powerful data manipulation through operators like map, flatMap, and filter. These operators enable users to create flexible, scalable workflows with fewer lines of code.
 However, Nextflow's power can also lead to over-engineering in inexperienced hands. The development team continually strives to simplify Nextflow while maintaining its flexibility. For example, integrating static typing and record types could ease the learning curve by clarifying data structures and inputs.
 
 ### Challenges and Improvements in Nextflow
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 Many of Nextflow's operators might be rethought or merged with standard library functions to simplify the language. Adding features like named parameter passing, similar to WDL, could enhance user-friendliness. Balancing power and simplicity is key to preventing users from creating unnecessarily complex workflows.
 
 ### The Future of Nextflow and WDL
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 Both Nextflow and WDL have come a long way and continue to evolve. Lessons from WDL's strong typing and static typing system offer valuable insights for Nextflow's development. Borrowing best practices from each other can drive future improvements.
 
 ### Conclusion
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 The comparison between Nextflow and WDL highlights the strengths of both languages and areas for potential enhancement. While WDL excels in its simplicity and user-friendliness, Nextflow shines in its flexibility and powerful data manipulation capabilities. As both languages continue to develop, they promise to offer even more robust solutions for bioinformatics and data science workflows. Stay tuned for more updates and innovations in both Nextflow and WDL!
 
 ## Resources for newcomers to Nextflow
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 If you're new to Nextflow and you'd like to give it a try, we recommend starting with the [Hello Nextflow tutorial series](https://training.nextflow.io/hello_nextflow/), which will guide you through the process of writing and running your first Nextflow pipelines. This web-based training uses Gitpod and does not require installing anything on your own computer.