Fetch API Data Faster: Parallel API Calls in Microsoft Fabric Notebooks

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How to Parallelize API Calls in Microsoft Fabric Notebooks
Want to speed up your data retrieval from APIs? In this tutorial, Aleksi shows how to parallelize API calls using Microsoft Fabric notebooks. Learn how to drastically reduce processing time by leveraging the concurrent.futures library in Python for efficient parallel processing. Perfect for anyone looking to optimize their data pipelines.
What You’ll Learn:
🔷 How to set up parallel API calls using concurrent.futures
🔷 Best practices for error handling in parallel processing
🔷 Techniques to optimize API performance while respecting limitations
🔷 Differences between using driver nodes and worker nodes for parallel tasks
Key Takeaways:
✨ Reduce processing time by running API calls simultaneously
✨ Handle API errors effectively to maintain robust pipelines
✨ Understand the limitations of driver nodes for parallel processing
✨ Future-proof your pipelines by learning scalable parallelization techniques
Related Hashtags:
#MicrosoftFabric #ParallelProcessing #DataEngineering #APIIntegration #PythonNotebooks #ConcurrentFutures #AzureTutorial

[@ssrakeshsharma]

I just loved it, Nice Explanation - Channel Subscribed

[@AleksiPartanenTech]

Glad you loved it! Thank you!

[@bhaskar9781]

❤ Thanks ❤

[@AleksiPartanenTech]

You're Welcome! :)

[@yuvarajand19]

Thank you for the video.
From a CU consumption perspective, would you recommend parallelization using spark notebook or python notebook?
Will be interested to see some videos on CU comparisons.

[@AleksiPartanenTech]

You're welcome! :)
I haven't done CU consumption comparison that much but based on what I know and have read online, I would say that parallelizing tasks that don't require spark like these API calls it is better to use python notebooks if you want to optimize the capacity consumption. The reason is that python notebooks run on a smaller computer that PySpark notebooks and if you are not using spark is just some unnecessary overhead. Also, keep in mind that python notebooks are very new and they have some limitations that pyspark notebooks don't have.