PyIceberg Now Supports Upsert: Simplify Data Management Without Spark!
PyIceberg just got a whole lot more powerful! Version 0.9.0 introduces native upsert functionality, allowing you to merge data directly into your Iceberg tables without the need for Spark or other external processing engines. This significantly simplifies data management workflows, especially when dealing with incremental updates and changes.
Video
Getting Started
Before you dive into the code, make sure you have the latest version of PyIceberg installed:
Example: Managing Site Messages
Let's walk through a practical example of how to use the new upsert functionality. Imagine we are managing site messages, where each message is associated with a site and has a unique ID. We want to be able to update existing messages and add new ones as needed
Initial Data
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Benefits
PyIceberg's new upsert functionality opens up a world of possibilities for managing your data lakes. Embrace this powerful feature and simplify your data workflows today!
References
Jawaharlal Nehru Technology University, Anathapur
1 周Very informative
Mathematician | Data Engineer @ Electronic Arts (EA)
1 周very great news!
Azure Data Engineer | Big Data Engineer | Databricks | Python | Spark | DP-203
1 周Great news
Full Digitalized Chief Operation Officer (FDO COO) | First cohort within "Coca-Cola Founders" - the 1st Corporate Venture funds in the world operated at global scale.
1 周Very informative
Data Engineer | Editor Data Engineering Weekly | Angel Investor| Advisor for early stage data startups| Let's chat about data engineering | Book me here calendly.com/apackkildurai
1 周The implementation is good (PyIceberg implementation of upsert). The code does the partition pruning. There is an explicit ask for the partition key, instead of only the primary key. I wonder how it will figure out the partition pruning.