Lakehouses combine the best features of data lakes and data warehouses, offering scalable and cost-effective solutions for storing and processing large datasets. However, they come with a notable limitation: insert, update, and delete operations on tables are not natively supported. This poses a challenge for use cases requiring data synchronization or incremental updates. To overcome… Continue reading Overcoming Lakehouse Limitations: Implementing Upserts with PySpark
