Open Variant Data Type in Delta Lake and Apache Spark

Key Takeaways

hello I'm s Shukla and in this demo I'll show you how variant data type in data brakes delivers flexibility and efficiency for handling semi-structured data some key features of variant data type are number one flexibility variant allows for ingestion and quering of semi-structured data without the need for predefined schemas this is beneficial for scenarios uh where the data schema is dynamic and evolves over time performance variant offers an order of magnitude performance improvement over Json strings for both nested and flat schemas third ease of use datab brick provides several functions to work with variant data such as pass Json to convert Json strings to variant and number four integration variant is supported in data bricks runtime 15.3 and above it's open sourced and integrated with Apache spark and Delta Lake ensuring no no proprietary data locking in this Demo First we create a table with a variant column and insert some Json data notice how past Json converts our Json strings into variant type let's explore different ways to query variant data we can access Fields using colon notation for top level fields notation for nested Fields array elements are accessed using square brackets there are a number of functions for supporting variant types let's look at a few of them we can inspect schema using schema of variant flatten arrays using variant explored function and perform type safe Vari operations that's how simple and Powerful the variant data type is in datab Brak it provides schema flexibility while maintaining performance and ease of use