SCD2 Deep Dive with dlt: How nested data affects queries and costs
Blog post from dltHub
Aman Gupta's blog post explores the complexities of managing Slowly Changing Dimensions Type 2 (SCD2) in nested data structures within data warehouses, focusing on the use of the dlt library to automate this process. SCD2 allows for the tracking of historical data by inserting new records instead of overwriting existing ones, and dlt simplifies this by managing SQL generation and versioning. The article demonstrates how dlt handles nested JSON records, generates SQL for maintaining historical changes, and evaluates the cost implications of different SCD2 strategies using BigQuery. Through practical examples and benchmarks, it highlights how incremental extraction is more cost-effective than non-incremental methods, and discusses the impact of varying nesting depths on query costs. The blog encourages readers to experiment with these concepts using an interactive Colab notebook and provides resources for further learning.
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