2. The Data Lifecycle Demands a Product Mindset

One of the core insights of the book is treating data as a product, not a byproduct. This means building data pipelines that are observable, maintainable, and trustworthy. The modern data lifecycle spans ingestion, transformation, storage, serving, and observability. Engineers must consider versioning, schema evolution, data SLAs, and downstream impacts. Applying a product mindset includes building pipelines with tests, documentation, ownership models, and service-level expectations. Data engineers are no longer just coders — they are stewards of data products that power analytics, ML