Data engineering is increasingly moving toward treating data as a product rather than a byproduct of processes. In this paradigm, data teams build reusable, discoverable, and trustworthy data assets that serve multiple teams across the organization.
Modern data engineering platforms focus on data observability, lineage tracking, and quality automation, ensuring that data consumers—including analysts, data scientists, and business leaders—can easily access and trust the data. The emergence of tools like dbt, Monte Carlo, and open-source metadata repositories has accelerated this trend, allowing organizations to unify their data strategy and reduce silos.
