Operational and analytical use cases struggle to access complete and trustworthy data, leading to the development of fragmented and inefficient solutions. Traditional ETL and multi-hop architectures fail to address the needs of modern businesses, resulting in duplicated efforts and excess costs. The shift left approach proposes moving data processing upstream, increasing accessibility for various teams. Data products play a critical role in this strategy, enabling reliable data communication. Furthermore, data contracts create stable APIs that delineate data domains and ensure the integrity and evolvability of data, allowing better management of internal and external data models.
Operational and analytical use cases face significant challenges accessing trustworthy data; a shift left approach is essential for providing relevant and complete data.
Rethinking ETL patterns and multi-hop architectures is vital, as these current methods create inefficiencies and raise costs due to their fragmented nature.
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