The modern data stack: a guide

Over recent years, the proliferation of data tools has transformed the way organizations handle data, primarily driven by the capabilities of cloud data warehouses. These warehouses allow for the efficient and cost-effective storage and querying of large datasets, enabling organizations to create a unified, high-value data asset that drives business value. The modern data stack, composed of specialized tools across various categories like data ingestion, storage, processing, and analysis, offers a scalable framework that can be adopted by startups and enterprises alike. This ecosystem is characterized by its ability to democratize data access, facilitate real-time decision-making, and support advanced analytics through AI and ML tools. The rise of cloud data warehouses, notably marked by the success of platforms like Snowflake and Redshift, has made it feasible for organizations to establish a centralized data asset as a source of truth, decoupling data collection from visualization and enabling the ELT (Extract, Load, Transform) approach. Despite the excitement around these developments, the complexity and variety of tools available can make it challenging for organizations to build and evolve their data platforms effectively.