The Modern Data Stack Is Over. Here's What's Next.

For nearly a decade, the Modern Data Stack, characterized by tools like Snowflake, DBT, and FiveTran, defined how data teams approached analytics, focusing on standardizing fragmented systems, but its relevance has waned as conditions evolved. Michael Driscoll and Matthaus Krzykowski, in a conversation on Data Talks on the Rocks, explore how the maintenance burden of these systems, with constant changes to APIs and internal services, has become unsustainable as companies produce vast data sources. The new era sees a shift toward Python-driven environments, with AI-powered coding tools generating code, emphasizing adaptability over rigid structures. This shift is marked by a move from SQL-centric to Python-first approaches, favoring file formats like Parquet and CSV stored in object storage solutions such as S3, GCS, and Azure Blob. dltHub's dlt library exemplifies this transition by providing a stable foundation for AI-generated data pipelines, challenging traditional business models that relied on stable connectors. The conversation highlights a move from control-focused, standardized tools to adaptive systems that accommodate the growing complexity and volume of data, signaling a fundamental change in data engineering practices.