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现代数据堆栈:面临哪些挑战?
3 6 Ke· 2025-08-25 02:22
Core Insights - The modern data stack is increasingly popular in data-driven enterprises, driven by cloud-native tools that support AI, machine learning, and advanced analytics, promising scalability, modularity, and speed [1] - However, the adoption of this stack has led to increased complexity and fragmentation, creating new "silos" within organizations as teams utilize multiple tools for different data functions [1][5] - The challenges faced by the modern data stack can significantly impact return on investment, as the complexity and operational overhead increase with the integration of various tools [26][28] Group 1: Challenges of the Modern Data Stack - Tool fragmentation is a pressing challenge, leading to a bloated ecosystem where tools lack the necessary interoperability, increasing complexity and diverting focus from solving business pain points [5][7] - Operational complexity arises from the need for dedicated monitoring and expertise for each tool, pushing data teams to their limits and increasing operational overhead [8][28] - Data quality and trust issues stem from inconsistent validation standards and unclear data ownership, leading to a lack of confidence in data quality and reliance on manual processes [9][11] Group 2: Metadata and Ownership Issues - Metadata management is underdeveloped, leading to outdated or fragmented metadata that diminishes the value of data, resulting in wasted resources on "dark data" [12][20] - The lack of clear ownership within the modern data stack creates confusion and weakens accountability, impacting effective data governance and policy enforcement [22] - Compliance, security, and access control gaps are evident, with many organizations unprepared to handle emerging vulnerabilities, leading to risks in data governance [23] Group 3: Future Directions - A "data-first" approach is emerging, focusing on the data lifecycle, accessibility, and value rather than merely unifying data through various technologies [30] - The Data Developer Platform (DDP) is a key element in this transition, enabling teams to efficiently create, manage, and scale data products without needing specific infrastructure knowledge [30][34] - The integration of DDP can lead to significant improvements in operational simplicity and governance, ensuring compliance and trust throughout the data lifecycle [34]