The rise of cloud computing, real-time analytics, and artificial intelligence has catalyzed significant advances in modern data stack architectures, reshaping how organizations manage scalable data integration and drive business intelligence (BI) initiatives. This study systematically reviews the evolution, components, and emerging innovations within the modern data stack, with a particular focus on enabling scalability, flexibility, and actionable insights across industries. We analyze peer-reviewed literature, white papers, and industry case studies published between 2014 and 2024, guided by PRISMA methodology. Findings reveal that the modern data stack—comprising modular, cloud-native components such as ELT pipelines, data warehouses, transformation layers, orchestration tools, and BI platforms—has become a critical enabler for data-driven decision-making. Key advancements include the decoupling of storage and compute, the proliferation of scalable ELT (Extract, Load, Transform) frameworks, metadata-driven governance, data observability solutions, and the adoption of lakehouse architectures that blend traditional warehouse reliability with the flexibility of data lakes. Additionally, innovations such as reverse ETL, real-time data streaming, and AI-augmented analytics are enhancing the timeliness and precision of business insights. Nevertheless, challenges persist, including data silos, cost optimization, data quality assurance, and maintaining interoperability across heterogeneous systems. Our review highlights successful architectural patterns that balance scalability, performance, and maintainability, providing organizations with blueprints for optimizing their analytics ecosystems. Emerging trends point toward serverless data integration models, decentralized data ownership (Data Mesh), and the increasing convergence of operational and analytical workloads. Future research directions emphasize the need for frameworks that integrate ethical AI, automate metadata management, and ensure end-to-end data observability in complex, multi-cloud environments. As businesses increasingly depend on data as a core asset, advancing the modern data stack will remain crucial for sustaining competitive advantage, driving innovation, and enabling responsive, intelligent enterprises.
Eweje et al. (Fri,) studied this question.