Big Data Architecture refers to the framework and components designed to handle the ingestion, processing, storage, and analysis of data sets too large or complex for traditional data processing applications, characterized by addressing volume, velocity, variety, and veracity challenges.

For enterprise architects, Big Data architecture has evolved substantially beyond early Hadoop-centric implementations toward more sophisticated, hybrid designs. Modern Big Data architectures typically incorporate logical layers including data sources, ingestion, storage, processing, analysis, and presentation components. The Lambda architecture pattern (combining batch and stream processing) and its evolution, the Kappa architecture (unifying processing models), have become standard approaches for handling mixed workloads. Data lakes have emerged as central repositories but are increasingly complemented by purpose-built data stores and processing systems for specific use cases. Enterprise architects must navigate trade-offs between data consistency, availability, and partition tolerance (the CAP theorem) while designing systems that maintain data lineage, governance, and compliance. The integration of Big Data architectures with enterprise data strategies requires careful consideration of data ownership, quality management, and master data harmonization. As organizations move toward real-time analytics and operational intelligence, architects should design for reduced data movement, employing techniques like data virtualization and federated queries where appropriate. The architecture should also accommodate emerging AI/ML workflows, providing the infrastructure for model training, deployment, and monitoring, while ensuring appropriate security controls are maintained throughout the data lifecycle.