The modern lakehouse architecture utilizes a medallion architecture to organize data in bronze (raw data), silver (validated and conformed), and gold (business-level aggregates) layers. This tiered approach ensures lineage, performance optimization through cached aggregates, and data quality control at every stage, balancing performance with cost efficiency and supporting operational and analytical needs.
Effective data integration is crucial, enabling a continuous lebanon rcs data data flow between systems with batch and real-time stream processing. Batch processing remains essential for high-volume data, frequently using tools to manage large-scale datasets with speed and efficiency. Real-time streaming capabilities are achieved through technologies like Apache Kafka and Flink, while change data capture (CDC) maintains data consistency, ensuring that decision-making is based on the latest information available.
Data engineering plays a central role in data platform architecture, with pipelines designed to support diverse processing requirements while ensuring reliability. Typical pipeline elements include source connectors, transformation engines, quality controls, and monitoring systems. Orchestration tools automate workflows, providing error handling and data movement management that help maintain platform integrity.