To scale marketing attribution, developers must establish robust, automated data ingestion pipelines from diverse sources like ad platforms, CRMs, and web analytics. This involves implementing ETL/ELT processes to unify and normalize customer journey data, ensuring consistent identifiers and event schemas across all touchpoints. Leveraging cloud-native data warehousing (e.g., Snowflake, BigQuery) and processing frameworks (e.g., Apache Spark) allows for efficient, large-scale computation of attribution models, whether rule-based or algorithmic. Furthermore, incorporating data governance and quality checks throughout the pipeline is crucial to maintain accuracy and reliability of attribution insights. Automating the entire pipeline, from data capture to model application and output to analytics dashboards, ensures timely and actionable insights without manual intervention. Building a modular architecture enables easy integration of new data sources or the experimentation with advanced attribution algorithms like machine learning. More details: https://sportfort.ru/AHL/Sites/SwitchView?mobile=true&returnUrl=https://infoguide.com.ua