What Is the Architecture of Automated Data Integration?

The architecture of automated data integration is based on several key concepts, including extracting, loading, and transforming data with minimal human intervention. Automated data integration is closely tied to the growth of cloud computing, which has led to cheaper storage and processing costs, enabling a profusion of cloud-based services. Fivetran's modern data stack includes components such as data sources, automated data pipelines, data warehouses or lakes, transformation layers, and business intelligence or data science platforms. Design considerations for an automated data pipeline include ELT vs. ETL, incremental updates, idempotence, normalized schemas, schema migrations, net-additive data integration, and data type detection and coercion. Other important factors to consider are reliability, scalability, and security.