In this demo, Joe Swanson, Co-founder and Lead Developer at DataForge, guides viewers through building a BI data model using the Coalesce ETL platform. He explains key stages of the process, such as defining data types, grouping customer data, and unpivoting item data for better reporting. Joe discusses crucial decision points, like when to use typed staging tables, group stages, or CTEs to optimize data transformations. He concludes by hinting at Part 2, where he will show how DataForge simplifies and automates these steps, making data modeling more efficient and reusable.

DataForge introduces Stream Processing, enabling seamless integration of real-time and batch data for dynamic, scalable pipelines. Leveraging Lambda Architecture, users can enrich streaming data with historical insights, facilitating comprehensive real-time analytics. Key features include Kafka integration, batch enrichment, and downstream processing. This advancement simplifies real-time data management, enhances analytics capabilities, and accelerates AI/ML applications, all within a fully managed, automated platform.