Selecting the right data warehouse stack is critical for manufacturing companies dealing with complex supply chain and production data. This case study outlines how one manufacturer evaluated options and settled on Apache Doris for its OLAP capabilities and DolphinScheduler for reliable workflow scheduling. Key factors included cost-effectiveness, scalability to handle large volumes of IoT and ERP data, and ease of integration with existing Hadoop and BI tools. The article also discusses challenges like data quality and team skill gaps, offering practical advice for others in similar situations. For data engineers and architects, this provides a real-world benchmark for comparing open-source data stack components. The insights are evergreen and applicable beyond manufacturing, making this a useful reference for any organization modernizing its data infrastructure.
A manufacturing company shares its data warehouse stack selection journey, choosing Apache Doris for OLAP and DolphinScheduler for workflow orchestration. The post provides real-world considerations like cost, performance, and integration with existing systems. This is valuable for data teams in similar industries evaluating modern data stack options.