Visualizing Dremio Workloads Using Preset Cloud

Dremio is a widely used lakehouse platform that integrates the benefits of data warehouses and data lakes, and when paired with Apache Superset, it enables the creation of robust data visualizations. As organizations increasingly use Dremio, it becomes critical to monitor and understand the workloads executed on the platform, which can inform users about potential failures, user challenges, and cost reductions in platform ownership. Key data sources like queries.json and query profiles provide insights into queries processed by Dremio, although queries.json lacks detailed execution information found in query profiles accessible via the Job UI. To facilitate querying, the queries.json files and query profiles must be stored in distributed storage accessible by all executors, such as S3 or HDFS, and may require some transformation. Visualizing these workloads in platforms like Preset Cloud offers insights into query distribution and performance, revealing important patterns like protocol usage, query acceleration issues, and resource contention, which can be analyzed further for optimizing system performance and ensuring compliance with service-level agreements. The article also notes the importance of proper workload analytics for maintaining a stable and reliable data platform and hints at future discussions on Dremio Monitoring.

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