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Ahana Announces New Presto Query Analyzer to Bring I****** Insights into Presto Clusters

Ahana, the only SaaS for Presto, announced a new tool for Presto users called the Presto Query Analyzer. With the Presto Query Analyzer, data platform teams can get i****** insights into their Presto clusters including query performance, bandwidth bottlenecks, and much more. The Presto Query Analyzer was built for the Presto community and is free to use.

Presto has become the SQL query engine of choice for the open data lakehouse. The open data lakehouse brings the reliability and performance of the data warehouse together with the flexibility and simplicity of the data lake, enabling data warehouse workloads to run alongside machine learning workloads. Presto on the open data lakehouse enables much better price performance as compared to expensive data warehousing solutions. As more companies are moving to an open data lakehouse approach with Presto as its engine, having more insights into query performance, workloads, resource consumption, and much more is critical.

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“We built the Presto Query Analyzer to help data platform teams get deeper insights into their Presto clusters, and we are thrilled to be making this tool freely available to the broader Presto community,” said Steven Mih, Cofounder & CEO, Ahana. “As we see the growth and adoption of Presto continue to skyrocket, our mission is to help Presto users get started and be successful with the open source project. The Presto Query Analyzer will help teams get even more out of their Presto usage, and we look forward to doing even more for the community in the upcoming months.”

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Key benefits of the Presto Query Analyzer include:

  • Understand query workloads: Break down queries by operators, CPU time, memory consumption, and bandwidth. Easily cross-reference queries for deep drill down.
  • Identify popular data: See which catalog, schema, tables, and columns are most and least frequently used and by who.
  • Monitor research consumption: Track CPU and memory utilization across the users in a cluster.

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