Dev tools · September 27, 2023
Rust based log analytics with Parquet on S3
For write once, read many, immutable data types, Parquet on S3 is one of the most economical and scale approach. Every organisation I have talked to, in the last few years has attempted some form of Parquet on S3 implementation. However, these initiatives face challenges in standardising the approach for both ingesting and querying the data. Data quality, data governance and access control - everything becomes a challenge. We at Parseable are taking the Parquet on S3 approach and making it into an Enterprise grade product. Integrate easily with existing log agents and libraries. Built-in…
In plain words
Parseable is a Rust-based log analytics platform that stores data in Parquet format on S3 for cost-effective, scalable log management. It integrates with existing log agents and libraries while providing enterprise features including role-based access control, OIDC integration, SQL querying, and a built-in console for data analysis. The platform addresses standardization challenges around data ingestion, quality, governance, and access control that organizations typically face with Parquet-on-S3 implementations.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
For write once, read many, immutable data types, Parquet on S3 is one of the most economical and scale approach. Every organisation I have talked to, in the last few years has attempted some form of Parquet on S3 implementation. However, these initiatives face challenges in standardising the approach for both ingesting and querying the data. Data quality, data governance and access control - everything becomes a challenge. We at Parseable are taking the Parquet on S3 approach and making it into an Enterprise grade product. Integrate easily with existing log agents and libraries. Built-in RBAC and user management. OIDC integration. Seamless querying by SQL. Built-in console to query and analyse this data. And much more. Please take a look!
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