Alternatives
Products that do what Rust, Apache Arrow, Parquet based cloud native log storage platform does
- 1VA
2019 · github.com
- 2LE
2016 · logdna.com
- 3HA
2016 · github.com
- 4SA
2019 · github.com
- 5IA
2014 · github.com
- 6VQ
2020 · github.com
- 7SA
2018 · github.com
- 8RS
2021 · github.com
- 9LR
2024 · docs.rs
- 10LS
2019 · github.com
- 11TS
2017 · timber.io
- 12AE
2020 · github.com
- 13DA
2013 · github.com
- 14AW
2018 · github.com
- 15JA
2020 · crates.io
- 16HL
2019 · github.com
- 17HP
2020 · crates.io
- 18AB
2021 · github.com
- 19NR
2021 · gitlab.com
- 20JA
2018 · github.com
- 21CS
Founder here, Parseable is a lightweight log ingestion and query engine written in Rust. Parseable can ingest data from existing logging agents (FluentBit, LogStash, Vector, syslog-ng and more) using HTTP + JSON output. Ingested logs are stored as semi-indexed Parquet files (on disk or S3). You can query the data with builtin query engine using SQL or use a query engines of choice like Spark, Presto, Trino and so on. We also developed a Grafana data source plugin that lets you visualise log data via Grafana. Sample dashboard link in readme. As log data grew, our industry has responded with…
2023 · github.com
- 22UL
2019 · github.com
- 23BA
2014 · github.com
- 24RB
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…
2023 · github.com
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