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  1. Stackups
  2. DevOps
  3. Log Management
  4. Log Management
  5. Stroom vs logagent

Stroom vs logagent

OverviewComparisonAlternatives

Overview

Logagent
Logagent
Stacks4
Followers5
Votes0
GitHub Stars390
Forks78
Stroom
Stroom
Stacks1
Followers3
Votes0
GitHub Stars452
Forks62

Stroom vs logagent: What are the differences?

What is Stroom? A scalable data storage, processing and analysis platform. It is a data processing, storage and analysis platform. It is scalable - just add more CPUs / servers for greater throughput. It is suitable for processing high volume data such as system logs, to provide valuable insights into IT performance and usage.

What is logagent? Lightweight open-source, cloud-native data shipper for Server, Container, and Application Logs. is a modern, open-source, light-weight data shipper with out of the box and extensible log parsing, on-disk buffering, secure transport and bulk indexing to Elasticsearch and Sematext Cloud. Its low memory footprint and low CPU overhead makes it suitable for deploying on edge nodes and devices, while its ability to parse and structure logs makes it a great Logstash alternative.

Stroom belongs to "Big Data Tools" category of the tech stack, while logagent can be primarily classified under "Log Management".

Stroom and logagent are both open source tools. Stroom with 329 GitHub stars and 35 forks on GitHub appears to be more popular than logagent with 308 GitHub stars and 68 GitHub forks.

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Detailed Comparison

Logagent
Logagent
Stroom
Stroom

Open-source, light-weight data shipper with out of the box and extensible log parsing, on-disk buffering, secure transport and bulk indexing.

It is a data processing, storage and analysis platform. It is scalable - just add more CPUs / servers for greater throughput. It is suitable for processing high volume data such as system logs, to provide valuable insights into IT performance and usage.

Open source - Apache Licence; Log parsing; On-disk buffering; Bulk indexing; Low memory overhead; Elasticsearch integration; Log routing; Container/Docker/Kubernetes integration; Log structure parsing; Log enrichment; Log rotation; Secure and reliable data transfer; Masking sensitive data; Syslog integration; Heroku integration; CloudFoundry integration; Filtering and aggregation; Two-way SSL auth
Receive and store large volumes of data such as native format logs. Ingested data is always available in its raw form; Create sequences of XSL and text operations, in order to normalise or export data in any format. It is possible to enrich data using lookups and reference data; Easily add new data formats and debug the transformations if they don't work as expected; Create multiple indexes with different retention periods. These can be sharded across your cluster; Run queries against your indexes or statistics and view the results within custom visualisations; Record counts or values of items over time
Statistics
GitHub Stars
390
GitHub Stars
452
GitHub Forks
78
GitHub Forks
62
Stacks
4
Stacks
1
Followers
5
Followers
3
Votes
0
Votes
0
Integrations
GitHub
GitHub
Node.js
Node.js
Docker
Docker
Kubernetes
Kubernetes
JavaScript
JavaScript
Git
Git
NGINX
NGINX
MariaDB
MariaDB
MySQL
MySQL
IntelliJ IDEA
IntelliJ IDEA

What are some alternatives to Logagent, Stroom?

Papertrail

Papertrail

Papertrail helps detect, resolve, and avoid infrastructure problems using log messages. Papertrail's practicality comes from our own experience as sysadmins, developers, and entrepreneurs.

Logmatic

Logmatic

Get a clear overview of what is happening across your distributed environments, and spot the needle in the haystack in no time. Build dynamic analyses and identify improvements for your software, your user experience and your business.

Loggly

Loggly

It is a SaaS solution to manage your log data. There is nothing to install and updates are automatically applied to your Loggly subdomain.

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Logentries

Logentries

Logentries makes machine-generated log data easily accessible to IT operations, development, and business analysis teams of all sizes. With the broadest platform support and an open API, Logentries brings the value of log-level data to any system, to any team member, and to a community of more than 25,000 worldwide users.

Logstash

Logstash

Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana.

Graylog

Graylog

Centralize and aggregate all your log files for 100% visibility. Use our powerful query language to search through terabytes of log data to discover and analyze important information.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Sematext

Sematext

Sematext pulls together performance monitoring, logs, user experience and synthetic monitoring that tools organizations need to troubleshoot performance issues faster.

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