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

Logsene vs Stroom

OverviewComparisonAlternatives

Overview

Sematext
Sematext
Stacks18
Followers31
Votes44
Stroom
Stroom
Stacks1
Followers3
Votes0
GitHub Stars452
Forks62

Logsene vs Stroom: What are the differences?

Developers describe Logsene as "ELK as a Service Log Management". Log Management SaaS, compatible with ELK stack. It exposes Elasticsearch API and Kibana, has compatibility with Grafana, powerful search, reporting, correlation with metrics, alerting, and anomaly detection. Also Available On Premises. On the other hand, Stroom is detailed as "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.

Logsene and Stroom are primarily classified as "Log Management" and "Big Data" tools respectively.

Some of the features offered by Logsene are:

  • Available in the Cloud and On Premises
  • Exposes Elasticsearch API
  • Includes Kibana

On the other hand, Stroom provides the following key features:

  • 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

Stroom is an open source tool with 294 GitHub stars and 32 GitHub forks. Here's a link to Stroom's open source repository on GitHub.

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Detailed Comparison

Sematext
Sematext
Stroom
Stroom

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

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.

Available in the Cloud and On Premises; Infrastructure Monitoring; Application Performance Monitoring; Log Management; Real User Monitoring; Synthetic Monitoring; Container/Docker/Kubernetes Monitoring; Server/VM Monitoring; Process Monitoring; Inventory Monitoring; Exposes Elasticsearch API; Includes Kibana; Can be used with Grafana; Ability to correlate logs, metrics, and events; Alerting, Anomaly Detection; Integrations with numerous ChatOps services; Ship data with Logstash, Syslog, Fluentd, Beats, Logagent, etc.; Multi-user and RBAC support; Powerful search and filter functionality; Custom tags
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
-
GitHub Stars
452
GitHub Forks
-
GitHub Forks
62
Stacks
18
Stacks
1
Followers
31
Followers
3
Votes
44
Votes
0
Pros & Cons
Pros
  • 5
    Centralized log management
  • 5
    ECS integration
  • 5
    Vercel integration
  • 5
    Easy to set up
  • 5
    Great filtering and grouping
No community feedback yet
Integrations
Fluentd
Fluentd
Cloud Foundry
Cloud Foundry
Cassandra
Cassandra
PagerDuty
PagerDuty
Solr
Solr
Apache Spark
Apache Spark
Azure Kubernetes Service
Azure Kubernetes Service
Ruby
Ruby
Elasticsearch
Elasticsearch
GitHub
GitHub
NGINX
NGINX
MariaDB
MariaDB
MySQL
MySQL
IntelliJ IDEA
IntelliJ IDEA

What are some alternatives to Sematext, 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.

Fluentd

Fluentd

Fluentd collects events from various data sources and writes them to files, RDBMS, NoSQL, IaaS, SaaS, Hadoop and so on. Fluentd helps you unify your logging infrastructure.

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