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  1. Stackups
  2. DevOps
  3. Log Management
  4. Log Management
  5. Papertrail vs Splunk

Papertrail vs Splunk

OverviewComparisonAlternatives

Overview

Papertrail
Papertrail
Stacks605
Followers378
Votes273
Splunk
Splunk
Stacks772
Followers1.0K
Votes20

Papertrail vs Splunk: What are the differences?

Introduction

This markdown document provides a comparison between Papertrail and Splunk, highlighting the key differences between the two log management and analysis tools.

  1. Scalability: Papertrail is ideal for small to medium-sized businesses, offering a cost-effective solution for log management. It has limitations in terms of scalability, as it is not designed to handle large volumes of data efficiently. On the other hand, Splunk is highly scalable, capable of handling massive amounts of log data, making it suitable for enterprise-level organizations with complex log analysis needs.

  2. Search Capabilities: Papertrail provides basic search functionality, allowing users to search for logs based on simple keyword queries. In contrast, Splunk offers advanced search capabilities, leveraging its robust search processing language (SPL) that enables complex searches, filtering, and correlation of logs. It provides users with the ability to create sophisticated queries and generate insightful reports.

  3. Data Retention: Papertrail has limited data retention capabilities, typically retaining logs for a predefined period, usually around a week or two. In contrast, Splunk offers extensive data retention options, allowing organizations to store log data for months or even years. This feature is particularly useful for compliance purposes and long-term analysis of historical logs.

  4. Alerting and Monitoring: While both Papertrail and Splunk offer alerting and monitoring features, there are differences in their capabilities. Papertrail provides basic alerting functionality, enabling users to set up alerts based on specific log events or search queries. Splunk, on the other hand, provides advanced alerting capabilities, allowing users to create complex alerts based on various conditions and thresholds. It also offers real-time monitoring capabilities for proactive log analysis.

  5. Integration Options: Papertrail integrates well with popular cloud platforms and services like AWS and Heroku, providing seamless log management for applications deployed on these platforms. However, its integration options are relatively limited compared to Splunk, which offers a wide range of integrations with various systems, technologies, and vendor-specific log sources. This extensive integration capability of Splunk allows organizations to centralize their logs from multiple sources for comprehensive analysis.

  6. User Interface and Customization: Papertrail offers a simple and intuitive user interface, making it easy for users to get started with log analysis. However, it lacks advanced customization options, limiting users from tailoring the interface to their specific needs. In contrast, Splunk provides a highly customizable user interface, allowing users to personalize their dashboards, reports, and visualizations. This flexibility enhances user experience and facilitates efficient log analysis.

In summary, Papertrail and Splunk differ in terms of scalability, search capabilities, data retention, alerting and monitoring capabilities, integration options, and user interface customization. Splunk is more suitable for large enterprises with complex log analysis requirements, whereas Papertrail is a cost-effective solution for smaller organizations with simpler log management needs.

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Detailed Comparison

Papertrail
Papertrail
Splunk
Splunk

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

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Intuitive Web-based log viewer;Powerful command-line tools;Long-term archive (S3);REST API;Team-wide groups and searches;Automated export for tables and charts;Search alerts;Easy SQL analytics (Hadoop);Unlimited systems and users;Encrypted logging
Predict and prevent problems with one unified monitoring experience; Streamline your entire security stack with Splunk as the nerve center; Detect, investigate and diagnose problems easily with end-to-end observability
Statistics
Stacks
605
Stacks
772
Followers
378
Followers
1.0K
Votes
273
Votes
20
Pros & Cons
Pros
  • 85
    Log search
  • 43
    Easy log aggregation across multiple machines
  • 43
    Integrates with Heroku
  • 37
    Simple interface
  • 26
    Backup to S3
Cons
  • 2
    Expensive
  • 1
    External Network Goes Down You Wont Be Logging
Pros
  • 3
    Alert system based on custom query results
  • 3
    API for searching logs, running reports
  • 2
    Ability to style search results into reports
  • 2
    Query engine supports joining, aggregation, stats, etc
  • 2
    Custom log parsing as well as automatic parsing
Cons
  • 1
    Splunk query language rich so lots to learn
Integrations
Slack
Slack
Heroku
Heroku
PagerDuty
PagerDuty
Amazon S3
Amazon S3
AWS Elastic Beanstalk
AWS Elastic Beanstalk
Amazon RDS
Amazon RDS
OpsGenie
OpsGenie
New Relic
New Relic
Librato
Librato
HipChat
HipChat
No integrations available

What are some alternatives to Papertrail, Splunk?

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.

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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