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Seq vs Splunk: What are the differences?

Key Differences between Seq and Splunk

Seq and Splunk are both popular log management systems used for analyzing and monitoring log data. However, they have key differences that set them apart.

  1. Query Language: The query languages used by Seq and Splunk differ significantly. Seq uses a query language called SeqQL, which is based on SQL syntax and allows for powerful log filtering and aggregation. On the other hand, Splunk uses a search processing language called SPL, which is specific to Splunk and relies on complex operators and functions for log analysis.

  2. Data Storage: Seq and Splunk use different approaches for storing log data. Seq leverages the Elasticsearch search engine for storing and indexing logs, providing fast and efficient search capabilities. In contrast, Splunk uses its proprietary indexing technology, which allows for efficient searching and correlation across large amounts of data.

  3. Scalability: When it comes to scalability, Seq and Splunk have different capabilities. Seq is designed to be horizontally scalable, meaning that multiple instances of Seq can be deployed to handle increasing amounts of log data. On the other hand, Splunk offers both horizontal and vertical scaling options, allowing the deployment of multiple Splunk instances as well as increasing the resources of a single instance.

  4. Pricing Model: Seq and Splunk have different pricing models. Seq offers a subscription-based pricing model that is based on the amount of log data ingested and the number of users. Splunk, on the other hand, offers a tiered pricing model based on the amount of data indexed per day as well as additional features and support options.

  5. Ease of Use: The user experience and interface of Seq and Splunk differ. Seq provides a simple and intuitive web interface that enables users to quickly search and analyze log data. Splunk, on the other hand, has a more advanced and feature-rich interface that may require a learning curve for new users.

  6. Integration Ecosystem: Both Seq and Splunk offer integration options with various platforms and technologies. However, Splunk has a wider range of integrations available, including popular systems and services used in enterprise environments.

In Summary, Seq and Splunk differ in their query languages, data storage approaches, scalability options, pricing models, ease of use, and integration ecosystems.

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Pros of Seq
Pros of Splunk
  • 5
    Easy to install and configure
  • 5
    Easy to use
  • 3
    Flexible query language
  • 2
    Free unlimited one-person version
  • 2
    Beautiful charts and dashboards
  • 2
    Extensive plug-ins and integrations
  • 3
    API for searching logs, running reports
  • 3
    Alert system based on custom query results
  • 2
    Splunk language supports string, date manip, math, etc
  • 2
    Dashboarding on any log contents
  • 2
    Custom log parsing as well as automatic parsing
  • 2
    Query engine supports joining, aggregation, stats, etc
  • 2
    Rich GUI for searching live logs
  • 2
    Ability to style search results into reports
  • 1
    Granular scheduling and time window support
  • 1
    Query any log as key-value pairs

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Cons of Seq
Cons of Splunk
  • 1
    This is a library tied to seq log storage
  • 1
    It is not free
  • 1
    Splunk query language rich so lots to learn

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What is Seq?

Seq is a self-hosted server for structured log search, analysis, and alerting. It can be hosted on Windows or Linux/Docker, and has integrations for most popular structured logging libraries.

What is Splunk?

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

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What companies use Seq?
What companies use Splunk?
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What tools integrate with Seq?
What tools integrate with Splunk?

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What are some alternatives to Seq and Splunk?
Elasticsearch
Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
ELK
It is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server‑side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch.
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.
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.
Kibana
Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch.
See all alternatives