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  1. Stackups
  2. DevOps
  3. Monitoring
  4. Monitoring Aggregation
  5. Bigpanda vs Splunk

Bigpanda vs Splunk

OverviewComparisonAlternatives

Overview

Bigpanda
Bigpanda
Stacks21
Followers55
Votes16
Splunk
Splunk
Stacks772
Followers1.0K
Votes20

Bigpanda vs Splunk: What are the differences?

  1. Integration with Third-Party Tools: One key difference between Bigpanda and Splunk is their integration capabilities with third-party tools. While Bigpanda provides seamless integration with popular monitoring and incident response tools like PagerDuty, Slack, and ServiceNow, Splunk offers its own ecosystem of apps and connectors for integration purposes. This means that Bigpanda allows for a more straightforward integration with existing toolsets, while Splunk provides a more comprehensive in-house solution for various needs.

  2. Data Collection and Parsing: Bigpanda and Splunk differ in their approach to data collection and parsing. Bigpanda focuses on collecting and parsing data from IT monitoring tools, such as Nagios, Zabbix, and New Relic, to create a consolidated and structured view of incidents. On the other hand, Splunk is designed as a versatile data analytics platform, capable of collecting and parsing data from a wide range of sources, including network logs, system logs, and security logs. This means that Bigpanda is more specialized in IT incident management, while Splunk has a broader scope for data analysis.

  3. Event Correlation and Noise Reduction: Bigpanda places a strong emphasis on event correlation and noise reduction. It uses advanced algorithms to analyze and correlate events, reducing noise and grouping similar events together to provide a concise overview of incidents. Splunk, on the other hand, provides powerful search and filtering capabilities, allowing users to manually search, filter, and analyze events based on customized criteria. This means that Bigpanda automates and streamlines event correlation, while Splunk offers more flexibility for manual investigation and analysis.

  4. Deployment and Scalability: When it comes to deployment and scalability, Bigpanda and Splunk have different characteristics. Bigpanda is a cloud-based platform that offers easy deployment and scalability, as it leverages cloud infrastructure to handle large volumes of data and provide high availability. On the other hand, Splunk can be deployed both on-premises and in the cloud, offering more flexibility in terms of deployment options. Splunk also provides enterprise-grade scalability, allowing organizations to scale their data ingestion and analytics capabilities as their needs grow.

  5. User Interface and User Experience: Bigpanda and Splunk differ in their user interface and user experience. Bigpanda offers a simple and intuitive interface that is focused on incident management, providing users with a clear overview of incidents and actionable insights. Splunk, on the other hand, has a more complex and feature-rich interface, designed to support various data analysis and visualization needs. Splunk provides a wide range of customizable dashboards, visualizations, and reports, allowing users to explore and analyze data in a highly flexible manner.

  6. Cost and Licensing Model: The cost and licensing models of Bigpanda and Splunk also differ. Bigpanda offers a subscription-based pricing model, where organizations pay based on the number of monitored systems and the desired features. Splunk, on the other hand, offers a more complex pricing structure, with options for perpetual licenses, term licenses, and cloud-based licensing. Splunk's pricing is based on data ingestion volume and the number of users accessing the platform. This means that organizations need to carefully consider their specific needs and usage patterns to determine the most cost-effective option.

In Summary, Bigpanda and Splunk differ in their integration capabilities, data collection approach, event correlation methods, deployment and scalability options, user interface and experience, as well as cost and licensing models.

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Detailed Comparison

Bigpanda
Bigpanda
Splunk
Splunk

Bigpanda helps you manage and respond to ops incidents faster. All your alerts: organized, assignable, trackable, snoozeable, and updated in real-time.

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

Issue tracking for ops;Reduce noisy alerts;Easy collaboration
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
21
Stacks
772
Followers
55
Followers
1.0K
Votes
16
Votes
20
Pros & Cons
Pros
  • 7
    User interface, easy setup, analytics, integrations
  • 6
    Consolidates many systems into one
  • 2
    Correlation engine
  • 1
    Quick setup
Pros
  • 3
    API for searching logs, running reports
  • 3
    Alert system based on custom query results
  • 2
    Ability to style search results into reports
  • 2
    Custom log parsing as well as automatic parsing
  • 2
    Dashboarding on any log contents
Cons
  • 1
    Splunk query language rich so lots to learn
Integrations
Nagios
Nagios
PagerDuty
PagerDuty
New Relic
New Relic
Amazon CloudWatch
Amazon CloudWatch
Puppet Labs
Puppet Labs
Pingdom
Pingdom
Chef
Chef
Capistrano
Capistrano
Jenkins
Jenkins
Ansible
Ansible
No integrations available

What are some alternatives to Bigpanda, Splunk?

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.

PagerDuty

PagerDuty

PagerDuty is an alarm aggregation and dispatching service for system administrators and support teams. It collects alerts from your monitoring tools, gives you an overall view of all of your monitoring alarms, and alerts an on duty engineer if there's a problem.

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.

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