Grafana and Splunk are both used as monitoring tools. But while Grafana is majorly used as a data visualization tool, Splunk is an enterprise security and observability platform. Monitoring tools are essential for any business that wants to have visibility into its IT infrastructure. They provide real-time data that can be used to identify and troubleshoot problems. Grafana and Splunk are two of the most popular monitoring tools on the market.
So, which one is better for your business? In this article, we’ll compare Grafana and Splunk side by side, looking at features, pricing, supported data sources, and more. By the end, you should have a good idea of which tool is a better fit for your business.
What is Grafana?
Grafana is a robust, open-source data visualization and monitoring tool that allows users to create interactive dashboards and charts. It is used to monitor and visualize data from a variety of data sources — including time series databases such as QuestDB or InfluxDB. Grafana's real-time data display capabilities have made it the choice among many people for monitoring systems and infrastructure.
- It gives users the ability to identify trends and patterns in their data, which can be used to create alerts or notifications based on specific conditions and thresholds.
- It is particularly popular among DevOps and SRE teams, who use it to quickly identify issues in their systems and infrastructure.
- In addition to its versatile data-monitoring capabilities, Grafana also provides an API that allows users to create and manage dashboards.
- Allows organizations to monitor large amounts of time-series data in real-time
What is Splunk?
Splunk is a log analysis platform that collects, indexes, and analyzes data from various sources, including messaging systems, databases, network devices, and more. It doesn't matter where your data is coming from; Splunk can normalize and extract fields from it automatically, which makes it a great tool for troubleshooting and identifying patterns in data.
Three key components in Splunk are its forwarder, indexer, and search head.
The forwarder pushes data to a remote indexer. The indexer manages all the indexing and search queries. The search head is the front-end web interface where these 3 components can be combined.
With Splunk, you can search and analyze large amounts of data in real-time, and it provides pre-built visualizations to help you quickly create informative dashboards.
Grafana vs Splunk: At a glance
|At a Glance||Splunk||Grafana|
|Software type||Closed Source (Commercial Software)||Open source, free to use and modify. Also offers a paid version.|
|Data Collection||Can collect data from different channels, including file-based, script-based, network-based, and API||Can collect data from a variety of sources, including QuestDB, Elastic Search, Prometheus|
|Data Visualisation||Pre-built visualization||Interactive dashboard and charts|
|Alerting & Notification||Robots alert management system. SMS, Email, etc can be used and channeled.||The alert manager is built-in for real-time monitoring & alerts using slack, Email, etc|
|Pricing||Splunk is a costly tool used by enterprises.||Free to use open source software|
|Scalability||Distributed Architecture is highly scalable||Plug-in Architecture can be scaled by adding more servers|
|Learning Curve||Steep learning curve for new users||An active community of users|
|Query Language||Search Processing Language (SPL).||SQL-like query language.|
Grafana vs Splunk: Key Features comparison
Data Collection & Ingestion:
While Splunk and Grafana are both data collection and analysis tools, they have different features and approaches in regard to data collection & ingestion.
Splunk is a centralized log management tool that can collect and store large amounts of data from various sources. This data is stored in an index, where it can be analyzed using Splunk's powerful search language.
As per the official documents of Splunk, data collection depends on what type of data source a client is using. These are the methods that Splunk uses for data collection.
- Ingest service which collects JSON objects from
/metricsendpoint of the ingest REST API.
- Forwarder service, which collects data from the Splunk forwarder.
- DSP HTTP event collector which collects data from HTTP clients and Syslog data sources.
- DSP Collect connectors that collect data from several types of data sources such as Amazon S3, Amazon CloudWatch, Azure, etc. These collectors collect data through jobs that run on a schedule.
- Steaming connectors collect data from several data sources such as Apache Kafka, Apache Pulsar, Google Cloud Pub/Sub, etc. These types of connectors receive data continuously emitted by the sources.
Grafana, meanwhile, is an open-source platform for data visualization and monitoring. It focuses on providing graphical representations of metrics. It can connect to various data sources, including InfluxDB, Prometheus, Elasticsearch, etc., and retrieve data using SQL-like query language.
In terms of data collection, Splunk has more options and is designed to be a central repository for all data, while Grafana collects information from specific sources in order to display it in a graphical format.
Both Kibana and Grafana offer a great set of visualization capabilities.
In Grafana, you need to set up these dashboards and panels, which requires some bandwidth depending on which metrics you wish to visualize. You can build a powerful dashboard by selecting a data source and then combining panels associated with different data sources together.
Splunk provides a dashboard studio that lets you customize dashboards in Splunk with control over dashboard’s layout, images, colors, and more.
Alerting and Notifications:
Splunk provides robust alerting capabilities, so alerts can be triggered based on conditions and thresholds. Alerts are sent through different channels, such as Slack, PagerDuty, ServiceNow, SMS, and email — and the built-in alert manager allows users to manage & track multiple sources of alerts in one place.
Grafana has a built-in alert manager for real-time monitoring and alerts. Alerts can be configured based on conditions and thresholds and can be sent through various notification channels such as email, Slack, etc. Grafana enables users to create alerting rules at a dashboard panel level, making it easy to set up and manage alerts for specific data sets.
Both Splunk and Grafana provide robust capabilities, but Splunk provides more advanced alert management and tracking, while Grafana provides more granular control over alerting rules at the panel level.
Pros and Cons
- Data Safety: Splunk keeps multiple copies of indexed data providing better data safety in case of technical failures.
- Advanced Features: Splunk provides advanced analytics and can be used to process large data sets.
- Security: Since Splunk is commercial software, the security features, including data encryption, authentication, and access control, are very robust compared to what Grafana offers.
- Expensive: Splunk is commercial enterprise-grade software, and it can be expensive for many organizations to implement.
- Steep learning curve: Although Splunk is a powerful tool, the learning curve for it can be very steep, and new users struggle with it.
- Open Source: Grafana is open source project, which means it can be used
- GNU Licensing: Organizations don't need any type of license to run Grafana which makes it a cost-effective monitoring option.
- Engagement: Grafana has an active community of users and developers who talk about their experiences and help each other.
- Limited Storage: Grafana on other hand has limited data storage capacity which can be a major issue for organizations that have large data sets.
Now coming to the most important point, SCALABILITY!
Both of these monitoring solutions have different approaches when it comes to scalability:
Splunk is built on top of distributed architecture which means it can process large amounts of data and index it on different nodes making it highly scalable.
In terms of storage, Splunk is built in a way that it can easily handle petabytes of data which can also scale horizontally by adding more nodes to the cluster.
Grafana, on the other hand, is built on plug-in architecture which allows it to integrate with different types of data sources and its functionality can be extended by using plugins.
The plug-in architecture in Grafana can be scaled by adding more servers.
In general, Splunk is comparatively more scalable than Grafana especially when it comes to organizations that have large amounts of data.
Choosing between Grafana and Splunk
Both Splunk and Grafana are powerful monitoring solutions with their own strengths & features.
Splunk has a powerful search engine that enables advanced data processing and analytics with robust security and alert management. Splunk is in general more scalable but has a cost associated with it, along with a steep learning curve.
Grafana is an open-source data visualization & monitoring tool that works best for real-time monitoring. Grafana is cost-effective, user-friendly, and flexible but lacks data storage capabilities also it doesn't offer as many features or provide the same level of security when compared to Splunk.
SigNoz - an alternative to Grafana and Splunk
SigNoz is an open source APM that provides metrics, logs, and traces under a single pane of glass. It uses OpenTelemetry for application instrumentation. OpenTelemetry is quietly becoming the world standard for instrumenting cloud-native applications. SigNoz can be a great alternative to Grafana and Splunk.
It is easy to get started with SigNoz with out of box charts for key application metrics. SigNoz also provides an enterprise version that can be self-hosted within your infra.
Getting Started with SigNoz
SigNoz can be installed on macOS or Linux computers in just three steps by using a simple install script.
The install script automatically installs Docker Engine on Linux. However, on macOS, you must manually install Docker Engine before running the install script.
git clone -b main https://github.com/SigNoz/signoz.git
You can visit our documentation for instructions on how to install SigNoz using Docker Swarm and Helm Charts.
If you liked what you read, then check out our GitHub repo 👇