Splunk vs Elastic/ELK Stack: The Key Differences to Know
Splunk and the Elastic/ELK Stack are two of the most popular log management and observability platforms. They are both capable of collecting, parsing, and storing large amounts of data, and each one provides a wide range of features for searching, filtering, analyzing, and visualizing the collected data.
Despite being very similar tools, there are some key differences to note. Splunk is a proprietary tool that is generally considered to be more user-friendly and easier to get started with than the Elastic stack. It is geared towards enterprise customers so it has a broader range of features and functionality, including support for machine learning and other advanced analytics capabilities.
On the other hand, the Elastic Stack is a group of open-source products that are combined together to collect, search, and visualize machine data. It is also generally considered to be more suitable than Splunk for handling very large volumes of data. Due to its open-source nature, it has a larger community of users who contribute to the project making it more customizable and flexible for a wide variety of use cases.
In this guide, we will pit these two leading SIEM tools against each other and compare their features, strengths and weaknesses to help you to decide which one best fits your needs.
What is Splunk?
Splunk is a proprietary security and observability platform. It is designed to index large amounts of machine data (logs, events, and metrics) from a variety of sources and to provide a range of features for searching, analyzing, and visualizing that data so as to provide valuable observability insights.
Essentially, you need to feed the machine data to Splunk and it will do the dirty work of processing such data to extract the only the meaningful bits that will help you easily diagnose problems and opportunities for improvement.
It is often used by organizations to provide log management, security analysis, compliance monitoring, business analytics, and much more for their entire infrastructure. Its proprietary search language called Search Processing Language (SPL) is used for for querying collected datasets, and this enables the easy creation of visualisations, alerts and dashboards from unstructured data, which is normally a tedious process.
Splunk is known for its user-friendly interface and ease of use. It also has a broad range of features and functionality, including support for machine learning and other advanced analytics capabilities. This makes it a popular choice for organizations of all sizes and industries.
What is the Elastic/ELK stack?
As mentioned earlier, the Elastic Stack is a set of open-source tools for data ingestion, enrichment, storage, analysis, and visualization. It is designed to help users manage large volumes of data and make it easier to search, analyze, and visualize that data in real time.
The Elastic Stack was formerly known as the ELK stack which is an acronym for Elasticsearch, Logstash, and Kibana. Elasticsearch is a search engine and analytics platform, Logstash is a data processing pipeline, Kibana is a data visualization tool. More recently, Beats was added to the stack as a way to collect data from various sources which lead to the rebrand.
When used together, these tools form a powerful platform for working with large datasets in real-time. Companies often employ the Elastic/ELK stack log analysis, real-time analytics, and other use cases where searching, analyzing and visualizing large datasets is important.
Key things to note when choosing between Splunk and Elastic Stack
1. Data Ingestion
Before both platforms can work on your data, you need to ship the data to them first. Machine data are often of different formats and types, so it is crucial to know how what each platform supports and how to get the data from your application environment into their respective pipelines.
Splunk can ingest machine data in various formats including XML, JSON, CSV, TSV, and other structured formats. It also work with semi-structured or even unstructured data which can then be modelled into a structured format according to your requirements.
Sending data to Splunk is also fairly straightforward as it offers a variety of options depending on the data source you are working with which include the following:
- Ingest service for collecting JSON objects from the /events and /metrics endpoints of the Ingest REST API.
- Forwarder service for collecting data through Splunk forwarders.
- Streaming connectors for continuously receiving the data that is emitted by the data sources like Apache Kafka, Amazon Kinesis Data Stream, Google Cloud Pub/Sub, and others.
In the Elastic/ELK Stack, Logstash is responsible for shipping machine data from source to destination. It is typically used to process large volumes of log data, such as application logs, web server logs, and operating system logs. It works with structured and unstructured data, and it can parse and extract relevant details from the data which can then be transformed and enriched, using a variety of built-in filters and plugins. The data is then finally routed to Elasticsearch.
Beats are also another way to ship data in the Elastic Stack. They are single-purpose and lightweight services that that you install on your servers to capture all kinds of machine data (such as logs, metrics, or network packet data) as you see fit. For example:
- Filebeat tails and ships logs.
- Heartbeat ships uptime monitoring data.
- Packetbeat ships network data.
- and more!
Indexes are used to organize and search the ingested data and they can be compared to a database in relational database schema. This means that documents in an index are typically related to each other. For example, you can have an index for users and another for products.
When data is ingested, it is automatically indexed, which means that it is processed and added to one or more indexes. Indexing is an important part of the data ingestion pipeline, as it allows both platforms to quickly and efficiently search and retrieve data.
In Elasticsearch, indexes represent the largest entity that you can query against. Each index is identified through a unique name while performing indexing, search, update, and delete operations against the documents in it.
Indexes in Elasticsearch use the inverted index data structure which stores a
mapping from content (such as strings or numbers) to one or more documents
enabling the best matches for full-text searches even from huge data sets. To
index data in Elasticsearch, you can use the
_index API, which allows you to
add a JSON document to an index. You can also have an unlimited number of
Splunk uses its indexer component to index logs sent by the Splunk forwarder. It parses each data entry to extract defaults such as host, event source, and source type, and configures the character encoding. It then breaks down the data into lines and identifies timestamps or creates them to sort individual events by time. It can also be used to mask sensitive data at this stage.
After the parsing stage, the data is placed in segments (called buckets) that can be searched on. The level of segmentation affects speed, search capability, and compression efficiency. The data is subsequently written to disk and compressed. A key benefit of the Splunk indexer is that it stores multiple copies of the data to minimize the risk of data loss.
3. Data visualization
Data visualization in the Elastic/ELK Stack is handled by Kibana. Once you have your data indexed in Elasticsearch, you can use Kibana to create a variety of visualizations, such as line graphs, bar charts, and pie charts, to gain insights into the data.
When you log into Kibana, you can query your indexed data and narrow down the result to the specific data you want to visualize. You can then choose your preferred visualization type (e.g., line graph, bar chart, pie chart) and use the options in the editor to customize their appearance. After saving a visualization, you can add it to a dashboard or share with others by linking to it or exporting to an image or PDF file.
The Splunk Dashboards interface also allows users to visualize aggregated data. The dashboards are made up of panels containing various modules such as charts, graphs, inputs, boxes, and so on. You can connect them to a saved search and view real-time results as it gets updated in the background.
4. Setup and maintenance
Both Splunk and Elasticsearch are powerful tools for data analysis and visualization. However, the process of setting up and configuring these tools can be quite different.
Elasticsearch is a distributed search and analytics engine, which means that it is designed to run across multiple servers in a cluster. To set it up, you need to install the software on each server in the cluster, and then configure the servers to communicate with each other. This typically involves setting up network and security settings, as well as defining the roles and responsibilities of each server in the cluster. Once the cluster is up and running, you can use tools like Logstash and Kibana to ingest and visualize data in Elasticsearch.
Setting up Splunk is a bit simpler. You need to install the Splunk software on a server and follow the instructions in the installation wizard. This will typically involve specifying a directory for the Splunk data and logs, as well as setting up a user account and password. Once the installation is complete, you can log in to the Splunk web interface and start ingesting and analyzing data.
To summarize, setting up Splunk is easier and requires less technical expertise, while Elasticsearch demands a deeper understanding of distributed systems.
5. User interface
Splunk and Elasticsearch both have web-based user interfaces that allow you to perform a various tasks such as data ingestion, analysis, and visualization. However, the user interfaces of these two tools are quite different in terms of design and functionality.
The Splunk user interface is focused on search-based analytics and includes a search bar at the top of the screen. This allows you to enter search queries and view the results in real-time. The user interface also includes a number of pre-built dashboards and visualizations that allow you to quickly and easily gain insights into your ingested data.
On the other hand, the Kibana user interface is focused on data discovery and exploration. It includes a number of pre-built data visualizations and analysis tools, such as the Discover, Visualize, and Dashboard apps. These tools also allow you to quickly and easily explore and analyze your data, without needing to write complex search queries.
Overall, the user interfaces of Splunk and the Elastic Stack are designed to serve different purposes and provide different functionality. Splunk is focused on search-based analytics, while Elasticsearch is focused on data discovery and exploration.
Splunk is proprietary software so you will need to pay for a license to use it. While it offers a number of pricing options, the exact cost will depend on the specific features and options you choose, as well as the amount of data you plan to ingest and analyze. You can also contact sales to get a quote that's tailored to your needs.
All the components in the Elastic Stack are open-source software, so you can download and use them for free. However, you need to account for the support and maintenance it requires.
If you want to use Elasticsearch and other Elastic Stack products at scale, you may need to purchase a subscription from Elastic. The cost of the subscription will depend on the specific features and options you choose, as well as the amount of data you plan to ingest and analyze. You can contact Elastic sales to get a quote for your specific needs.
In general, the Elastic Stack is much cheaper than Splunk, but the exact cost will depend on your specific needs and requirements.
Choosing between Splunk and Elastic/ELK stack
Overall, the choice between Splunk and Elastic/ELK depends on the specific needs of your organization and the resources available to you. If you need a robust and user-friendly solution that can handle a wide range of log management and analysis tasks, Splunk may be the better option. If you want a more customizable and scalable tool that can be tailored to your specific needs, Elastic/ELK may be the better choice.
If you're looking for a simpler and more user-friendly alternative to both Splunk and the Elastic/ELK stack, you should check out Better Stack. Although it's not a complete replacement for either tool, it is a great choice for businesses that don’t have the resources to manage and setup the Elastic stack and are put off by the cost and complexity of Splunk. It is built on Clickhouse technology, and offers a compelling set of features relating to log management, monitoring and observability features at a great price.
You can sign up for a free trial to Better Uptime (for monitoring and incident management) and Logtail for log management and analysis.
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