Servers are at the core of an IT network and are mostly considered protected by the firewall layer, but in fact, they are often exposed to great risks. In reality, they are vulnerable and need protection. The greatest concern is to find the source of the threat, whether a hacker acting as an insider who committed security breaches is involved. n unhappy employee can destroy the entire network and rapidly undermine the finances. With the growing use of remote connectivity technologies; more telecommuters, global offices, and business ventures and competitors are being incorporated into the business. The remote employees can pose the same risks as internal employees. This could be due to inadequate security and a lack of monitoring of your remote networking facilities. It’s necessary to keep an eye on the wrongdoers, the data thieves, and their pattern of working. The worst cases involve theft or manipulation of specific, confidential files or damaging confidential data, mismanagement of the password, deletion of the web pages, which eventually results in adverse business disruption, affecting the revenue, company prestige, and legal penalties.
As businesses face outages and multiple security risks, tracking an entire application network is important for identifying the cause of vulnerability or when the outage occurred, as well as checking events, records and traces at that point in time to identify network behavior and take preventive and corrective measures. Monitoring logs and log analysis is important for any IT operations team to identify intrusion attempts and misconfigurations, track application performance, improve customer satisfaction, enhance security against cyber-attacks, perform root cause analysis, and analyze system behavior, performance, measurements, and log analysis based metrics. Log management solutions play a key role in the layered security framework of an enterprise — without them, firms have little visibility of the actions and events taking place within their infrastructure that could either result in data breaches or lead to an ongoing security compromise. Splunk and ELK (a.k.a. BELK or Elastic Stack) are two of the category ‘s leading business applications.
Within today’s IT networks, most, if not all, systems and tools generate extensive logfiles that archive the minutiae of day-to-day operations: what services were accessed and by whom, tasks conducted, host errors/exceptions, and more. As you might expect, the number of log files inside the network of any particular company will quickly become disadvantageous. Log management and analytics tools allow companies to derive actionable information from this sea of data collective.
Elasticsearch vs Splunk: An overview
To solve the same problem Splunk and the ELK Stack use two different methods. People typically choose one or the other based on how structured their organizations are, and how much time they want to spend on log analytics. Splunk takes a bunch of data and helps people to search and find what they need from the information. In the beginning, ELK needs more preparation and planning but in the end, extraction of the value becomes easier.
Splunk’s three main components are its forwarder, which transfers data to remote indexers; indexer, which has functions to store and index data and handle search requests; and search head, which is the front end of the user interface where all three components can be integrated or spread over servers. Splunk also facilitates the incorporation of its functionalities through SDKs into applications. Operational management, compliance, and application behavior analytics are common use cases. Splunk is a subscription service where indexing volume is used to generate billing.
The ELK Stack is a compilation of three open-source tools both built and maintained by Elastic — Elasticsearch, Logstash, and Kibana. Elasticsearch is the NoSQL database that uses the search engine Lucene. Logstash is a data collection and distribution pipeline used to load Elasticsearch with the data (although it supports other destinations such as Graphite, Kafka, Nagios, and RabbitMQ as well). Kibana is a dashboard that works on top of Elasticsearch, using visualizations and dashboards to facilitate data analysis.
Both Splunk and the ELK Stack can be used for monitoring and analyzing infrastructure in IT operations and for monitoring applications, security, and business intelligence.
In addition to being a log management and analysis platform, Splunk is also marketed as a Security Information and Event Management (SIEM) solution, known as the “Google for logfiles” SIEM is simply logging management as applied to security: by unifying logfile data obtained from a variety of systems and devices in an IT ecosystem, operators and infosec professionals can conduct higher-order security analytics and evaluations from a common platform about the overall status of their networks. SIEM devices are abundant on the market, but Splunk reigns supreme in this category because of the Google-Esque search features. The platform uses a proprietary search language named Search Processing Language (SPL) to traverse and execute large data sets of contextual queries. Splunk now offers over 1000 applications and add-ons for extending the functionality of the platform to fit various data sources.
ELK is a consolidated data analytics platform from the open-source software developer Elastic,. ELK is short for Elasticsearch, Logstash, and Kibana. The organization is most widely known for the elastic Apache Lucene-based search engine, Elasticsearch. Elasticsearch (distributed RESTful search/analytics engine), Logstash (data retrieval pipeline), and Kibana (data visualization) are the application stack of ELK. Recently, Beats made its place into the stack, providing single purpose agent-based data shipping. Now Elastic is promoting this conglomerate as the open source Elastic Stack. Aside from the ELK/Elastic stack, each of these technologies is available as a separate Elastic package.
Comparison of Splunk and ELK
Ease of use
Both frameworks are fairly simple to implement and use, especially because of the variety of features and capabilities of each platform. That said, the dashboards from Splunk provide greater accessibility and their set-up options are a lot more advanced and streamlined than ELK / Elastic Stack. Moreover, the user management features of ELK are more difficult to use than those of Splunk. On the other hand, AWS offers Elasticsearch as a service that removes much of the deployment and management difficulties involved.
It is fairly easy to ship data to Splunk. The forwarders are pre-configured after installation for a wide selection of data sources such as files and directories, network events, windows sources, and application logs and used to import data into Splunk.
Logstash is used in the ELK Stack to forward data from source to destination. Additionally, Logstash must be configured to identify each field before the data is shipped to Elasticsearch. To those who don’t deal with scripting languages (such as Bash, Python, or Ruby) this sort of configuration can be complicated, but there is a strong online support that can be accessed very easily.
A core feature of any log management tool is the search function. Both the web interfaces of Splunk and ELK Stack support the search through a dedicated search bar. The query syntax on Kibana is based on the syntax of the Lucene query while Splunk uses its own Splunk Search Processing Language. Lucene is already common to people familiar with scripting languages, while SPL is proprietary and therefore needs to be learned. Another difference is that when formatted, Splunk offers dynamic data exploration to help users find and extract everything as a searchable field in a way that allows them to search for non-configured fields. On the other hand, Elasticsearch fields must be defined in advance to use aggregation over the log properties.
The Splunk web user interface contains flexible controls to edit and add new components to your dashboard. Management and user controls for multiple users can be configured differently, with each having a custom dashboard. Splunk also supports application and visualization features that can be easily customized using XML on mobile devices.
Kibana is the visualization tool in the ELK Stack. Much like Splunk, the platform allows the creation of visualizations such as line charts, area graphics, and tables in a dashboard. The search filter is also displayed over the different views: if a query is used, it will be added automatically to the dashboard objects. Splunk also has a similar alternative but XML setup is required. Still, Kibana does not support user management but provides it out of the box with hosting ELK solutions.
API and interoperability
Splunk provides a well-documented RESTful API with more than 200 endpoints to access each feature in the app, as well as common language SDKs. The Elasticsearch by ELK/Elastic Stack was developed as a distributed search and analytics engine from the ground up using traditional RESTful APIs and JSON. It also offers pre-built custom app building clients in languages like Java, Python, .NET, and more.
The high price tag for Splunk comes with the advantage of delivering a well-rounded overall product. Users can be locked into a vendor, but one vendor is what it takes to do almost something. The ELK Stack open-source is seemingly free, but it doesn’t provide many features like alerting out of the box — and it costs money to develop and maintain them.
ELK has many varieties:
- Open-source platform ELK Stack (Elastic)
- The Hosted Elasticsearch (AWS)
- AI-powered ELK on the enterprise-grade platform (Logz.io)
One way to look at the Splunk-versus-ELK debate is to depict it as the old debate between Microsoft and Linux. You’ll like Splunk if you like Microsoft. You will want to use the ELK Stack if you like Linux.
Integrations of the third-party tools
The Splunk App portal has over 1000 add-ons and applications organized into six categories: DevOps, IT operations, security/fraud, business intelligence, IoT/industrial data, and utilities. ELK/Elastic Stack also provides a range of extensions and integrations both from the community and supported by third-party vendors.
With a large community of users and supporters, both are market leaders in their respective categories. However, open-source has its advantages and ELK/Elastic Stack boasts a highly active and responsive developer/user community and an abundance of online resources.
Splunk is a proprietary company with a high-end price tag, while ELK/Elastic Stack is an open-source platform free of charge. Nevertheless, the total cost of ownership of ELK/Elastic Stack can be quite substantial for expansive infrastructures as well: hardware costs, storage costs, and professional services can quickly add up (although the aforementioned AWS service can simplify that if cloud hosting is a viable option). Both Splunk and ELK/Elastic Stack now offer more price-conscious organizations cloud-based, hosted versions. Both the support offerings from ELK/Elastic Stack and Splunk are exceptional in terms of support.
SIEM and Log-analytics are just one piece of the existing security puzzle. In short, both Splunk and ELK/Elastic Stack are trusted by the leading organizations of the world as the competent log management and analytical platforms. Total ownership costs may be essential for both solutions, with Splunk and Elastic recently started to offer the host versions of their products in response to demand from more budget-oriented companies. Users prefer to use something they know about or are already using. The open-source ELK Stack has many features. This reduces the gap between Splunk and itself. At any point in time, features only present in Splunk may be added to ELK. Therefore, if you need a more mature product, go to Splunk. However, if you want a flexible product, ELK is the way forward. In any case, seeing these two continue to compete is interesting.