Open Source Alternatives to Splunk

Splunk offers powerful features for enterprise log management and analysis. But Splunk’s pricing is based on how much data you index, which has become very costly for many organizations with growing data. Many users find that once they point Splunk to data sources, those sources tend to generate more data at an escalating rate than expected, resulting in higher costs. In addition, users are required to be accustomed to Splunk's proprietary technology to really get the most out of it, which can impact the developer experience.

THE ELASTIC STACK – A VIABLE OPEN SOURCE ALTERNATIVE TO SPLUNK

With the expanding functionality, security, and control in leading open source log analytics solutions like the Elastic Stack (Elasticsearch, Logstash, Kibana, and Beats), organizations seeking the benefits of a system-wide logging infrastructure now have fully capable and highly cost-effective alternatives to proprietary products. They can: 

• Conduct search, real-time analytics, and reporting across all data sources.
• Execute complex queries from large datasets in seconds via a user-friendly interface, without developing or managing complex SQL queries. 
• Gather and parse log data from hundreds and thousands of production computers with minimal resource impact. 
• Provide reliable log file management for high-volume transaction systems that are intolerant of data loss. 
• Scale and customize to a full-fledged enterprise-class analytics platform for business intelligence. 
• Perform analysis of all machine-generated logs, structured and unstructured data, on-premise or in the cloud.
• Reduce licensing costs, especially for the enormous volumes of log data which modern systems produce.
• Be hosted and supported in a highly available environment of your choice, with enterprise-grade SLAs for peak performance.

COST-EFFECTIVE OPEN SOURCE LOG ANALYTICS APPLICATIONS

Common log analytics applications include regulatory compliance, e-commerce personalization, fraud detection, insider threat detection, risk management, security analysis, and IT operations/market intelligence. 

These applications can be built with the Elastic Stack or with a combination of open source tools like Apache Kafka, Flume, Spark, Hue, MLlib, and others.

A REFERENCE ARCHITECTURE FOR LOG ANALYTICS WITH THE ELASTIC STACK

Search Technologies has developed and already delivered a scalable, reliable, extensible and largely off-the-shelf open source analytics architecture that can help businesses find insights within massive data, seize new opportunities, and gain competitive advantage.

For example, Apache Kafka coupled with Elasticsearch, Logstash, Beats, and Kibana (the Elastic Stack) can store multiple petabytes of data in one cluster and combine clusters for horizontal scaling, providing a powerful tool for log analytics as well as a versatile foundation for other big data applications. The components work together to provide a seamless log analytics process:

• Logstash & Beats: collection, transmission, and parsing of logs
• Apache Kafka:  persistent storage of log data and distribution to multiple subscribers
• Elasticsearch:  indexing, search, and real-time analysis of log data
• Kibana: log reporting and visualization using a browser interface

Search Technologies have assisted our customers in many open source log analytics implementations that delivered quantified ROI while avoiding pitfalls. 

Contact us to learn more about open source log analytics alternatives and how we can help you implement the most suitable solution for your needs.