Splunk Enterprise Security and Elasticsearch both compete in the data management space, particularly for large-scale log and security information. Splunk seems to have an edge in handling compliance due to its extensive security features and adaptability to various data types.
Features: Splunk Enterprise Security offers simplified search capabilities, powerful log management, real-time alert systems, data type support, and compliance features. Elasticsearch, on the other hand, is known for its agile open-source structure, excellent scalability, and integration with Logstash and Kibana for robust data analysis and visualization.
Room for Improvement: Splunk could improve operational workflows, visualization capabilities, and ease of integration with security devices. Elasticsearch needs to address user interface complexities, improve usability for non-technical users, and enhance integration and analytics, particularly for threat intelligence and stability.
Ease of Deployment and Customer Service: Splunk offers extensive support with an active user community and flexible cloud and on-premises deployment but faces issues with response times. Elasticsearch's open-source nature offers flexibility and cost savings, though deployment can be challenging due to the need for tailored setups.
Pricing and ROI: Splunk Enterprise Security is more expensive but offers a comprehensive suite for larger enterprises. Its cost model may deter smaller businesses. Elasticsearch offers a more cost-effective solution with its open-source model and modular pricing, making it attractive for budget-conscious organizations seeking powerful data processing capabilities.
We have not purchased any licensed products, and our use of Elastic Search is purely open-source, contributing positively to our ROI.
I have noticed a return on investment with Splunk Enterprise Security, as it delivers substantial value for money.
For smaller organizations, other products may provide better value for money.
If you want to write your own correlation rules, it is very difficult to do, and you need Splunk's support to write new correlation rules for the SIEM tool.
I have sought assistance from Splunk Enterprise Security support in the past, particularly during deployment, and they provide friendly and effective help.
The technical support for Splunk met my expectations.
I can actually add more storage and memory because I host it in the cloud.
They struggle a bit with pure virtual environments, but in terms of how much they can handle, it is pretty good.
It is easy to scale.
I find it easy to scale Splunk Enterprise Security for our environment.
It provides a stable environment but needs to integrate with ITSM platforms to achieve better visibility.
It is very stable.
This can create problems for new developers because they have to quickly switch to another version.
Improving the infrastructure behind Splunk Enterprise Security is vital—enhanced cores, CPUs, and memory should be prioritized to support better processing power.
Data retention can be better. If we want to look at the data for five months or six months, that is not available to us. We only have a history of 20 or 30 days.
Splunk could enhance its offerings by incorporating modules for network detection and response and fraud management.
I saw clients spend two million dollars a year just feeding data into the Splunk solution.
The platform requires significant financial investment and resources, making it expensive despite its comprehensive features.
Splunk is priced higher than other solutions.
Elastic Search makes handling large data volumes efficient and supports complex search operations.
Aggregation is faster than querying directly from a database, like Postgres or Vertica.
This capability is useful for performance monitoring and issue identification.
Splunk Enterprise Security's most valuable features are its stability and the robust Splunk Search Processing Language.
The Splunk Enterprise Security's threat-hunting capabilities have been particularly useful in later releases.
Elasticsearch is a prominent open-source search and analytics engine known for its scalability, reliability, and straightforward management. It's a favored choice among enterprises for real-time data search, analysis, and visualization. Open-source Elasticsearch is free, offering a comprehensive feature set and scalability. It allows full control over deployments but requires managing and maintaining the infrastructure. On the other hand, Elastic Cloud provides a managed service with features like automated provisioning, high availability, security, and global reach.
Elasticsearch excels in handling time-sensitive data and complex search requirements across large datasets. Its scalability allows it to handle growing data volumes efficiently, maintaining high performance and fast response times. Integrated with Kibana, Elasticsearch enables powerful data visualization, providing real-time insights crucial for data-driven decision-making.
Elastic Cloud reduces operational overhead and improves scalability and performance, though it comes with associated costs. It is available on your preferred cloud provider — AWS, Azure, or Google Cloud. Customers who want to manage the software themselves, whether on public, private, or hybrid cloud, can download the Elastic Stack.
At its core, Elasticsearch is renowned for its full-text search capabilities, capable of performing complex queries and supporting features like fuzzy matching and auto-complete.
Peer reviews from various professionals highlight its strengths and weaknesses. Pros include its detection and correlation features, flexibility, cloud-readiness, extensibility, and efficient search capabilities. However, users have noted challenges like steep learning curves, data analysis limitations, and integration complexities. The platform is generally viewed as stable and scalable, with varying degrees of satisfaction regarding its usability and feature set.
In summary, Elasticsearch stands out for its high-speed search, scalability, and versatile analytics, making it a go-to solution for organizations managing large datasets. Its adaptability to different enterprise needs, robust community support, and continuous development keep it at the forefront of enterprise search and analytics solutions. However, potential users should be aware of its learning curve and the need for skilled personnel for optimization.
Splunk Enterprise Security is widely used for security operations, including threat detection, incident response, and log monitoring. It centralizes log management, offers security analytics, and ensures compliance, enhancing the overall security posture of organizations.
Companies leverage Splunk Enterprise Security to monitor endpoints, networks, and users, detecting anomalies, brute force attacks, and unauthorized access. They use it for fraud detection, machine learning, and real-time alerts within their SOCs. The platform enhances visibility and correlates data from multiple sources to identify security threats efficiently. Key features include comprehensive dashboards, excellent reporting capabilities, robust log aggregation, and flexible data ingestion. Users appreciate its SIEM capabilities, threat intelligence, risk-based alerting, and correlation searches. Highly scalable and stable, it suits multi-cloud environments, reducing alert volumes and speeding up investigations.
What are the key features?Splunk Enterprise Security is implemented across industries like finance, healthcare, and retail. Financial institutions use it for fraud detection and compliance, while healthcare organizations leverage its capabilities to safeguard patient data. Retailers deploy it to protect customer information and ensure secure transactions.
We monitor all Indexing and Search reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.