Splunk Enterprise Security and Microsoft Power BI compete in data management and analysis. Splunk has an advantage with its advanced security capabilities, while Power BI excels in visualization and integration.
Features: Splunk is noted for its real-time security monitoring, advanced threat detection, and handling of structured and unstructured data. Microsoft Power BI offers intuitive dashboards, versatile analytics, and ease of use with integration capabilities.
Room for Improvement: Splunk can improve in user-friendliness for non-technical users, reduce complexity in deployment, and enhance its visualization capabilities. Microsoft Power BI could benefit from deeper security features, enhanced real-time data processing, and improved data ingestion from complex sources.
Ease of Deployment and Customer Service: Splunk requires careful configuration but has extensive support resources, making it suitable for complex security operations. Power BI’s cloud-based deployment is faster, offering accessible support and quick setup, making it appealing for businesses seeking rapid deployment.
Pricing and ROI: Splunk has higher upfront costs but provides strong security ROI. Power BI offers lower setup costs and delivers quick value through its comprehensive analytics, making it budget-friendly, especially for organizations not focused primarily on security.
In a world surrounded by data, tools that allow navigation of large data volumes ensure decisions are data-driven.
Power BI is easy to deploy within an hour, providing robust security against data leaks.
Splunk's cost is justified for large environments with extensive assets.
The significant drawback I notice is that Microsoft's size makes it hard to get specific change requests addressed unless they involve a bug.
We have a partnership with Microsoft, involving multiple weekly calls with dedicated personnel to ensure our satisfaction.
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.
The technical support for Splunk met my expectations.
You expect only a small percentage of users concurrently, but beyond a thousand concurrent users, it becomes difficult to manage.
With increasing AI capabilities, architectural developments within Microsoft, and tools like Fabric, I expect Power BI to scale accordingly.
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.
In terms of stability, there's no data loss or leakage, and precautions are well-managed by Microsoft.
We typically do not have problems with end-user tools like Excel and Power BI.
It's not a bad grade, as I know of better products in this field.
It provides a stable environment but needs to integrate with ITSM platforms to achieve better visibility.
It is very stable.
This makes Power BI difficult to manage as loading times can reach one or two minutes, which is problematic today.
Access was more logical in how it distinguished between data and its formatting.
Microsoft updates Power BI monthly based on user community feedback.
What Splunk could do better is to create an API to the standard SIEM tools, such as Microsoft Sentinel.
Splunk Enterprise Security would benefit from a more robust rule engine to reduce false positives.
Splunk could enhance its offerings by incorporating modules for network detection and response and fraud management.
I found the setup cost to be expensive
Power BI isn't very cheap, however, it is economical compared to other solutions available.
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.
In today's data-driven environment, these tools are of substantial value, particularly for large enterprises with numerous processes that require extensive data analysis.
The solution makes it easy for me to develop reports and publish them.
The entire ETL process is easy and supports many databases, allowing data pipelines from multiple sources to be gathered in one place for visualization.
This capability is useful for performance monitoring and issue identification.
They have approximately 50,000 predefined correlation rules.
The Splunk Enterprise Security's threat-hunting capabilities have been particularly useful in later releases.
Microsoft Power BI is a powerful tool for data analysis and visualization. This tool stands out for its ability to merge and analyze data from various sources. Widely adopted across different industries and departments, Power BI is instrumental in creating visually appealing dashboards and generating insightful business intelligence reports. Its intuitive interface, robust visualization capabilities, and seamless integration with other Microsoft applications empower users to easily create interactive reports and gain valuable insights.
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.
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