

Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
Returns depend on the application you deploy and the amount of benefits you are getting, which depends on how many applications you are deploying, what are the sorts of applications, and what are the requirements.
Incident troubleshooting time reduced by 35 to 45 percent because Zscaler logs could be directly used instead of correlating multiple cloud and firewall logs.
We did not need additional staff for cloud traffic security, and audit prep became faster, which helped optimize overall cost.
We have definitely seen a return on investment with Cloud Security Connector for Zscaler, saving us money by at least 20 to 25%.
I was getting prompt responses, and it was nicely handled regarding the support.
I would rate them eight if 10 was the best and one was the worst.
In more complex scenarios such as routing or policy tuning, support provided best practices and recommendations, which made the implementation smoother.
They were responsive whenever we had deployment or policy questions, and they helped ensure best practices.
We reached out to customer support three weeks back due to an issue where Zscaler got stuck, and they identified and solved the problem within 45 minutes, which is exceptional.
According to me, it is quite scalable in terms of all the data it can handle and stream.
As workloads grow, routing can be updated and traffic can be distributed across multiple connectors without major architecture changes.
The centralized policy scales with it, so we did not hit bottlenecks as we expanded.
Cloud Security Connector for Zscaler's scalability is definitely impressive, as it has handled growth and changes in our organization well.
There have not been any major downtime or critical issues.
We have run it in production for years and it is consistently reliable with no major downtime issues.
If it were easier to configure clusters and had more straightforward configuration, high-level API abstraction in the APIs could improve it.
Regarding additional improvements, I would say probably around error handling, where when we encounter errors specific to our response structures and everything, or the tables or anything of that nature, it would be better if we were prompted with better error handling mechanisms.
Observability and monitoring are areas that could be enhanced.
A key learning from this was that while Cloud Security Connector for Zscaler provides strong security control, proper policy tuning and understanding application behavior is critical for smooth deployment.
If I need to suggest an improvement, it would be to simplify the steep learning curve, as it can be complex for newcomers without prior experience.
Having deeper automation through auto-scaling based on workload changes could further reduce manual setup.
I thought Confluent would stop me when I crossed the credits, but it did not, and then I got charged.
Overall, while pricing depends on the enterprise agreement, the solution delivers good value considering the visibility, control, and security it provides.
The experience with pricing, setup cost, and licensing for Cloud Security Connector for Zscaler is definitely competitive.
Setup costs were mostly tied to initial deployment and policy design, but licensing was predictable based on the number of connectors and throughput.
These features are important due to scalability and resiliency.
The Kafka Streams API helps with real-time data transformations and aggregations.
The best features Apache Kafka on Confluent Cloud offers would be the connection with various external systems through various languages such as Python and C#.
It also provides Layer 4 routed bypass for TCP, UDP, and ICMP traffic, enabling granular traffic control that streamlines our services working with Zscaler.
The most valuable features of Cloud Security Connector for Zscaler include centralized visibility and control.
One of the best features Cloud Security Connector for Zscaler offers is its agentless design for cloud workloads.
| Product | Mindshare (%) |
|---|---|
| Apache Kafka on Confluent Cloud | 0.7% |
| Apache Flink | 8.9% |
| Databricks | 8.1% |
| Other | 82.3% |
| Product | Mindshare (%) |
|---|---|
| Cloud Security Connector for Zscaler | 0.7% |
| Cisco Umbrella | 26.5% |
| Zscaler Internet Access | 25.9% |
| Other | 46.900000000000006% |

| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 3 |
| Large Enterprise | 8 |
Apache Kafka on Confluent Cloud provides real-time data streaming with seamless integration, enhanced scalability, and efficient data processing, recognized for its real-time architecture, ease of use, and reliable multi-cloud operations while effectively managing large data volumes.
Apache Kafka on Confluent Cloud is designed to handle large-scale data operations across different cloud environments. It supports real-time data streaming, crucial for applications in transaction processing, change data capture, microservices, and enterprise data movement. Users benefit from features like schema registry and error handling, which ensure efficient and reliable operations. While the platform offers extensive connector support and reduced maintenance, there are areas requiring improvement, including better data analysis features, PyTRAN CDC integration, and cost-effective access to premium connectors. Migrating with Kubernetes and managing message states are areas for development as well. Despite these challenges, it remains a robust option for organizations seeking to distribute data effectively for analytics and real-time systems across industries like retail and finance.
What are the key features of Apache Kafka on Confluent Cloud?In industries like retail and finance, Apache Kafka on Confluent Cloud is implemented to manage real-time location tracking, event-driven systems, and enterprise-level data distribution. It aids in operations that require robust data streaming, such as CDC, log processing, and analytics data distribution, providing a significant edge in data management and operational efficiency.
Cloud Security Connector for Zscaler is designed to enhance security by blocking malicious content and providing agentless integration with cloud workloads. It simplifies policy management and improves scalability, making it a critical tool for securing cloud environments.
Cloud Security Connector for Zscaler automates node detection, route selection, and offers centralized visibility, allowing businesses to focus on scaling without traditional firewall constraints. Its high availability routing, SIEM integration, and centralized log management streamline security operations. The solution reduces data leakage risk by enforcing consistent policies across infrastructures. However, areas such as remote user network resolution, configuration complexity, and enhanced routing require improvements for optimal performance.
What are the key features of Cloud Security Connector for Zscaler?In industries like banking, Cloud Security Connector for Zscaler is vital for compliance support, as it monitors outbound traffic and enhances visibility over workload communication. Organizations benefit from zero-trust protection by inspecting all traffic for threats and accurately enforcing security policies, reducing configuration errors while supporting secure routing for cloud services like AWS.
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