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.
With Microsoft, expectations are higher because we pay for a license and have a contract.
There is plenty of community support available online.
The Apache community provides support for the open-source version.
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.
Customers have not faced issues with user growth or data streaming needs.
I need to enable my solution with high availability and scalability.
According to me, it is quite scalable in terms of all the data it can handle and stream.
This feature of Apache Kafka has helped enhance our system stability when handling high volume data.
Apache Kafka is stable.
Apache Kafka is a mature product and can handle a massive amount of data in real time for data consumption.
The performance angle is critical, and while it works in milliseconds, the goal is to move towards microseconds.
We are always trying to find the best configs, which is a challenge.
Scaling up continues to be a challenge, though it is much easier now than it was in the beginning.
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.
If it were easier to configure clusters and had more straightforward configuration, high-level API abstraction in the APIs could improve it.
Observability and monitoring are areas that could be enhanced.
The open-source version of Apache Kafka results in minimal costs, mainly linked to accessing documentation and limited support.
Its pricing is reasonable.
There was back and forth with the team regarding the cost, but overall, I feel it would have been easier to handle if there were a mechanism that let us know when the credits were being fully utilized or when the limits were getting crossed.
Apache Kafka is particularly valuable for managing high levels of transactions.
Apache Kafka is effective when dealing with large volumes of data flowing at high speeds, requiring real-time processing.
Splitting the topic data into multiple partitions primarily helps with my system scalability.
These features are important due to scalability and resiliency.
This helped a lot with regard to log monitoring and many other streaming use cases where we were able to get all the data in one place and stream it at a quicker rate.
The Kafka Streams API helps with real-time data transformations and aggregations.
| Product | Market Share (%) |
|---|---|
| Apache Kafka | 3.8% |
| Apache Kafka on Confluent Cloud | 0.5% |
| Other | 95.7% |
| Company Size | Count |
|---|---|
| Small Business | 32 |
| Midsize Enterprise | 18 |
| Large Enterprise | 49 |
| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 3 |
| Large Enterprise | 8 |
Apache Kafka is an open-source distributed streaming platform that serves as a central hub for handling real-time data streams. It allows efficient publishing, subscribing, and processing of data from various sources like applications, servers, and sensors.
Kafka's core benefits include high scalability for big data pipelines, fault tolerance ensuring continuous operation despite node failures, low latency for real-time applications, and decoupling of data producers from consumers.
Key features include topics for organizing data streams, producers for publishing data, consumers for subscribing to data, brokers for managing clusters, and connectors for easy integration with various data sources.
Large organizations use Kafka for real-time analytics, log aggregation, fraud detection, IoT data processing, and facilitating communication between microservices.
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.
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