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.
I have seen a return on investment with VMware Tanzu Data Solutions because of its speed and the robustness of the environment.
There is plenty of community support available online.
The Apache community provides support for the open-source version.
I want to receive good technical support, which I only need once a month or every six months, and the experience has been unsatisfactory.
If anything happens in terms of technicalities and I raise a ticket, they address it immediately, irrespective of the SLA agreement.
Customer support for VMware Tanzu Data Solutions has been good with me and with VMware, including Broadcasts.
Customers have not faced issues with user growth or data streaming needs.
I need to enable my solution with high availability and scalability.
Most of our functions or jobs are queued due to that.
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.
I have faced stability issues, mainly due to the storage my organization has, though I am not sure if it's specifically due to the tool.
The performance angle is critical, and while it works in milliseconds, the goal is to move towards microseconds.
Scaling up continues to be a challenge, though it is much easier now than it was in the beginning.
A more user-friendly interface and better management consoles with improved documentation could be beneficial.
They are losing business.
VMware Tanzu Data Solutions can be improved as it is better and faster for administration and clusters, Dockers, and Kubernetes.
The open-source version of Apache Kafka results in minimal costs, mainly linked to accessing documentation and limited support.
Its pricing is reasonable.
My experience with pricing, setup cost, and licensing for VMware Tanzu Data Solutions is that it is a bit expensive.
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.
Apache Kafka acts as a broker and supports my data integration process by sitting in between systems.
The principal aspect is the creation of Kubernetes clusters.
VMware Tanzu Data Solutions has no limitation. We can meet the maximum customer requirements.
The product is not complex; I do not have to create stored procedures, functions, or views.
| Product | Mindshare (%) |
|---|---|
| Apache Kafka | 4.2% |
| Apache Flink | 10.9% |
| Databricks | 9.0% |
| Other | 75.9% |
| Product | Mindshare (%) |
|---|---|
| VMware Tanzu Data Solutions | 4.0% |
| Snowflake | 10.2% |
| Teradata | 9.0% |
| Other | 76.8% |
| Company Size | Count |
|---|---|
| Small Business | 32 |
| Midsize Enterprise | 18 |
| Large Enterprise | 49 |
| Company Size | Count |
|---|---|
| Small Business | 30 |
| Midsize Enterprise | 11 |
| Large Enterprise | 49 |
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.
VMware Tanzu is a robust platform tailored for data warehousing, complex analytics, BI applications, and predictive analytics. It excels in scalability, performance, and parallel processing, enhancing data handling efficiency. Users report significant productivity improvements and streamlined operations, making it ideal for comprehensive data solutions.
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