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.
The Apache community provides support for the open-source version.
There is plenty of community support available online.
Customers have not faced issues with user growth or data streaming needs.
Most of our functions or jobs are queued due to that.
Partitioning helps us distribute all the messages that we receive between all partitions, which helps us to be stable.
Apache Kafka is stable.
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.
I would appreciate having some kind of UI integrated into Apache Kafka for connecting to it because using code to connect it is basic, but we can use a UI.
A more user-friendly interface and better management consoles with improved documentation could be beneficial.
The open-source version of Apache Kafka results in minimal costs, mainly linked to accessing documentation and limited support.
Its pricing is reasonable.
Apache Kafka is effective when dealing with large volumes of data flowing at high speeds, requiring real-time processing.
It allows the use of data in motion, allowing data to propagate from one source to another while it is in motion.
The impact of Apache Kafka's scalability features on my organization and data processing capabilities depends on how many messages each company wants to receive.
The product is not complex; I do not have to create stored procedures, functions, or views.
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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