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.
Customers have not faced issues with user growth or data streaming needs.
Most of our functions or jobs are queued due to that.
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.
We are always trying to find the best configs, which is a challenge.
The open-source version of Apache Kafka results in minimal costs, mainly linked to accessing documentation and limited support.
It allows the use of data in motion, allowing data to propagate from one source to another while it is in motion.
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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