Azure Stream Analytics and Apache Kafka are prominent players in the real-time data streaming and analytics domain. Azure Stream Analytics stands out with its seamless integration with Azure resources, making it more suitable for Microsoft-centric enterprises.
Features: Azure Stream Analytics offers ease of setup and use, especially for enterprises utilizing Microsoft ecosystems, with SQL-based configurations that ensure simplicity. It provides excellent real-time analytics capabilities and easy integration with Azure services like Power BI. Apache Kafka, being open-source, is known for its scalability and flexibility, offering clustering and message replay features, which are crucial for companies requiring robust, scalable solutions. Its integration capabilities across various platforms are noteworthy for high throughput and diverse data handling.
Room for Improvement: Azure Stream Analytics could benefit from more transparency in pricing and better integration with platforms outside Azure. Users also find a need for improved real-time data joins and data manipulation flexibility. Apache Kafka might improve by making consumer creation simpler, enhancing queue management, and providing better documentation. Users seek easier management, especially concerning latency and resource consumption, with room for improved UI and cloud integrations.
Ease of Deployment and Customer Service: Azure Stream Analytics is favorable for deployment in Microsoft-centric infrastructures on the public cloud, with commendable technical support responsiveness, contingent on service agreements. Meanwhile, Apache Kafka allows for broader deployment options, including on-premises and hybrid environments, requiring more technical expertise. Though community support is available, its deployment often requires substantial resources, affecting customer service experience.
Pricing and ROI: Azure Stream Analytics operates on a pay-per-use pricing model that sometimes results in unclear costs, yet it is generally competitive, with enterprises reporting good ROI due to rapid deployment and efficient integration. Apache Kafka, by virtue of being open-source, generally incurs lower direct costs, with expenses often related to third-party support or managed services like Confluent, offering strong ROI benefits, especially for open-source deployments that avoid licensing costs.
The Apache community provides support for the open-source version.
There is plenty of community support available online.
With Microsoft, expectations are higher because we pay for a license and have a contract.
There is a big communication gap due to lack of understanding of local scenarios and language barriers.
They've managed to answer all my questions and provide help in a timely manner.
The support on critical issues depends on the level of subscription that you have with Microsoft itself.
Customers have not faced issues with user growth or data streaming needs.
I need to enable my solution with high availability and scalability.
Maintenance requires a couple of people, however, it's not a full-time endeavor.
This is crucial for applications demanding constant monitoring, such as healthcare or financial services.
Azure Stream Analytics is scalable, and I would rate it seven out of ten.
Apache Kafka is stable.
This feature of Apache Kafka has helped enhance our system stability when handling high volume data.
Apache Kafka is a mature product and can handle a massive amount of data in real time for data consumption.
They require significant effort and fine-tuning to function effectively.
For example, Azure Stream Analytics processes more data every second, which is why it's recommended for real-time streaming.
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.
A more user-friendly interface and better management consoles with improved documentation could be beneficial.
A cost comparison between products is also not straightforward.
There's setup time required to get it integrated with different services such as Power BI, so it's not a straight out-of-the-box configuration.
Azure Stream Analytics currently allows some degree of code writing, which could be simplified with low-code or no-code platforms to enhance performance.
The open-source version of Apache Kafka results in minimal costs, mainly linked to accessing documentation and limited support.
Its pricing is reasonable.
Choosing between pay-as-you-go or enterprise models can affect pricing, and depending on data volume, charges might increase substantially.
From my point of view, it should be cheaper now, considering the years since its release.
We sell the data analytics value and operational value to customers, focusing on productivity and efficiency from the cloud.
Apache Kafka is effective when dealing with large volumes of data flowing at high speeds, requiring real-time processing.
Apache Kafka is particularly valuable for managing high levels of transactions.
It allows the use of data in motion, allowing data to propagate from one source to another while it is in motion.
It's very accurate and uses existing technologies in terms of writing queries, utilizing standard query languages such as SQL, Spark, and others to provide information.
Azure Stream Analytics reads from any real-time stream; it's designed for processing millions of records every millisecond.
It is quite easy for my technicians to understand, and the learning curve is not steep.
| Product | Market Share (%) |
|---|---|
| Azure Stream Analytics | 6.2% |
| Apache Kafka | 3.8% |
| Other | 90.0% |
| Company Size | Count |
|---|---|
| Small Business | 32 |
| Midsize Enterprise | 18 |
| Large Enterprise | 49 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 3 |
| Large Enterprise | 18 |
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.
Azure Stream Analytics is a robust real-time analytics service that has been designed for critical business workloads. Users are able to build an end-to-end serverless streaming pipeline in minutes. Utilizing SQL, users are able to go from zero to production with a few clicks, all easily extensible with unique code and automatic machine learning abilities for the most advanced scenarios.
Azure Stream Analytics has the ability to analyze and accurately process exorbitant volumes of high-speed streaming data from numerous sources at the same time. Patterns and scenarios are quickly identified and information is gathered from various input sources, such as social media feeds, applications, clickstreams, sensors, and devices. These patterns can then be implemented to trigger actions and launch workflows, such as feeding data to a reporting tool, storing data for later use, or creating alerts. Azure Stream Analytics is also offered on Azure IoT Edge runtime, so the data can be processed on IoT devices.
Top Benefits
Reviews from Real Users
“Azure Stream Analytics is something that you can use to test out streaming scenarios very quickly in the general sense and it is useful for IoT scenarios. If I was to do a project with IoT and I needed a streaming solution, Azure Stream Analytics would be a top choice. The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex.” - Olubisi A., Team Lead at a tech services company.
“It's used primarily for data and mining - everything from the telemetry data side of things. It's great for streaming and makes everything easy to handle. The streaming from the IoT hub and the messaging are aspects I like a lot.” - Sudhendra U., Technical Architect at Infosys
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