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  5. Azure IoT Hub vs Kafka

Azure IoT Hub vs Kafka

OverviewDecisionsComparisonAlternatives

Overview

Kafka
Kafka
Stacks24.2K
Followers22.3K
Votes607
GitHub Stars31.2K
Forks14.8K
Azure IoT Hub
Azure IoT Hub
Stacks72
Followers106
Votes0

Azure IoT Hub vs Kafka: What are the differences?

Introduction

Azure IoT Hub and Kafka are both popular platforms for data streaming and processing. However, there are key differences between the two that make them suitable for different scenarios.

  1. Scalability: Azure IoT Hub is highly scalable and can handle millions of devices and messages, making it ideal for large-scale IoT applications. On the other hand, Kafka is designed for high-throughput data streaming and can handle large volumes of data from various sources, making it suitable for scenarios where scalability is a priority.

  2. Message persistence: Azure IoT Hub provides built-in message persistence, ensuring that messages are reliably delivered to devices even in the case of temporary network disruptions. Kafka, on the other hand, does not have built-in message persistence and relies on external storage systems for durability. This makes IoT Hub a better choice for applications that require guaranteed message delivery.

  3. Message transformation and routing: Azure IoT Hub provides a rich set of features for message transformation and routing, allowing users to filter, transform, and route messages based on different conditions and rules. Kafka, on the other hand, is primarily focused on data streaming and does not provide built-in capabilities for message transformation and routing. This makes IoT Hub more suitable for applications that require sophisticated message processing.

  4. Security and authentication: Azure IoT Hub offers robust security features, including device-to-cloud and cloud-to-device authentication. It also supports per-device access control and integration with Azure Active Directory for fine-grained access management. Kafka, on the other hand, provides basic security mechanisms such as SSL/TLS encryption but does not offer the same level of authentication and access control features as IoT Hub. This makes IoT Hub a better choice for applications that require strong security measures.

  5. Integration with other Azure services: Azure IoT Hub is tightly integrated with other Azure services, such as Azure Functions, Azure Stream Analytics, and Azure Machine Learning. This allows users to easily build end-to-end IoT solutions by leveraging the capabilities of these services. Kafka, on the other hand, is a standalone platform and requires additional integration efforts to work with other Azure services. This integration advantage makes IoT Hub a preferred choice for users already leveraging Azure services.

  6. Platform maturity and support: Azure IoT Hub is a fully managed service provided by Microsoft with extensive documentation, support, and community resources. It has been in the market for several years and is widely adopted. Kafka, on the other hand, is an open-source platform that is managed by the Apache Software Foundation. While Kafka has a strong community and ecosystem, it may not have the same level of commercial support and maturity as Azure IoT Hub.

Summary

In summary, Azure IoT Hub and Kafka have distinct differences in terms of scalability, message persistence, message transformation and routing, security and authentication, integration with other Azure services, and platform maturity and support. These differences make each platform more suitable for specific use cases and requirements.

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Advice on Kafka, Azure IoT Hub

viradiya
viradiya

Apr 12, 2020

Needs adviceonAngularJSAngularJSASP.NET CoreASP.NET CoreMSSQLMSSQL

We are going to develop a microservices-based application. It consists of AngularJS, ASP.NET Core, and MSSQL.

We have 3 types of microservices. Emailservice, Filemanagementservice, Filevalidationservice

I am a beginner in microservices. But I have read about RabbitMQ, but come to know that there are Redis and Kafka also in the market. So, I want to know which is best.

933k views933k
Comments
Ishfaq
Ishfaq

Feb 28, 2020

Needs advice

Our backend application is sending some external messages to a third party application at the end of each backend (CRUD) API call (from UI) and these external messages take too much extra time (message building, processing, then sent to the third party and log success/failure), UI application has no concern to these extra third party messages.

So currently we are sending these third party messages by creating a new child thread at end of each REST API call so UI application doesn't wait for these extra third party API calls.

I want to integrate Apache Kafka for these extra third party API calls, so I can also retry on failover third party API calls in a queue(currently third party messages are sending from multiple threads at the same time which uses too much processing and resources) and logging, etc.

Question 1: Is this a use case of a message broker?

Question 2: If it is then Kafka vs RabitMQ which is the better?

804k views804k
Comments
Roman
Roman

Senior Back-End Developer, Software Architect

Feb 12, 2019

ReviewonKafkaKafka

I use Kafka because it has almost infinite scaleability in terms of processing events (could be scaled to process hundreds of thousands of events), great monitoring (all sorts of metrics are exposed via JMX).

Downsides of using Kafka are:

  • you have to deal with Zookeeper
  • you have to implement advanced routing yourself (compared to RabbitMQ it has no advanced routing)
10.9k views10.9k
Comments

Detailed Comparison

Kafka
Kafka
Azure IoT Hub
Azure IoT Hub

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

Use device-to-cloud telemetry data to understand the state of your devices and define message routes to other Azure services without writing any code. In cloud-to-device messages, reliably send commands and notifications to your connected devices and track message delivery with acknowledgement receipts. Device messages are sent in a durable way to accommodate intermittently connected devices.

Written at LinkedIn in Scala;Used by LinkedIn to offload processing of all page and other views;Defaults to using persistence, uses OS disk cache for hot data (has higher throughput then any of the above having persistence enabled);Supports both on-line as off-line processing
device management; provisioning; bidirectional communication;
Statistics
GitHub Stars
31.2K
GitHub Stars
-
GitHub Forks
14.8K
GitHub Forks
-
Stacks
24.2K
Stacks
72
Followers
22.3K
Followers
106
Votes
607
Votes
0
Pros & Cons
Pros
  • 126
    High-throughput
  • 119
    Distributed
  • 92
    Scalable
  • 86
    High-Performance
  • 66
    Durable
Cons
  • 32
    Non-Java clients are second-class citizens
  • 29
    Needs Zookeeper
  • 9
    Operational difficulties
  • 5
    Terrible Packaging
No community feedback yet
Integrations
No integrations available
Windows
Windows
Python
Python
Linux
Linux
Java
Java
Azure Machine Learning
Azure Machine Learning

What are some alternatives to Kafka, Azure IoT Hub?

RabbitMQ

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Celery

Celery

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

Amazon SQS

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

NSQ

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

ActiveMQ

ActiveMQ

Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.

ZeroMQ

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Apache NiFi

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Gearman

Gearman

Gearman allows you to do work in parallel, to load balance processing, and to call functions between languages. It can be used in a variety of applications, from high-availability web sites to the transport of database replication events.

Memphis

Memphis

Highly scalable and effortless data streaming platform. Made to enable developers and data teams to collaborate and build real-time and streaming apps fast.

IronMQ

IronMQ

An easy-to-use highly available message queuing service. Built for distributed cloud applications with critical messaging needs. Provides on-demand message queuing with advanced features and cloud-optimized performance.

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