Alternatives to Azure IoT Hub logo

Alternatives to Azure IoT Hub

AWS Greengrass, Kafka, Amazon IoT, Google Cloud IoT Core, and AWS IoT Device Management are the most popular alternatives and competitors to Azure IoT Hub.
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What is Azure IoT Hub and what are its top alternatives?

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.
Azure IoT Hub is a tool in the Internet of Things Services category of a tech stack.

Top Alternatives to Azure IoT Hub

  • AWS Greengrass

    AWS Greengrass

    Greengrass lets you run IoT applications seamlessly across the AWS cloud and local devices using AWS Lambda and AWS IoT. ...

  • Kafka

    Kafka

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

  • Amazon IoT

    Amazon IoT

    AWS IoT is a managed cloud platform that lets connected devices easily and securely interact with cloud applications and other devices. AWS IoT can support billions of devices and trillions of messages, and can process and route those messages to AWS endpoints and to other devices reliably and securely. With AWS IoT, your applications can keep track of and communicate with all your devices, all the time, even when they aren鈥檛 connected. ...

  • Google Cloud IoT Core

    Google Cloud IoT Core

    Cloud IoT Core is a fully managed service that allows you to easily and securely connect, manage, and ingest data from millions of globally dispersed devices. Cloud IoT Core, in combination with other services on Google Cloud IoT platform, provides a complete solution for collecting, processing, analyzing, and visualizing IoT data in real time to support improved operational efficiency. ...

  • AWS IoT Device Management

    AWS IoT Device Management

    AWS IoT Device Management makes it easy to securely onboard, organize, monitor, and remotely manage IoT devices at scale. IoT Device Management lets you register your devices individually or in bulk, and manage permissions so that devices remain secure. Then, you use the IoT Device Management console to organize your devices into groups, monitor and troubleshoot device functionality, and send remote updates to your devices. ...

  • Resin.io

    Resin.io

    Balena makes it simple to deploy, update, and maintain code running on remote devices. ...

  • Bespoken Tools

    Bespoken Tools

    Bespoken makes it easy to develop for Alexa, Google Home and Chatbots. We provide logging, analytics, and our rapid development CLI. ...

  • Losant

    Losant

    Losant is a complete developer platform for quickly, easily, and securely building connected experiences and solutions. ...

Azure IoT Hub alternatives & related posts

AWS Greengrass logo

AWS Greengrass

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Local compute, messaging, data caching, and synch capabilities for connected devices
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PROS OF AWS GREENGRASS
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      Kafka logo

      Kafka

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      Distributed, fault tolerant, high throughput pub-sub messaging system
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      PROS OF KAFKA
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        High-throughput
      • 113
        Distributed
      • 85
        Scalable
      • 78
        High-Performance
      • 64
        Durable
      • 35
        Publish-Subscribe
      • 17
        Simple-to-use
      • 14
        Open source
      • 10
        Written in Scala and java. Runs on JVM
      • 6
        Message broker + Streaming system
      • 4
        Avro schema integration
      • 2
        Suport Multiple clients
      • 2
        Robust
      • 2
        KSQL
      • 2
        Partioned, replayable log
      • 1
        Fun
      • 1
        Extremely good parallelism constructs
      • 1
        Simple publisher / multi-subscriber model
      • 1
        Flexible
      CONS OF KAFKA
      • 27
        Non-Java clients are second-class citizens
      • 26
        Needs Zookeeper
      • 7
        Operational difficulties
      • 2
        Terrible Packaging

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      Eric Colson
      Chief Algorithms Officer at Stitch Fix | 21 upvotes 路 1.8M views

      The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

      Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

      At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

      For more info:

      #DataScience #DataStack #Data

      See more
      John Kodumal

      As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data鈥攖his is made HA with the use of Patroni and Consul.

      We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

      See more
      Amazon IoT logo

      Amazon IoT

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      Easily and securely connect devices to the cloud. Reliably scale to billions of devices and trillions of messages.
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      PROS OF AMAZON IOT
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      CONS OF AMAZON IOT
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        Google Cloud IoT Core logo

        Google Cloud IoT Core

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        A fully managed service to easily and securely connect, manage, and ingest data from globally dispersed devices
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        PROS OF GOOGLE CLOUD IOT CORE
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            AWS IoT Device Management logo

            AWS IoT Device Management

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            Onboard, organize, monitor, and remotely manage connected devices at scale
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            PROS OF AWS IOT DEVICE MANAGEMENT
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                Resin.io logo

                Resin.io

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                Push Updates to Linux Devices with Git and Docker
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                    Bespoken Tools logo

                    Bespoken Tools

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                    Bespoken makes it easy to develop for Alexa, Google Home and Chatbots.
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                        Losant logo

                        Losant

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                        IoT developer platform for building and visualizing real-time Internet of Things solutions with ease
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                        PROS OF LOSANT
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