What is Azure IoT Hub and what are its top alternatives?
Top Alternatives to Azure IoT Hub
- AWS Greengrass
Greengrass lets you run IoT applications seamlessly across the AWS cloud and local devices using AWS Lambda and AWS IoT. ...
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...
- 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’t connected. ...
- 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. ...
- 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. ...
Balena makes it simple to deploy, update, and maintain code running on remote devices. ...
Losant is a complete developer platform for quickly, easily, and securely building connected experiences and solutions. ...
- Bespoken Tools
Bespoken makes it easy to develop for Alexa, Google Home and Chatbots. We provide logging, analytics, and our rapid development CLI. ...
Azure IoT Hub alternatives & related posts
related AWS Greengrass posts
- Open source17
- Written in Scala and java. Runs on JVM11
- Message broker + Streaming system8
- Avro schema integration4
- Suport Multiple clients2
- Partioned, replayable log2
- Extremely good parallelism constructs1
- Simple publisher / multi-subscriber model1
- Non-Java clients are second-class citizens30
- Needs Zookeeper28
- Operational difficulties8
- Terrible Packaging3
related Kafka posts
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:
- Our Algorithms Tour: https://algorithms-tour.stitchfix.com/
- Our blog: https://multithreaded.stitchfix.com/blog/
- Careers: https://multithreaded.stitchfix.com/careers/
#DataScience #DataStack #Data
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—this 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.
- Websocket broadcast1