What is Firebase Cloud Messaging and what are its top alternatives?
Top Alternatives to Firebase Cloud Messaging
OneSignal is a high volume push notification service for websites and mobile applications. OneSignal supports all major native and mobile platforms by providing dedicated SDKs for each platform, a RESTful server API, and a dashboard. ...
Google Cloud Messaging
Google Cloud Messaging (GCM) is a free service that enables developers to send messages between servers and client apps. This includes downstream messages from servers to client apps, and upstream messages from client apps to servers. ...
Firebase is a cloud service designed to power real-time, collaborative applications. Simply add the Firebase library to your application to gain access to a shared data structure; any changes you make to that data are automatically synchronized with the Firebase cloud and with other clients within milliseconds. ...
Amazon Simple Notification Service makes it simple and cost-effective to push to mobile devices such as iPhone, iPad, Android, Kindle Fire, and internet connected smart devices, as well as pushing to other distributed services. Besides pushing cloud notifications directly to mobile devices, SNS can also deliver notifications by SMS text message or email, to Simple Queue Service (SQS) queues, or to any HTTP endpoint. ...
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...
SignalR allows bi-directional communication between server and client. Servers can now push content to connected clients instantly as it becomes available. SignalR supports Web Sockets, and falls back to other compatible techniques for older browsers. SignalR includes APIs for connection management (for instance, connect and disconnect events), grouping connections, and authorization. ...
Pusher is the category leader in delightful APIs for app developers building communication and collaboration features. ...
It was designed as an extremely lightweight publish/subscribe messaging transport. It is useful for connections with remote locations where a small code footprint is required and/or network bandwidth is at a premium. ...
Firebase Cloud Messaging alternatives & related posts
- Free of cost28
- More parametrable22
- Simple and Easy To Use. Well Documented :)15
- Device to device13
- Great support9
- Many Platforms8
- Flexibility in working with web, android and iOS4
- Phonegap / Cordova / Ionic starters3
related OneSignal posts
At the beginning of the ¹OnePlace project, our team needed to decide which service to use to send bulk notifications to all users quickly and consistently. We tested some services without success and as we already used firebase we decided to implement in the beginning using Notifications by Firebase. We're having trouble updating the user's token to see if it has removed the app or not. This brought a working overhead and we were not sure if the notification had arrived at the destination. Today we use OneSignal, which did not bring reliability to the submitted notifications, nor do we need to bother with manual token updates. We ship approximately 500 thousand notifications with 100% delivery!
Hi there, we are a seed-stage startup in the personal development space. I am looking at building the marketing stack tool to have an accurate view of the user experience from acquisition through to adoption and retention for our upcoming React Native Mobile app. We qualify for the startup program of Segment and Mixpanel, which seems like a good option to get rolling and scale for free to learn how our current 60K free members will interact in the new subscription-based platform. I was considering AppsFlyer for attribution, and I am now looking at an affordable yet scalable Mobile Marketing tool vs. building in-house. Braze looks great, so does Leanplum, but the price points are 30K to start, which we can't do. I looked at OneSignal, but it doesn't have user flow visualization. I am now looking into Urban Airship and Iterable. Any advice would be much appreciated!
- Easy setup3
- Easy iOS setup1
- IOS Support1
related Google Cloud Messaging posts
- Realtime backend made easy361
- Fast and responsive263
- Easy setup234
- Backed by google121
- Angular adaptor81
- Great customer support36
- Great documentation26
- Real-time synchronization23
- Mobile friendly20
- Rapid prototyping17
- Great security12
- Automatic scaling11
- Freakingly awesome10
- Angularfire is an amazing addition!8
- Super fast development8
- Awesome next-gen backend6
- Ios adaptor6
- Built in user auth/oauth5
- Firebase hosting5
- Speed of light4
- Very easy to use4
- It's made development super fast3
- Brilliant for startups3
- Great all-round functionality2
- Low battery consumption2
- I can quickly create static web apps with no backend2
- The concurrent updates create a great experience2
- JS Offline and Sync suport2
- Faster workflow1
- Free SSL1
- Good Free Limits1
- Push notification1
- Easy to use1
- Easy Reactjs integration1
- Can become expensive27
- Scalability is not infinite15
- No open source, you depend on external company14
- Not Flexible Enough9
- Cant filter queries5
- Very unstable server3
- Too many errors2
- No Relational Data2
related Firebase posts
This is my stack in Application & Data
My Utilities Tools
Google Analytics Postman Elasticsearch
My Devops Tools
Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack
My Business Tools
- Low cost9
- Supports multi subscribers3
related Amazon SNS posts
We decided to use AWS Lambda for several serverless tasks such as
- Managing AWS backups
- Processing emails received on Amazon SES and stored to Amazon S3 and notified via Amazon SNS, so as to push a message on our Redis so our Sidekiq Rails workers can process inbound emails
- Pushing some relevant Amazon CloudWatch metrics and alarms to Slack
Instead of Amazon SNS, which is currently being used to send outbound push notification and including SMS, we want to build the 2 Way SMS using Amazon Pinpoint. Just want to know about Pinpoint and any outstanding issues if we drop SNS since it does not support 2 Way and use Pinpoint for both incoming and outgoing flow.
- Open source14
- Written in Scala and java. Runs on JVM10
- Message broker + Streaming system6
- Avro schema integration4
- Suport Multiple clients2
- Partioned, replayable log2
- Extremely good parallelism constructs1
- Simple publisher / multi-subscriber model1
- Non-Java clients are second-class citizens27
- Needs Zookeeper26
- Operational difficulties7
- Terrible Packaging2
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.
- Supports .NET server19
- Fallback to SSE, forever frame, long polling10
- Open source4
- Ease of use2
- Requires jQuery1
- Expertise hard to get1
- Weak iOS and Android support1
related SignalR posts
- An easy way to give customers realtime features51
- Easy to get started with27
- Free plan24
- Heroku Add-on12
- Easy and fast to configure and to understand11
- Azure Add-on6
- Push notification4
related Pusher posts
Which messaging service (Pusher vs. PubNub vs. Google Cloud Pub/Sub) to use for IoT?
Recently we finished long research on chat tool for our students and mentors. In the end we picked Mattermost Team Edition as the cheapest and most feature complete option. We did consider building everything from scratch and use something like Pusher or Twilio on a backend, but then we would have to implement all the desktop and mobile clients and all the features oursevles. Mattermost gave us flexible API, lots of built in or easy to install integrations and future-proof feature set. We are still integrating it with our main platform but so far the team, existing mentors and students are very happy.
- Varying levels of Quality of Service to fit a range of2
- Very easy to configure and use with open source tools1
- Lightweight with a relatively small data footprint1
- Easy to configure in an unsecure manner1