Firebase
Supabase
OneSignal
Android Studio
Socket.io
Pusher
PubNub
Xcode
Hadoop
Apache Spark
Apache Cassandra
Apache Flink
PostgreSQL
MongoDB
Apache Storm
Apache Kafka
Firebase HadoopBased on our record, Firebase seems to be a lot more popular than Hadoop. While we know about 248 links to Firebase, we've tracked only 15 mentions of Hadoop. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Head over to Firebase Developer Console homepage, sign in using your Gmail address, and click the Go to Console button to navigate to the console's overview page. - Source: dev.to / 3 days ago
I didn't really give much thought as to which backend I would use. I already had 2 projects in Supabase (BOXCUT & MineWork), but also a few projects in Firebase too. I was more concerned at the time at actually building the product. - Source: dev.to / 5 days ago
Firebase, a well-known backend platform, is widely utilized for building Serverless or Headless web and mobile applications. This discussion will delve into executing comprehensive CRUD (Create, Read, Update, Delete) operations within Firebase. CRUD operations serve as fundamental building blocks for both web and mobile applications. To initiate this process, create a new project in the Firebase Console.... - Source: dev.to / about 2 months ago
For example, you can rely on the powerful OAuth by Okta to handle your Auth services, Flutterwave payment gateway to accept payment, and Google Firebase Messaging to manage notifications. - Source: dev.to / 2 months ago
Backend as a Service (BaaS) goes back to early 2010’s with companies like Parse and Firebase. These products integrated everything a backend provides to a webapp in a single, integrated package that makes it easier to get started and enables you to offload some of the devops maintenance work to someone else. - Source: dev.to / 3 months ago
Did you check out tools like https://hadoop.apache.org/ ? Source: about 1 year ago
There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 1 year ago
There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS. - Source: dev.to / over 1 year ago
A copy of Hadoop installed on each of these machines. You can download Hadoop from the Apache website, or you can use a distribution like Cloudera or Hortonworks. - Source: dev.to / over 1 year ago
The Apache™ Hadoop™ project develops open-source software for reliable, scalable, distributed computing. - Source: dev.to / over 1 year ago
Supabase - An open source Firebase alternative
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
OneSignal - Customer engagement platform used by over 1 million developers and marketers; the fastest and most reliable way to send mobile and web push notifications, in-app messages, emails, and SMS.
Apache Cassandra - The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance.
Android Studio - Android development environment based on IntelliJ IDEA
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
