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Amazon Kinesis
Amazon Kinesis

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Amazon Kinesis vs Firehose.io: What are the differences?

What is Amazon Kinesis? Store and process terabytes of data each hour from hundreds of thousands of sources. Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.

What is Firehose.io? Build realtime Ruby web applications. Firehose is both a Rack application and JavaScript library that makes building real-time web applications possible.

Amazon Kinesis can be classified as a tool in the "Real-time Data Processing" category, while Firehose.io is grouped under "Realtime Backend / API".

Some of the features offered by Amazon Kinesis are:

  • Real-time Processing- Amazon Kinesis enables you to collect and analyze information in real-time, allowing you to answer questions about the current state of your data, from inventory levels to stock trade frequencies, rather than having to wait for an out-of-date report.
  • Easy to use- You can create a new stream, set the throughput requirements, and start streaming data quickly and easily. Amazon Kinesis automatically provisions and manages the storage required to reliably and durably collect your data stream.
  • High throughput. Elastic.- Amazon Kinesis seamlessly scales to match the data throughput rate and volume of your data, from megabytes to terabytes per hour. Amazon Kinesis will scale up or down based on your needs.

On the other hand, Firehose.io provides the following key features:

  • Firehose embraces RESTful design prinicpals and consequently works great with any library that speaks HTTP
  • Integrates with Backbone.js and Ember.js
  • Hook it into an ORM, like ActiveRecord
- No public GitHub repository available -
- No public GitHub repository available -

What is Amazon Kinesis?

Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.

What is Firehose.io?

Firehose is both a Rack application and JavaScript library that makes building real-time web applications possible.
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Why do developers choose Amazon Kinesis?
Why do developers choose Firehose.io?
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      What companies use Amazon Kinesis?
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      What tools integrate with Amazon Kinesis?
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      What are some alternatives to Amazon Kinesis and Firehose.io?
      Kafka
      Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
      Apache Spark
      Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
      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.
      Amazon Kinesis Firehose
      Amazon Kinesis Firehose is the easiest way to load streaming data into AWS. It can capture and automatically load streaming data into Amazon S3 and Amazon Redshift, enabling near real-time analytics with existing business intelligence tools and dashboards you鈥檙e already using today.
      Apache Storm
      Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.
      See all alternatives
      Decisions about Amazon Kinesis and Firehose.io
      Praveen Mooli
      Praveen Mooli
      Engineering Manager at Taylor and Francis | 12 upvotes 528K views
      MongoDB Atlas
      MongoDB Atlas
      Java
      Java
      Spring Boot
      Spring Boot
      Node.js
      Node.js
      ExpressJS
      ExpressJS
      Python
      Python
      Flask
      Flask
      Amazon Kinesis
      Amazon Kinesis
      Amazon Kinesis Firehose
      Amazon Kinesis Firehose
      Amazon SNS
      Amazon SNS
      Amazon SQS
      Amazon SQS
      AWS Lambda
      AWS Lambda
      Angular 2
      Angular 2
      RxJS
      RxJS
      GitHub
      GitHub
      Travis CI
      Travis CI
      Terraform
      Terraform
      Docker
      Docker
      Serverless
      Serverless
      Amazon RDS
      Amazon RDS
      Amazon DynamoDB
      Amazon DynamoDB
      Amazon S3
      Amazon S3
      #Backend
      #Microservices
      #Eventsourcingframework
      #Webapps
      #Devops
      #Data

      We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

      To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

      To build #Webapps we decided to use Angular 2 with RxJS

      #Devops - GitHub , Travis CI , Terraform , Docker , Serverless

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      How developers use Amazon Kinesis and Firehose.io
      Avatar of Luca Bianchi
      Luca Bianchi uses Amazon KinesisAmazon Kinesis

      Fast data stream maanagement hiding complexity

      Avatar of KASA FIK s.r.o.
      KASA FIK s.r.o. uses Amazon KinesisAmazon Kinesis

      Event streaming

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