Amazon Kinesis Firehose聽vs聽Google Cloud Dataflow

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Amazon Kinesis Firehose
Amazon Kinesis Firehose

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Google Cloud Dataflow
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Amazon Kinesis Firehose vs Google Cloud Dataflow: What are the differences?

What is Amazon Kinesis Firehose? Simple and Scalable Data Ingestion. 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.

What is Google Cloud Dataflow? A fully-managed cloud service and programming model for batch and streaming big data processing. Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.

Amazon Kinesis Firehose and Google Cloud Dataflow can be primarily classified as "Real-time Data Processing" tools.

Some of the features offered by Amazon Kinesis Firehose are:

  • Easy-to-Use
  • Integrated with AWS Data Stores
  • Automatic Elasticity

On the other hand, Google Cloud Dataflow provides the following key features:

  • Fully managed
  • Combines batch and streaming with a single API
  • High performance with automatic workload rebalancing Open source SDK

According to the StackShare community, Google Cloud Dataflow has a broader approval, being mentioned in 32 company stacks & 8 developers stacks; compared to Amazon Kinesis Firehose, which is listed in 32 company stacks and 7 developer stacks.

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What is 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.

What is Google Cloud Dataflow?

Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.
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          What are some alternatives to Amazon Kinesis Firehose and Google Cloud Dataflow?
          Stream
          Stream allows you to build scalable feeds, activity streams, and chat. Stream鈥檚 simple, yet powerful API鈥檚 and SDKs are used by some of the largest and most popular applications for feeds and chat. SDKs available for most popular languages.
          Kafka
          Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
          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.
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          Decisions about Amazon Kinesis Firehose and Google Cloud Dataflow
          Praveen Mooli
          Praveen Mooli
          Engineering Manager at Taylor and Francis | 12 upvotes 541.5K 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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