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’re 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:
- 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.
What is Amazon Kinesis Firehose?
What is Google Cloud Dataflow?
Need advice about which tool to choose?Ask the StackShare community!
Why do developers choose Amazon Kinesis Firehose?
Why do developers choose Google Cloud Dataflow?
What are the cons of using Amazon Kinesis Firehose?
What are the cons of using Google Cloud Dataflow?
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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