Amazon Kinesis Firehose聽vs聽AWS Snowball Edge

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Amazon Kinesis Firehose
Amazon Kinesis Firehose

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AWS Snowball Edge

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Amazon Kinesis Firehose vs AWS Snowball Edge: What are the differences?

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; AWS Snowball Edge: Petabyte-scale data transport with on-board storage and compute. AWS Snowball Edge is a 100TB data transfer device with on-board storage and compute capabilities. You can use Snowball Edge to move large amounts of data into and out of AWS, as a temporary storage tier for large local datasets, or to support local workloads in remote or offline locations.

Amazon Kinesis Firehose belongs to "Real-time Data Processing" category of the tech stack, while AWS Snowball Edge can be primarily classified under "Data Transfer".

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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 AWS Snowball Edge?

AWS Snowball Edge is a 100TB data transfer device with on-board storage and compute capabilities. You can use Snowball Edge to move large amounts of data into and out of AWS, as a temporary storage tier for large local datasets, or to support local workloads in remote or offline locations.
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            What are some alternatives to Amazon Kinesis Firehose and AWS Snowball Edge?
            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.
            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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            Decisions about Amazon Kinesis Firehose and AWS Snowball Edge
            Praveen Mooli
            Praveen Mooli
            Technical Leader at Taylor and Francis | 11 upvotes 155K views
            MongoDB Atlas
            MongoDB Atlas
            Amazon S3
            Amazon S3
            Amazon DynamoDB
            Amazon DynamoDB
            Amazon RDS
            Amazon RDS
            Serverless
            Serverless
            Docker
            Docker
            Terraform
            Terraform
            Travis CI
            Travis CI
            GitHub
            GitHub
            RxJS
            RxJS
            Angular 2
            Angular 2
            AWS Lambda
            AWS Lambda
            Amazon SQS
            Amazon SQS
            Amazon SNS
            Amazon SNS
            Amazon Kinesis Firehose
            Amazon Kinesis Firehose
            Amazon Kinesis
            Amazon Kinesis
            Flask
            Flask
            Python
            Python
            ExpressJS
            ExpressJS
            Node.js
            Node.js
            Spring Boot
            Spring Boot
            Java
            Java
            #Data
            #Devops
            #Webapps
            #Eventsourcingframework
            #Microservices
            #Backend

            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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