Amazon Kinesis聽vs聽Google Cloud Dataflow

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

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; 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 and Google Cloud Dataflow can be categorized as "Real-time Data Processing" tools.

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the current state of your data, from inventory levels to stock trade frequencies, rather than having to wait for an out-of-date report.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, 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

Instacart, Lyft, and Zillow are some of the popular companies that use Amazon Kinesis, whereas Google Cloud Dataflow is used by Spotify, Resultados Digitais, and Kapten. Amazon Kinesis has a broader approval, being mentioned in 130 company stacks & 24 developers stacks; compared to Google Cloud Dataflow, which is listed in 32 company stacks and 8 developer stacks.

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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 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 and Google Cloud Dataflow?
        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.
        Firehose.io
        Firehose is both a Rack application and JavaScript library that makes building real-time web applications possible.
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        Decisions about Amazon Kinesis and Google Cloud Dataflow
        Praveen Mooli
        Praveen Mooli
        Engineering Manager at Taylor and Francis | 12 upvotes 553K 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 Google Cloud Dataflow
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        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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