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Amazon Kinesis vs Kafka: What are the differences?

Developers describe Amazon Kinesis as "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. On the other hand, Kafka is detailed as "Distributed, fault tolerant, high throughput pub-sub messaging system". Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

Amazon Kinesis can be classified as a tool in the "Real-time Data Processing" category, while Kafka is grouped under "Message Queue".

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the current state of your data, from inventory levels to stock trade frequencies, rather than having to wait for an out-of-date report.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

  • Written at LinkedIn in Scala
  • Used by LinkedIn to offload processing of all page and other views
  • Defaults to using persistence, uses OS disk cache for hot data (has higher throughput then any of the above having persistence enabled)

Kafka is an open source tool with 12.7K GitHub stars and 6.81K GitHub forks. Here's a link to Kafka's open source repository on GitHub.

Uber Technologies, Spotify, and Slack are some of the popular companies that use Kafka, whereas Amazon Kinesis is used by Instacart, Lyft, and Zillow. Kafka has a broader approval, being mentioned in 509 company stacks & 470 developers stacks; compared to Amazon Kinesis, which is listed in 132 company stacks and 25 developer stacks.

- 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 Kafka?

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
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      What are some alternatives to Amazon Kinesis and Kafka?
      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.
      Firehose.io
      Firehose is both a Rack application and JavaScript library that makes building real-time web applications possible.
      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 Kafka
      Adam Rabinovitch
      Adam Rabinovitch
      Global Technical Recruiting Lead & Engineering Evangelist at Beamery | 3 upvotes 156.8K views
      atBeameryBeamery
      Kafka
      Kafka
      Redis
      Redis
      Elasticsearch
      Elasticsearch
      MongoDB
      MongoDB
      RabbitMQ
      RabbitMQ
      Go
      Go
      Node.js
      Node.js
      Kubernetes
      Kubernetes
      #Microservices

      Beamery runs a #microservices architecture in the backend on top of Google Cloud with Kubernetes There are a 100+ different microservice split between Node.js and Go . Data flows between the microservices over REST and gRPC and passes through Kafka RabbitMQ as a message bus. Beamery stores data in MongoDB with near-realtime replication to Elasticsearch . In addition, Beamery uses Redis for various memory-optimized tasks.

      See more
      Conor Myhrvold
      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber | 4 upvotes 95.9K views
      atUber TechnologiesUber Technologies
      Kafka Manager
      Kafka Manager
      Kafka
      Kafka
      GitHub
      GitHub
      Apache Spark
      Apache Spark
      Hadoop
      Hadoop

      Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

      Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

      https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

      (Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

      See more
      Roman Bulgakov
      Roman Bulgakov
      Senior Back-End Developer, Software Architect at Chemondis GmbH | 3 upvotes 10.5K views
      Kafka
      Kafka

      I use Kafka because it has almost infinite scaleability in terms of processing events (could be scaled to process hundreds of thousands of events), great monitoring (all sorts of metrics are exposed via JMX).

      Downsides of using Kafka are: - you have to deal with Zookeeper - you have to implement advanced routing yourself (compared to RabbitMQ it has no advanced routing)

      See more
      RabbitMQ
      RabbitMQ
      Kafka
      Kafka

      The question for which Message Queue to use mentioned "availability, distributed, scalability, and monitoring". I don't think that this excludes many options already. I does not sound like you would take advantage of Kafka's strengths (replayability, based on an even sourcing architecture). You could pick one of the AMQP options.

      I would recommend the RabbitMQ message broker, which not only implements the AMQP standard 0.9.1 (it can support 1.x or other protocols as well) but has also several very useful extensions built in. It ticks the boxes you mentioned and on top you will get a very flexible system, that allows you to build the architecture, pick the options and trade-offs that suite your case best.

      For more information about RabbitMQ, please have a look at the linked markdown I assembled. The second half explains many configuration options. It also contains links to managed hosting and to libraries (though it is missing Python's - which should be Puka, I assume).

      See more
      Fr茅d茅ric MARAND
      Fr茅d茅ric MARAND
      Core Developer at OSInet | 2 upvotes 88K views
      atOSInetOSInet
      RabbitMQ
      RabbitMQ
      Beanstalkd
      Beanstalkd
      Kafka
      Kafka

      I used Kafka originally because it was mandated as part of the top-level IT requirements at a Fortune 500 client. What I found was that it was orders of magnitude more complex ...and powerful than my daily Beanstalkd , and far more flexible, resilient, and manageable than RabbitMQ.

      So for any case where utmost flexibility and resilience are part of the deal, I would use Kafka again. But due to the complexities involved, for any time where this level of scalability is not required, I would probably just use Beanstalkd for its simplicity.

      I tend to find RabbitMQ to be in an uncomfortable middle place between these two extremities.

      See more
      John Kodumal
      John Kodumal
      CTO at LaunchDarkly | 15 upvotes 97.1K views
      atLaunchDarklyLaunchDarkly
      Kafka
      Kafka
      Amazon Kinesis
      Amazon Kinesis
      Redis
      Redis
      Amazon EC2
      Amazon EC2
      Amazon ElastiCache
      Amazon ElastiCache
      Consul
      Consul
      Patroni
      Patroni
      TimescaleDB
      TimescaleDB
      PostgreSQL
      PostgreSQL
      Amazon RDS
      Amazon RDS

      As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data鈥攖his is made HA with the use of Patroni and Consul.

      We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

      See more
      Praveen Mooli
      Praveen Mooli
      Technical Leader at Taylor and Francis | 11 upvotes 92.5K 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

      See more
      Interest over time
      Reviews of Amazon Kinesis and Kafka
      No reviews found
      How developers use Amazon Kinesis and Kafka
      Avatar of Pinterest
      Pinterest uses KafkaKafka

      http://media.tumblr.com/d319bd2624d20c8a81f77127d3c878d0/tumblr_inline_nanyv6GCKl1s1gqll.png

      Front-end messages are logged to Kafka by our API and application servers. We have batch processing (on the middle-left) and real-time processing (on the middle-right) pipelines to process the experiment data. For batch processing, after daily raw log get to s3, we start our nightly experiment workflow to figure out experiment users groups and experiment metrics. We use our in-house workflow management system Pinball to manage the dependencies of all these MapReduce jobs.

      Avatar of Coolfront Technologies
      Coolfront Technologies uses KafkaKafka

      Building out real-time streaming server to present data insights to Coolfront Mobile customers and internal sales and marketing teams.

      Avatar of ShareThis
      ShareThis uses KafkaKafka

      We are using Kafka as a message queue to process our widget logs.

      Avatar of Christopher Davison
      Christopher Davison uses KafkaKafka

      Used for communications and triggering jobs across ETL systems

      Avatar of theskyinflames
      theskyinflames uses KafkaKafka

      Used as a integration middleware by messaging interchanging.

      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

      How much does Amazon Kinesis cost?
      How much does Kafka cost?
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