Amazon Kinesis
Amazon Kinesis

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Amazon SQS

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Amazon Kinesis vs Amazon SQS: 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, Amazon SQS is detailed as "Fully managed message queuing service". 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 belongs to "Real-time Data Processing" category of the tech stack, while Amazon SQS can be primarily classified 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the current state of your data, from inventory levels to stock trade frequencies, rather than having to wait for an out-of-date report.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, Amazon SQS provides the following key features:

  • A queue can be created in any region.
  • The message payload can contain up to 256KB of text in any format. Each 64KB ‘chunk’ of payload is billed as 1 request. For example, a single API call with a 256KB payload will be billed as four requests.
  • Messages can be sent, received or deleted in batches of up to 10 messages or 256KB. Batches cost the same amount as single messages, meaning SQS can be even more cost effective for customers that use batching.

Medium, Lyft, and Coursera are some of the popular companies that use Amazon SQS, whereas Amazon Kinesis is used by Instacart, Lyft, and Zillow. Amazon SQS has a broader approval, being mentioned in 384 company stacks & 103 developers stacks; compared to Amazon Kinesis, which is listed in 132 company stacks and 25 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 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.

Want advice about which of these to choose?Ask the StackShare community!

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      What are some alternatives to Amazon Kinesis and Amazon SQS?
      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.
      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’re already using today.
      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 and Amazon SQS
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      How developers use Amazon Kinesis and Amazon SQS
      Avatar of Karma
      Karma uses Amazon SQSAmazon SQS

      In the beginning we thought we wanted to start using something like RabbitMQ or maybe Kafka or maybe ActiveMQ. Back then we only had a few developers and no ops people. That has changed now, but we didn't really look forward to setting up a queuing cluster and making sure that all works.

      What we did instead was we looked at what services Amazon offers to see if we can use those to build our own messaging system within those services. That's basically what we did. We wrote some clients in Ruby that can basically do the entire orchestration for us, and we run all our messaging on both SNS and SQS. Basically what you can do in Amazon services is you can use Amazon Simple Notification Service, so SNS, for creating topics and you can use queues to subscribe to these topics. That's basically all you need for a messaging system. You don't have to worry about scalability at all. That's what really appealed to us.

      Avatar of Brandon Adams
      Brandon Adams uses Amazon SQSAmazon SQS

      This isn't exactly low-latency (10s to 100s of milliseconds), but it has good throughput and a simple API. There is good reliability, and there is no configuration necessary to get up and running. A hosted queue is important when trying to move fast.

      Avatar of Simple Merchant
      Simple Merchant uses Amazon SQSAmazon SQS

      SQS is the bridge between our new Lambda services and our incumbent Rails applications. Extremely easy to use when you're already using other AWS infrastructure.

      Avatar of Olo
      Olo uses Amazon SQSAmazon SQS

      Primary message queue. Enqueueing operations revert to a local file-system-based queue when SQS is unavailable.

      Avatar of IndiTip
      IndiTip uses Amazon SQSAmazon SQS

      I can't afford to lose data if Dynamo throttles my writes, so everything goes into a message queue first.

      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 Amazon SQS cost?