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the current state of your data, from inventory levels to stock trade frequencies, rather than having to wait for an out-of-date report.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

Advice on Amazon Kinesis and Amazon SQS
MITHIRIDI PRASANTH
Software Engineer at LightMetrics · | 4 upvotes · 102.5K views
Needs advice
on
Amazon SQSAmazon SQS
and
Amazon MQAmazon MQ
in

I want to schedule a message. Amazon SQS provides a delay of 15 minutes, but I want it in some hours.

Example: Let's say a Message1 is consumed by a consumer A but somehow it failed inside the consumer. I would want to put it in a queue and retry after 4hrs. Can I do this in Amazon MQ? I have seen in some Amazon MQ videos saying scheduling messages can be done. But, I'm not sure how.

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Replies (1)
Andres Paredes
Lead Senior Software Engineer at InTouch Technology · | 1 upvotes · 78K views
Recommends
Amazon SQSAmazon SQS

Mithiridi, I believe you are talking about two different things. 1. If you need to process messages with delays of more 15m or at specific times, it's not a good idea to use queues, independently of tool SQM, Rabbit or Amazon MQ. you should considerer another approach using a scheduled job. 2. For dead queues and policy retries RabbitMQ, for example, doesn't support your use case. https://medium.com/@kiennguyen88/rabbitmq-delay-retry-schedule-with-dead-letter-exchange-31fb25a440fc I'm not sure if that is possible SNS/SQS support, they have a maximum delay for delivery (maxDelayTarget) in seconds but it's not clear the number. You can check this out: https://docs.aws.amazon.com/sns/latest/dg/sns-message-delivery-retries.html

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Pros of Amazon Kinesis
Pros of Amazon SQS
  • 7
    Scalable
  • 4
    Cons
  • 60
    Easy to use, reliable
  • 39
    Low cost
  • 27
    Simple
  • 13
    Doesn't need to maintain it
  • 8
    It is Serverless
  • 4
    Has a max message size (currently 256K)
  • 3
    Easy to configure with Terraform
  • 3
    Triggers Lambda
  • 3
    Delayed delivery upto 15 mins only
  • 3
    Delayed delivery upto 12 hours
  • 1
    JMS compliant
  • 1
    Support for retry and dead letter queue
  • 1
    D

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Cons of Amazon Kinesis
Cons of Amazon SQS
  • 2
    Cost
  • 2
    Has a max message size (currently 256K)
  • 2
    Proprietary
  • 2
    Difficult to configure
  • 1
    Has a maximum 15 minutes of delayed messages only

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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.

Need advice about which tool to choose?Ask the StackShare community!

What companies use Amazon Kinesis?
What companies use Amazon SQS?
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What tools integrate with Amazon Kinesis?
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Jul 2 2019 at 9:34PM

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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.
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.
Firehose.io
Firehose is both a Rack application and JavaScript library that makes building real-time web applications possible.
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.
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