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

Amazon Kinesis

723
600
+ 1
9
Kafka

23.2K
21.8K
+ 1
607
Add tool

Amazon Kinesis vs Kafka: What are the differences?

Introduction

This Markdown code presents the key differences between Amazon Kinesis and Kafka, focusing on specific aspects that distinguish these two popular streaming platforms.

  1. Scalability: Amazon Kinesis is a fully managed service offered by Amazon Web Services, providing seamless scalability to handle large amounts of streaming data. It automatically scales up or down as per the demand, allowing users to process terabytes of data per hour without worrying about infrastructure management or performance. Kafka, on the other hand, offers horizontal scalability by allowing developers to add more machines or servers to the Kafka cluster, providing high throughput and fault tolerance.

  2. Ease of Use: Amazon Kinesis simplifies the streaming data processing by managing the underlying infrastructure and abstracting the complexities of setup and administration. It provides an intuitive web interface and a wide range of SDKs, making it easier for developers to integrate and start streaming data quickly. Kafka, on the other hand, requires manual configuration and setup, which can be more intimidating for beginners. It offers a command-line interface and comprehensive documentation for configuration and deployment.

  3. Data Retention: Amazon Kinesis retains streaming data for a maximum of 7 days by default, which can be extended up to 365 days with additional configuration. This makes it suitable for use cases that require real-time analytics or batch processing within a limited time frame. Kafka, on the other hand, allows users to configure their own retention policy since it is designed as a distributed commit log. It gives users more flexibility to store data for as long as required, making it suitable for use cases that need long-term data retention.

  4. Integration with Ecosystem: Amazon Kinesis is natively integrated with various AWS services, enabling seamless data ingestion, processing, and analysis within the AWS ecosystem. It integrates well with services like Amazon S3, Amazon Redshift, Amazon Elasticsearch, and more, allowing users to build end-to-end data pipelines easily. Kafka, on the other hand, offers broader integration options as an open-source platform. It has numerous connectors and frameworks available, allowing users to integrate with various third-party tools, databases, and cloud platforms beyond AWS.

  5. Delivery Guarantees: Amazon Kinesis guarantees that data will be delivered in the order it was received within a shard, providing strict ordering guarantees for stream processing applications. It also ensures durability by storing data redundantly across multiple availability zones. Kafka guarantees both ordering and fault tolerance at the partition level. It allows users to choose the desired trade-offs between latency, throughput, and durability based on configuration settings.

  6. Community Support: Amazon Kinesis being an AWS-managed service, users can leverage the extensive support and documentation provided by Amazon Web Services. Any technical issues or inquiries can be addressed through AWS support channels. Kafka, on the other hand, benefits from a vast and active open-source community. It has a large number of contributors, forums, and resources available, providing a wealth of community support and knowledge-sharing opportunities.

In summary, Amazon Kinesis is a fully managed service suitable for users seeking simplified setup, tight integration with AWS ecosystem, and a streamlined streaming experience. Kafka, on the other hand, offers more customization options, greater scalability control, and broader integration possibilities, making it a preferred choice for users with specific requirements and use cases.

Advice on Amazon Kinesis and Kafka
Needs advice
on
KafkaKafkaRabbitMQRabbitMQ
and
RedisRedis

We are going to develop a microservices-based application. It consists of AngularJS, ASP.NET Core, and MSSQL.

We have 3 types of microservices. Emailservice, Filemanagementservice, Filevalidationservice

I am a beginner in microservices. But I have read about RabbitMQ, but come to know that there are Redis and Kafka also in the market. So, I want to know which is best.

See more
Replies (4)
Maheedhar Aluri
Recommends
on
KafkaKafka

Kafka is an Enterprise Messaging Framework whereas Redis is an Enterprise Cache Broker, in-memory database and high performance database.Both are having their own advantages, but they are different in usage and implementation. Now if you are creating microservices check the user consumption volumes, its generating logs, scalability, systems to be integrated and so on. I feel for your scenario initially you can go with KAFKA bu as the throughput, consumption and other factors are scaling then gradually you can add Redis accordingly.

See more
Recommends
on
AngularAngular

I first recommend that you choose Angular over AngularJS if you are starting something new. AngularJs is no longer getting enhancements, but perhaps you meant Angular. Regarding microservices, I recommend considering microservices when you have different development teams for each service that may want to use different programming languages and backend data stores. If it is all the same team, same code language, and same data store I would not use microservices. I might use a message queue, in which case RabbitMQ is a good one. But you may also be able to simply write your own in which you write a record in a table in MSSQL and one of your services reads the record from the table and processes it. The most challenging part of doing it yourself is writing a service that does a good job of reading the queue without reading the same message multiple times or missing a message; and that is where RabbitMQ can help.

See more
Amit Mor
Software Architect at Payoneer · | 3 upvotes · 784.6K views
Recommends
on
KafkaKafka

I think something is missing here and you should consider answering it to yourself. You are building a couple of services. Why are you considering event-sourcing architecture using Message Brokers such as the above? Won't a simple REST service based arch suffice? Read about CQRS and the problems it entails (state vs command impedance for example). Do you need Pub/Sub or Push/Pull? Is queuing of messages enough or would you need querying or filtering of messages before consumption? Also, someone would have to manage these brokers (unless using managed, cloud provider based solution), automate their deployment, someone would need to take care of backups, clustering if needed, disaster recovery, etc. I have a good past experience in terms of manageability/devops of the above options with Kafka and Redis, not so much with RabbitMQ. Both are very performant. But also note that Redis is not a pure message broker (at time of writing) but more of a general purpose in-memory key-value store. Kafka nowadays is much more than a distributed message broker. Long story short. In my taste, you should go with a minialistic approach and try to avoid either of them if you can, especially if your architecture does not fall nicely into event sourcing. If not I'd examine Kafka. If you need more capabilities than I'd consider Redis and use it for all sorts of other things such as a cache.

See more
Recommends
on
NATSNATS

We found that the CNCF landscape is a good advisor when working going into the cloud / microservices space: https://landscape.cncf.io/fullscreen=yes. When choosing a technology one important criteria to me is if it is cloud native or not. Neither Redis, RabbitMQ nor Kafka is cloud native. The try to adapt but will be replaced eventually with technologies that are cloud native.

We have gone with NATS and have never looked back. We haven't spend a single minute on server maintainance in the last year and the setup of a cluster is way too easy. With the new features NATS incorporates now (and the ones still on the roadmap) it is already and will be sooo much mure than Redis, RabbitMQ and Kafka are. It can replace service discovery, load balancing, global multiclusters and failover, etc, etc.

Your thought might be: But I don't need all of that! Well, at the same time it is much more leightweight than Redis, RabbitMQ and especially Kafka.

See more
Pramod Nikam
Co Founder at Usability Designs · | 2 upvotes · 526.9K views
Needs advice
on
Apache ThriftApache ThriftKafkaKafka
and
NSQNSQ

I am looking into IoT World Solution where we have MQTT Broker. This MQTT Broker Sits in one of the Data Center. We are doing a lot of Alert and Alarm related processing on that Data, Currently, we are looking into Solution which can do distributed persistence of log/alert primarily on remote Disk.

Our primary need is to use lightweight where operational complexity and maintenance costs can be significantly reduced. We want to do it on-premise so we are not considering cloud solutions.

We looked into the following alternatives:

Apache Kafka - Great choice but operation and maintenance wise very complex. Rabbit MQ - High availability is the issue, Apache Pulsar - Operational Complexity. NATS - Absence of persistence. Akka Streams - Big learning curve and operational streams.

So we are looking into a lightweight library that can do distributed persistence preferably with publisher and subscriber model. Preferable on JVM stack.

See more
Replies (1)
Naresh Kancharla
Staff Engineer at Nutanix · | 4 upvotes · 524.3K views
Recommends
on
KafkaKafka

Kafka is best fit here. Below are the advantages with Kafka ACLs (Security), Schema (protobuf), Scale, Consumer driven and No single point of failure.

Operational complexity is manageable with open source monitoring tools.

See more
Needs advice
on
KafkaKafka
and
RabbitMQRabbitMQ

Our backend application is sending some external messages to a third party application at the end of each backend (CRUD) API call (from UI) and these external messages take too much extra time (message building, processing, then sent to the third party and log success/failure), UI application has no concern to these extra third party messages.

So currently we are sending these third party messages by creating a new child thread at end of each REST API call so UI application doesn't wait for these extra third party API calls.

I want to integrate Apache Kafka for these extra third party API calls, so I can also retry on failover third party API calls in a queue(currently third party messages are sending from multiple threads at the same time which uses too much processing and resources) and logging, etc.

Question 1: Is this a use case of a message broker?

Question 2: If it is then Kafka vs RabitMQ which is the better?

See more
Replies (4)
Tarun Batra
Senior Software Developer at Okta · | 7 upvotes · 734.8K views
Recommends
on
RabbitMQRabbitMQ

RabbitMQ is great for queuing and retrying. You can send the requests to your backend which will further queue these requests in RabbitMQ (or Kafka, too). The consumer on the other end can take care of processing . For a detailed analysis, check this blog about choosing between Kafka and RabbitMQ.

See more
Trevor Rydalch
Software Engineer at InsideSales.com · | 6 upvotes · 734.6K views
Recommends
on
RabbitMQRabbitMQ

Well, first off, it's good practice to do as little non-UI work on the foreground thread as possible, regardless of whether the requests take a long time. You don't want the UI thread blocked.

This sounds like a good use case for RabbitMQ. Primarily because you don't need each message processed by more than one consumer. If you wanted to process a single message more than once (say for different purposes), then Apache Kafka would be a much better fit as you can have multiple consumer groups consuming from the same topics independently.

Have your API publish messages containing the data necessary for the third-party request to a Rabbit queue and have consumers reading off there. If it fails, you can either retry immediately, or publish to a deadletter queue where you can reprocess them whenever you want (shovel them back into the regular queue).

See more
Recommends
on
RabbitMQRabbitMQ

In my opinion RabbitMQ fits better in your case because you don’t have order in queue. You can process your messages in any order. You don’t need to store the data what you sent. Kafka is a persistent storage like the blockchain. RabbitMQ is a message broker. Kafka is not a good solution for the system with confirmations of the messages delivery.

See more
Guillaume Maka
Full Stack Web Developer · | 2 upvotes · 733.9K views
Recommends
on
RabbitMQRabbitMQ

As far as I understand, Kafka is a like a persisted event state manager where you can plugin various source of data and transform/query them as event via a stream API. Regarding your use case I will consider using RabbitMQ if your intent is to implement service inter-communication kind of thing. RabbitMQ is a good choice for one-one publisher/subscriber (or consumer) and I think you can also have multiple consumers by configuring a fanout exchange. RabbitMQ provide also message retries, message cancellation, durable queue, message requeue, message ACK....

See more
Needs advice
on
KafkaKafkaRabbitMQRabbitMQ
and
RedisRedis

Hello! [Client sends live video frames -> Server computes and responds the result] Web clients send video frames from their webcam then on the back we need to run them through some algorithm and send the result back as a response. Since everything will need to work in a live mode, we want something fast and also suitable for our case (as everyone needs). Currently, we are considering RabbitMQ for the purpose, but recently I have noticed that there is Redis and Kafka too. Could you please help us choose among them or anything more suitable beyond these guys. I think something similar to our product would be people using their webcam to get Snapchat masks on their faces, and the calculated face points are responded on from the server, then the client-side draw the mask on the user's face. I hope this helps. Thank you!

See more
Replies (3)
Jordi Martínez
Senior software architect at Bootloader · | 3 upvotes · 684.1K views
Recommends
on
KafkaKafka

For your use case, the tool that fits more is definitely Kafka. RabbitMQ was not invented to handle data streams, but messages. Plenty of them, of course, but individual messages. Redis is an in-memory database, which is what makes it so fast. Redis recently included features to handle data stream, but it cannot best Kafka on this, or at least not yet. Kafka is not also super fast, it also provides lots of features to help create software to handle those streams.

See more
Recommends
on
RabbitMQRabbitMQ

I've used all of them and Kafka is hard to set up and maintain. Mostly is a Java dinosaur that you can set up and. I've used it with Storm but that is another big dinosaur. Redis is mostly for caching. The queue mechanism is not very scalable for multiple processors. Depending on the speed you need to implement on the reliability I would use RabbitMQ. You can store the frames(if they are too big) somewhere else and just have a link to them. Moving data through any of these will increase cost of transportation. With Rabbit, you can always have multiple consumers and check for redundancy. Hope it clears out your thoughts!

See more
Recommends
on
RabbitMQRabbitMQ

For this kind of use case I would recommend either RabbitMQ or Kafka depending on the needs for scaling, redundancy and how you want to design it.

Kafka's true value comes into play when you need to distribute the streaming load over lot's of resources. If you were passing the video frames directly into the queue then you'd probably want to go with Kafka however if you can just pass a pointer to the frames then RabbitMQ should be fine and will be much simpler to run.

Bear in mind too that Kafka is a persistent log, not just a message bus so any data you feed into it is kept available until it expires (which is configurable). This can be useful if you have multiple clients reading from the queue with their own lifecycle but in your case it doesn't sound like that would be necessary. You could also use a RabbitMQ fanout exchange if you need that in the future.

See more
Decisions about Amazon Kinesis and Kafka
Baran Bartu Demirci
Head of Engineering at Volt Lines · | 2 upvotes · 6K views

As a small startup, maintaining all of our products and services would be difficult. During the growth phase, we preferred to focus on business logic instead of maintenance, thus, we started to work with Amazon Kinesis. Configurations like sharding and auto scaling can be changed easily on ec2 console. Also, it stores your data for up to 24 hours, lovely!

See more
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Amazon Kinesis
Pros of Kafka
  • 9
    Scalable
  • 126
    High-throughput
  • 119
    Distributed
  • 92
    Scalable
  • 86
    High-Performance
  • 66
    Durable
  • 38
    Publish-Subscribe
  • 19
    Simple-to-use
  • 18
    Open source
  • 12
    Written in Scala and java. Runs on JVM
  • 9
    Message broker + Streaming system
  • 4
    KSQL
  • 4
    Avro schema integration
  • 4
    Robust
  • 3
    Suport Multiple clients
  • 2
    Extremely good parallelism constructs
  • 2
    Partioned, replayable log
  • 1
    Simple publisher / multi-subscriber model
  • 1
    Fun
  • 1
    Flexible

Sign up to add or upvote prosMake informed product decisions

Cons of Amazon Kinesis
Cons of Kafka
  • 3
    Cost
  • 32
    Non-Java clients are second-class citizens
  • 29
    Needs Zookeeper
  • 9
    Operational difficulties
  • 5
    Terrible Packaging

Sign up to add or upvote consMake informed product decisions

- 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.

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

What companies use Amazon Kinesis?
What companies use Kafka?
See which teams inside your own company are using Amazon Kinesis or Kafka.
Sign up for StackShare EnterpriseLearn More

Sign up to get full access to all the companiesMake informed product decisions

What tools integrate with Amazon Kinesis?
What tools integrate with Kafka?

Sign up to get full access to all the tool integrationsMake informed product decisions

Blog Posts

Dec 22 2021 at 5:41AM

Pinterest

MySQLKafkaDruid+3
3
575
Amazon S3KafkaZookeeper+5
8
1583
Mar 24 2021 at 12:57PM

Pinterest

GitJenkinsKafka+7
3
2159
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.
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.
See all alternatives