In this article I will help to choose between AWS Kinesis vs Kafka with a detailed features comparison and costs analysis. Similar to Kafka, there are plenty of language-specific clients available for working with Kinesis including Java, Scala, Ruby, Javascript (Node), etc. An interesting aspect of Kafka and Kinesis lately is the use of stream processing. When the TTL is reached the data will expire from the stream. Join thousands of aspiring developers and DevOps enthusiastsย�Take a look, Mount Your AWS EFS Volume Into AWS Lambda With the Serverless Framework, Docker/Kubernetes for the Decision Makers, 10 habits I borrowed from python that I use in React(Part I), ๐Ÿ‘ป How I Ghosted My Ex-Boyfriend Hugo and Stole His Web Apps ๐Ÿ‘ป, Getting Started with Spannables on Android, The Easy Way to Recover From Burnout as a Developer. Both Apache Kafka and AWS Kinesis Data Streams are good choices for real-time data streaming platforms. AWS Kinesis: Kinesis is similar to Kafka in many ways. Apache Kafka is most compared with ActiveMQ, PubSub+ Event Broker, VMware RabbitMQ, Amazon SQS and Red Hat AMQ, whereas IBM MQ is most compared with VMware RabbitMQ, ActiveMQ, PubSub+ Event Broker, Anypoint MQ and TIBCO Enterprise Message Service. If you don’t have need for scale, strict ordering, hybrid cloud architectures, exactly-once semantics, it can be a perfectly fine choice. See our Apache Kafka vs. IBM MQ report. Amazon Kinesis. AWS Kinesis was shining on our AWS console waiting to be picked up. Please check Amazon for the latest Kinesis Data Streams pricing. Amazon Kinesis has a built-in cross replication while Kafka requires configuration to be performed on your own. For the data flowing through Kafka or Kinesis, Kinesis refers to this as a “Data Record” whereas Kafka will refer to this as an Event or a Message interchangeably. Throughput Comparison kinesis vs Kafka (Single to Multiple Producer) Conclusion. Apache Kafka is an open source distributed publish subscribe system. Let’s start with Kinesis. Distributed log technologies such as Apache Kafka, Amazon Kinesis, Microsoft Event Hubs and Google Pub/Sub have matured in the last few years, and have added some great new types of solutions when moving data around for certain use cases.According to IT Jobs Watch, job vacancies for projects with Apache Kafka have increased by 112% since last year, whereas more traditional point to point brokers haven’t faired so well. Introduction. Amazon SNS with SQS is also similar to Google Pubsub (SNS provides the fanout and SQS provides the queueing). Kinesis doesn’t offer an on-premises solution. The Connect API allows implementing connectors that continually pull from some source system or application into Kafka or push from Kafka into some sink system or application. Kafka vs. Kinesis. Also, the extra effort by the user to configure and scale according to requirements such as high availability, durability, and recovery. The key advantage of AWS Kinesis is its deep integration into AWS ecosystem. Keep an eye on http://confluent.io. Data can be automatically brokered by the SPS to available partitions or explicitly set by the producer. Kafka Vs Kinesis are both effectively amazing. Apache Kafka is an open-source stream-processing software platform developed by Linkedin, donated to Apache Software Foundation, and written in Scala and Java. I mean, I’m thinking we could write their own or use Spark, but is there a direct comparison to Kafka Streams / KSQL in Kinesis? Please let me know. As briefly mentioned above, stream processing between the two options appears to be quite different. It will also probably be cheaper at first, since they have a good pay as you go model, but the cost will not scale as well, so you have to think about that. Cross-replication is not mandatory, and you should consider doing so only if you need it. With them you can only write at the end of the log or you can read entries sequentially. For example, a multi-stage design might include raw input data consumed from Kafka topics in stage 1. Kinesis is known to be incredibly fast, reliable and easy to operate. Kinesis will take you a couple of hours max. Integration between systems is assisted by Kafka clients in a variety of languages including Java, Scala, Ruby, Python, Go, Rust, Node.js, etc. The producers put records (data ingestion) into KDS. The stream data is stored on a partition. If your organization lacks Apache Kafka experts and/or human support, then choosing a fully-managed AWS Kinesis service will let you focus on the development. KDS has no upfront cost, and you only pay for the resources you use (e.g., $0.015 per Shard Hour.) Kafka - Distributed, fault tolerant, high throughput pub-sub messaging system. Cross-replication is the idea of syncing data across logical or physical data centers. AWS has several fully managed messaging services: Kinesis Streams being the closest equivalent to Apache Kafka, simpler solutions like SNS and SQS seem also do the job, especially when you combine the two. Consumers can subscribe to topics. The choice, as I found out, was not an easy one and had a lot of factors to be taken into consideration and the winner could surprise you. Engineers sold on the value proposition of Kafka and Software-as-a-Service or perhaps more specifically Platform-as-a-Service have options besides Kinesis or Amazon Web Services. In this article, I will compare Apache Kafka and AWS Kinesis. greater than 7 days), scale, stream processing implementation options, pre-built connectors or frameworks for building custom integrations, exactly-once semantics, and transactions. Keep an eye on https://confluent.io. Then, in stage 3, the data is published to new topics for further consumption or follow-up processing during a later stage. 1 month ago. In Kinesis, this is called a shard while Kafka calls it a partition. Amazon Kinesis has a built-in cross replication while Kafka requires configuration to be performed on your own. Amazon AWS Kinesis is a managed version of Kafka whereas I think of Google Pubsub as a managed version of Rabbit MQ. Scaling up. Integration between systems is assisted by Kafka clients in a variety of languages including Java, Scala, Ruby, Python, Go, Rust, Node.js, etc. Performance: Works with the huge volume of real-time data streams. As a result of our customer engagements, we decided to share our findings in our Apache Kafka vs. Amazon Kinesis whitepaper. In Kafka, data is stored in partitions. The Streams API allows transforming streams of data from input topics to output topics. If you’re already using AWS or you’re looking to move to AWS, that isn’t an issue. The difference is primarily that Kinesis is a “serverless” bus where you’re just paying for the data volume that you pump through it. You can build your applications using either Kinesis Data Analytics, Kinesis API or Kinesis Client Library (KCL). Kinesis is more directly the comparable product. Kafka vs Amazon Kinesis – How do they compare? Amazon Kinesis vs Amazon SQS. Like many of the offerings from Amazon Web Services, Amazon Kinesis software is modeled after an existing Open Source system. However, Kafka requires some human support to install and manage the clusters. Kafka vs Kinesis often comes up. Data records are composed of a sequence number, a partition key, and a data blob (up to 1 MB), which is an immutable sequence of bytes. And as it’s in AWS, it’s production-worthy from the start. How would you do that? Both options have the construct of Consumers and Producers. The consumers get records from Kinesis Data Streams and process them. It is known to be incredibly fast, reliable, and easy to operate. Both Apache Kafka and AWS Kinesis Data Streams are good choices for real-time data streaming platforms. AWS Kinesis Data Streams vs Kinesis Data Firehose Kinesis acts as a highly available conduit to stream messages between data producers and data consumers. [Kafka] [Kinesis] Kafka Connect Kafka-rest Kafka-Pixy Kastle AWS API Gateway HTTP API ETL ETL OSS •Kafka Streams •PipelineDB AWS •Kinesis Analytics 7 11. RabbitMQ - Open source multiprotocol messaging broker An interesting aspect of Kafka and Kinesis lately is the use in stream processing. With them you can only write at the end of the log or you can read entries sequentially. Using that example as the basis, the Kinesis implementation of our audio example ingest followed nicely. *** Updated Spring 2020 *** Since this original post, AWS has released MSK. You can have one or many partitions on a stream. Kinesis, unlike Flume and Kafka, only provides example implementations, there are no default producers available. In this post, we summarize some of the whitepaper’s important takeaways. Thomas Schreiter (now a Data Engineer at Microsoft/Yammer) discusses his project of comparing two ingestion technologies: Open source Kafka and AWS Kinesis. Apache Kafka vs. Amazon Kinesis. AWS Glue maybe? Ongoing ops (human costs) It also might be worth adding that there can be a big difference between the ongoing burden of running your own infrastructure vs. paying AWS to do it … Since this original post, AWS has released MSK. I was tasked with a project that involved choosing between AWS Kinesis vs Kafka. Let’s start with Kinesis. With Kinesis you pay for use, by buying read and write units. [Kafka] [Kinesis] 6 8. Kafka is famous but can be “Kafkaesque” to maintain in production. So, if you can live with vendor-lockin and limited scalability, latency, SLAs and cost, then it might be the right choice for you. Engineers sold on the value proposition of Kafka and Software-as-a-Service or perhaps more specifically Platform-as-a-Service have options besides Kinesis or Amazon Web Services. Apache Kafka was developed by the fine folks over at LinkedIn and works like a distributed tracing service despite being designed for logging. Amazon Kinesis has four capabilities: Kinesis Video Streams, Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics. If you need to keep messages for more than 7 days with no limitation on message size per blob, Apache Kafka should be your choice. Kafka allows specifying either maximum retention period or maximum retention size of all records. The Kafka-Kinesis-Connector is a connector to be used with Kafka Connect to publish messages from Kafka to Amazon Kinesis Streams or Amazon Kinesis Firehose.. Kafka-Kinesis-Connector for Firehose is used to publish messages from Kafka to one of the following destinations: Amazon S3, Amazon Redshift, or Amazon Elasticsearch Service and in turn enabling … A Kinesis data Stream a set of shards. In this case, Kinesis is appears to be modeled after a combination of pub/sub solutions like RabbitMQ and ActiveMQ with regards to the maximum retention period of 7 days and Kafka in other ways such as sharding. When designing Workiva’s durable messaging system we took a hard look at using Amazon’s Kinesis as the message storage and delivery mechanism. For an in-depth analysis of the two solutions in terms of core concepts, architecture, cost analysis, and the application API differences, see the Apache Kafka vs. Amazon Kinesis whitepaper. In stage 2, data is consumed and then aggregated, enriched, or otherwise transformed. The canonical example of the importance of ordering is bank or inventory scenarios. The AdminClient API allows managing and inspecting topics, brokers, and other Kafka objects. Both Apache Kafka and AWS Kinesis Data Streams are good choices for real-time data streaming platforms. Handles high throughput for both publishing and subscribing, Scalability: Highly scales distributed systems with no downtime in all four dimensions: producers, processors, consumers, and connectors, Fault tolerance: Handles failures with the masters and databases with zero downtime and zero data loss, Data Transformation: Offers provisions for deriving new data streams using the data streams from producers, Durability: Uses Distributed commit logs to support messages persisting on disk, Replication: Replicates the messages across the clusters to support multiple subscribers. Kafka has the following feature for real-time streams of data collection and big data real-time analytics: As a result, Kafka aims to be scalable, durable, fault-tolerant and distributed. Like Apache Kafka, Amazon Kinesis is also a publish and subscribe messaging solution, however, it is offered as a managed service in the AWS cloud, and unlike Kafka cannot be run on-premise. Systems like Apache Kafka and AWS Kinesis were built to handle petabytes of data. Let’s consider that for a moment. Both Kafka and Kinesis are often utilized as an integration system in enterprise environments similar to traditional message pub/sub systems. AWS Kinesis is catching up in terms of overall performance regarding throughput and events processing. Fully managed: Kinesis is fully managed and runs your streaming applications without requiring you to manage any infrastructure, Scalability: Handle any amount of streaming data and process data from hundreds of thousands of sources with very low latencies. Stavros Sotiropoulos LinkedIn. AWS Kinesis. When an SPS accepts data from a producer the SPS stores the data with a TTL on a stream. Kafka can run on a cluster of brokers with partitions split across cluster nodes. Recently, I got the opportunity to work on both the Streaming Services. But you cannot remove or update entries, nor add new ones in the middle of the log. Amazon MSK provides multiple levels of security for your Apache Kafka clusters including VPC network isolation, AWS IAM for control-plane API authorization, encryption at rest, TLS encryption in-transit, TLS based certificate authentication, SASL/SCRAM authentication secured by AWS Secrets Manager, and supports Apache Kafka Access Control Lists (ACLs) for data-plane authorization. Hope this helps, let me know if I missed anything or if you’d like more detail in a particular area. I’ll make updates to the content below, but let me know if any questions or concerns. Like many of the offerings from Amazon Web Services, Amazon Kinesis software is modeled after an existing Open Source system. APIs allow producers to publish data streams to topics. Kinesis, created by Amazon and hosted on Amazon Web Services (AWS), prides itself on real-time message processing for hundreds of gigabytes of data from thousands of data sources. APIs allow producers to publish data streams to topics. Each shard has a sequence of data records. It is modeled after Apache Kafka. Also, since the original post, Kinesis has been separated into multiple “services” such as Kinesis Video Streams, Data Streams, Data Firehose, and Data Analytics. Kinesis does not seem to have this capability yet, but AWS EventBridge Schema Registry appears to be coming soon at the time of this writing. Amazon Web Services Messaging System: SNS vs SQS vs Kinesis; ... Kinesis. The Kinesis Data Streams can collect and process large streams of data records in real time as same as Apache Kafka. The ordering of a product shipping event compared to available product inventory matters. The ordering of credits and debits matters. This demo also allows you to evaluate … Other use cases include website activity tracking for a range of use cases including real-time processing or loading into Hadoop or analytic data warehousing systems for offline processing and reporting. Yes, of course, you could write custom Consumer code, but you could also use an off-the-shelf solution as well. or loading into Hadoop or analytic data warehousing systems from a variety of data sources for possible batch processing and reporting. I think this tells us everything we need to know about Kafka vs Kinesis. Common use cases include website activity tracking for real-time monitoring, recommendations, etc. When creating a cloud application you may want to follow a distributed architecture, and when it comes to creating a message-based service for your application, AWS offers two solutions, the Kinesis stream and the SQS Queue. More and more applications and enterprises are building architectures which include processing pipelines consisting of multiple stages. Chant it with me now, Your email address will not be published. To join our community Slack ๐Ÿ—ฃ๏ธ and read our weekly Faun topics ๐Ÿ—ž๏ธ, click hereโฌ‡, Mediumโ€™s largest and most followed independent DevOps publication. [Kafka] [Kinesis] 6 9. As Datapipe’s data and analytics consultants, we are frequently asked by customers to help pick the right solution for them. Share! Share! The high-level architecture on Kinesis Data Streams: Kinesis Data Streams has the following benefits: As a result, Kinesis Data Streams is massively scalable and durable, allowing rapid and continuous data intake and aggregation; however, there is a cost for a fully managed service. Elasticity: Scale the stream up or down, so the data records never lose before they expire, Fault tolerance: The Kinesis Client Library enables fault-tolerant consumption of data from streams and provides scaling support for Kinesis Data Streams applications, Security: Data can be secured at-rest by using server-side encryption and AWS KMS master keys on sensitive data within Kinesis Data Streams. Cross-replication is the idea of syncing data across logical or physical data centers. However, Apache Kafka requires extra effort to set up, manage, and support. If you don’t have a need for certain pre-built connectors compared to Kafka Connect or stream processing with Kafka Streams / KSQL, it can also be a perfectly fine choice. Featured image credit https://flic.kr/p/7XWaia, Share! Amazon SQS - Fully managed message queuing service. Apache Kafka. It is a fully managed service that integrates really well with other AWS services. Access data privately via your Amazon Virtual Private Cloud (VPC). In Kinesis, data is stored in shards. In this case, Kinesis is modeled after Apache Kafka. The question of Kafka vs Kinesis often comes up. And I donโ€™t agree with them totally. In stage 2, data is consumed and then aggregated, enriched, or otherwise transformed. Letโ€™s focus on Kinesis Data Streams(KDS). Iโ€™ll try my best to explain the core concepts of both the bigshots. When you have multiple consumers for the same queue in an SQS setup, the messages will … It enables you to process and analyze data as it arrives and responds instantly instead of having to wait until all your data is collected before the processing can begin. ... One big difference between Kafka vs. For example, a multi-stage design might include raw input data consumed from Kafka topics in stage 1. Cloud Pub/Sub is that Cloud Pub/Sub is fully managed for you. I have heard people saying that kinesis is just a rebranding of Apacheโ€™s Kafka. With Kinesis data can be analyzed by lambda before it gets sent to S3 or RedShift. A few of the Kafka ecosystem components were mentioned above such as Kafka Connect and Kafka Streams. AWS provides Kinesis Producer Library (KPL) to simplify producer application development and to achieve high write throughput to a Kinesis data stream. Advantage: Kinesis, by a mile. This makes it easy to scale and process incoming information. Similar to Kafka, there are plenty of language-specific clients available including Java, Scala, Ruby, Javascript (Node), etc. Partitions incr… Your email address will not be published. The Producer API allows applications to send streams of data to topics in the Kafka cluster. Example: you’d like to land messages from Kafka or Kinesis into ElasticSearch. I think this tells us everything we need to know about Kafka vs Kinesis. And believe me, both are Awesome but it depends on your use case and needs. We decided to do some due diligence against a 3 node Kafka cluster that we setup on m1.large instances. Kafka guarantees the order of messages in partitions while Kinesis does not. The Kinesis Producer continuously pushes data to Kinesis Streams. Apache Kafka is an open-source stream-processing software platform developed by Linkedin, donated to Apache Software Foundation, and written in Scala and Java. A topic is a partitioned log of records with each partition being ordered and immutable. The Consumer API allows applications to read streams of data from topics in the Kafka cluster. A good SPS is designed to scale very large and consume lots of data. Resources for Data Engineers and Data Architects. Durability: Kinesis Data Streams application can start consuming the data from the stream almost immediately after the data is added. AWS Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application. If you need to keep messages for more than 7 days with no limitation on message size per blob, Apache Kafka should be your choice. The default retention period is seven days, but it can even be infinite if the log compaction feature is enabled. The question of Kafka vs Kinesis often comes up. I believe an attempt for the equivalent of pre-built integration for Kinesis is Kinesis Data Firehose. I’m not sure if there is an equivalent of Kafka Streams / KSQL for Kinesis. Conclusion. Key technical components in the comparisons include ordering, retention period (i.e. [Kafka] [Kinesis] Kafka Connect Kafka-rest Kafka-Pixy Kastle AWS API Gateway HTTP API ETL ETL 7 10. At first glance, Kinesis has a feature set that looks like it can solve any problem: it can store terabytes of data, it can replay old messages, and it can support multiple message consumers. Follow us on Twitter ๐Ÿฆ and Facebook ๐Ÿ‘ฅ and join our Facebook Group ๐Ÿ’ฌ. AWS tools (SQS, SNS) These will be easier for you to setup, and integrate with the rest of your architecture, especially if most of it is already running on AWS. Required fields are marked *. The AWS Kinesis SDK does not provide any default producers only an example application. A final consideration, for now, is Kafka Schema Registry. Kinesis is very similar to Kafka, as the original Kafka author points out. Head to Head Comparison Between Kafka and Kinesis(Infographics) Below are Top 5 Differences between Kafka vs Kinesis: More and more applications and enterprises are building architectures which include processing pipelines consisting of multiple stages. Kafka and Kinesis are message brokers that have been designed as distributed logs. Kinesis is known to be reliable, and easy to operate. Cloudurable provides Kafka training, Kafka consulting, Kafka support and helps setting up Kafka clusters in AWS. Cross-replication is not mandatory, and you should consider doing so only if you need it. AWS MSK (managed Kafka) AWS MSK stands for “AWS Managed Streaming for Kafka.” Conceptually, Kafka is similar to Kinesis: producers publish messages on Kafka topics (streams), while multiple different consumers can process messages concurrently. Apache Kafka Architecture – Delivery Guarantees. Kafka Connect has a rich ecosystem of pre-built Kafka Connectors. Kafka and Kinesis are message brokers that have been designed as distributed logs. Kinesis is a fully-managed streaming processing service that’s available on Amazon Web Services (AWS). Both attempt to address scale through the use of “sharding”. To evaluate the Kafka Connect Kinesis source connector, AWS S3 sink connector, Azure Blob sink connector, and GCP GCS sink connector in an end-to-end streaming deployment, refer to the Cloud ETL demo on GitHub. Kafka or Kinesis are often chosen as an integration system in enterprise environments similar to traditional message brokering systems such as ActiveMQ or RabbitMQ. Published 19th Jan 2018. Selecting an appropriate tool for the task at hand is a recurring theme for an engineer’s work. Apache Kafka Then, in stage 3, the data is published to new topics for further consumption or follow-up processing during a later stage. Emulating Apache Kafka with AWS. The thing is, you just can’t emulate Kafka’s consumer groups with Amazon SQS, there just isn’t any feature similar to that. Producers send data to an SPS, and consumersrequest that data from the system.