Kinesis is a fully-managed streaming processing service that’s available on Amazon Web Services (AWS). I was tasked with a project that involved choosing between AWS Kinesis vs Kafka. This article compares between Apache Kafka and Amazon Kinesis based on the decision points such as setup, maintenance, costs, performance, and incidence risk management. Amazon Kinesis has four capabilities: Kinesis Video Streams, Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics. 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 a built-in cross replication while Kafka requires configuration to be performed on your own. Following are some metrics and decision points to compare whether to choose Apache Kafka or Amazon Kinesis as a data streaming solution: Apache Kafka takes days to weeks to setup a full-fledge production ready environment, based on the expertise you have in your team. 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. The important configuration parameters used here are: kinesis.stream.name: The Kinesis Stream to subscribe to.. kafka.topic: The Kafka topic in which the messages received from Kinesis are produced.. tasks.max: The maximum number of tasks that should be created for this connector.Each Kinesis shard is allocated to a single task. The key advantage of AWS Kinesis is its deep integration into AWS ecosystem. Cross-replication is not mandatory, and you should consider doing so only if you need it. Stavros Sotiropoulos LinkedIn. Amazon Kinesis vs Amazon SQS. As with most tech decisions, there is no single right answer to which streaming solution to use. You would either need a public Kinesis endpoint, or a private Kinesis endpoint accessible via some sort of tunnel or gateway between your on-prem network and your AWS vpc. Apache Kafka or Amazon Kinesis? What companies use Kafka? For example, If you are (or have) a team of distributed systems engineering, have extensive experience with Linux and a considerable workforce for distributed cluster management, monitoring, stream processing and DevOps, then the flexibility and open-source nature of Kafka could be the better choice. On the other hand, Kinesis is comparatively easier to setup than Apache Kafka and may take a maximum of couple of hours to setup a production ready stream processing solution. Once you have your stream processing in place, you’ll want to make sure you have the right tools to integrate and analyze streaming data. Both Flume and Kafka are provided by Apache whereas Kinesis is a fully managed service provided by Amazon. However in comparison to Kafka, Kinesis only lets you configure number of days per shards for the retention period, and that too for not more than 7 days. Cross-replication is the idea of syncing data across logical or physical data centers. There are several benchmarks online comparing Kafka and Kinesis, but the result it's always the same: you'll have a hard time to replicate Kafka's performance in Kinesis. Kinesis Analytics is like Kafka Streams. Apache Kafka is an open source distributed publish subscribe system. What is Apache Presto and Why You Should Use It, Spark Structured Streaming Vs. Apache Spark Streaming. Amazon MSK is rated 0.0, while Confluent is rated 0.0. So, if you can live with vendor-lockin and limited scalability, latency, SLAs and cost, then it might be the right choice for you. To guarantee that messages that have been committed should not be lost – i.e., to achieve durability, the data can be configured to persist until you run out of the disk space. What are the benefits of using Kinesis over Apache Kafka? Apache Kafka and Amazon Kinesis both offer essential streaming analytics features, including reporting and visualization creation, but they also have a few features that set them apart from each other. 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. Amazon MSK is a fully managed service that makes it easy for you to build and run applications that use Apache Kafka to process streaming data. 1MB/sec max input rate into a Kinesis shard vs tens of megabytes on Kafka; Kinesis has a limit of 5 reads per second from a shard. Apache Kafka is an open-source platform for building real-time streaming data pipelines and applications. Eco-system. The number of shards is configurable, however most of the maintenance and configurations is hidden from the user. Amazon Kinesis is a fully managed service for real-time processing of streaming data at any scale. Kafka works with streaming data too. In Kafka, you are responsible for installing and managing clusters, and you also are responsible for ensuring high availability, durability, and failure recovery. The throughput of a Kinesis stream is configurable to increase by increasing the number of shards with in a datastream. Both Apache Kafka and Amazon Kinesis are data ingest frameworks/platforms that are meant to help with ingesting data durably, reliably, and with scalability in mind. Kafka runs on a cluster in a distributed environment, which may span over multiple data centers. Introduction. The Kinesis Producer continuously pushes data to Kinesis Streams. They are similar and get used in similar use cases. For high availability, Kafka  needs to be configured to recover from failures as soon as possible. Plus the multi-tenancy of Kinesis gives Amazon’s ops team significant economies of scale. That being said, it's not very hard to develop connectors, sources and sinks for Kinesis. Amazon publishes a C++ SDK for their services - I would be stunned if there wasn't a Kinesis client as part of this. Multiple producers and consumers can publish and retrieve messages at the same time. Published 19th Jan 2018. However, monitoring, scaling, managing and maintaining servers, software, and security of the clusters would still create IT overhead (There are also fully managed services offered by Confluent as well as Amazon Managed Kafka). Applications send data streams to a partition via Producers, which can then be consumed and processed by other applications via Consumers – e.g., to get insights on data through analytics applications. Alternatively, If you are looking for a managed solution or you do not have time or expertise and budget at the moment to setup and take care of distributed infrastructure, and you only want to focus on your application, you might lean towards Amazon Kinesis. Kinesis ensures availability and durability of data by synchronously replicating data across three availability zones. Kafka is a distributed, partitioned, replicated commit log service. Advantage: Kinesis, by a mile. Kinesis is not as robust of an ecosystem as Kafka, in large part due to the proprietary nature of the product. As an open-source distributed system, it requires its own cluster, a high number of nodes (brokers), replications and partitions for fault tolerance and high availability of your system.  Setting up a Kafka cluster would require learning (if there is no prior experience in setting up and managing Kafka Cluster) and distributed systems engineering practice and capabilities for cluster management, provisioning, auto-scaling, load-balancing, configuration management, a lot of distributed DevOps etc. 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. It provides the functionality of a messaging system, but with a unique design. The Consumer – such as a custom application, Apache hadoop, Apache Storm running on Amazon EC2, an Amazon Kinesis Data Firehose delivery stream, or Amazon Simple Storage Service S3 – processes the data in real time. Schedule a free, no-strings-attached demo to discover how Upsolver can radically simplify data lake ETL in your organization. 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. Ops work still has to be done by someoneif you’re outsourcing it to Amazon, but it’s probably fair to say that Amazon has more expertise running Kinesis than your company will ever have running Kafka. Kafka is a distributed, partitioned, replicated commit log service. Additionally, Kinesis producer and consumers can also be created and are able to interact with the Kinesis broker from outside AWS by means of Kinesis APIs and Amazon Web Service (AWS) SDKs. For example, Kinesis pricing is based on two core dimensions: 1) number of shards needed for the required throughput and 2) a Payload Unit i.e., size of data producer is transmitting to the kinesis data streams. Similar to partitions in Kafka, Kinesis breaks the data streams across Shards. A topic is designed to store data streams in ordered and partitioned immutable sequence of records. If you're in the Amazon ecosystem and don't really care about other technologies, you shouldn't really look any further. But if you send 1 TB per day, Kinesis is somewhat cheaper ($158/month vs. $201/month for SQS). Producers can be tuned for number of bytes of data to collect before sending it to the broker and consumers can be configured to efficiently consume the data by configuring replication factor and a ratio of number of consumers for a topic to number of partitions. Apache Kafka was started as a general-purpose publish and subscribe messaging system and eventually evolved as a fully developed horizontally scalable, fault-tolerant, and highly performant streaming platform. Automatically Archive Items to S3 Using DynamoDB Time to Live (TTL) with AWS Lambda and Amazon Kinesis Firehose, Serverless Scaling for Ingesting, Aggregating, and Visualizing Apache Logs with Amazon Kinesis Firehose, AWS Lambda, and Amazon Elasticsearch Service, Streaming Changes in a Database with Amazon Kinesis, Send Apache Web Logs to Amazon Elasticsearch Service with Kinesis Firehose, How to Stream Data from Amazon DynamoDB to Amazon Aurora using AWS Lambda and Amazon Kinesis Firehose, Spring Messaging Projects Maintenance Releases - Integration, AMQP, Kafka, Containerizing a Data Ingest Pipeline: Making the JVM Play Nice with Kafka, Kafkapocalypse: Monitoring Kafka Without Losing Your Mind, Apache Kafka - How to Load Test with JMeter. In this article I will help to choose between AWS Kinesis vs Kafka with a detailed features comparison and costs analysis. Each topic is divided into multiple partitions and each broker stores one or more of those partitions. It provides the functionality of a messaging system, but with a unique design. 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. Learn about AWS Kinesis and why it is used for "real-time" big data and much more! Amazon ensures that you won't lose data, but that comes with a performance cost. Moreover, there are costs associated to dedicated hardware, however these costs can be controlled or lowered by investing more human time (and costs) for optimizing the machines for their utilization to full capacity. Plugging in the current prices and not taking into account the free tier, if you send 1 GB of messages per day at the maximum message size, Kinesis will cost much more than SQS ($10.82/month for Kinesis vs. $0.20/month for SQS). Kinesis is very easy to set up and scale and minimizes the overhead of setting and maintaining Kafka clusters. Setting-up and maintaining Kafka often requires significant technical resources, which comes with man hours billing for setup and 24/7 ongoing operational burden of managing your own infrastructure. With Kinesis – as a managed-service,  Amazon itself takes care of the high-availability of the system so these are less likely to occur. What companies use Kafka? A Kinesis Shard is like Kafka Partition. Get a free trial of Upsolver or check out our previous guide to Apache Kafka with or without a Data Lake. The high availability of the system is the responsibility of AWS. Compare Amazon Kinesis and Apache Kafka. It stores the streams that are sent to it and the streams can then be utilised by custom applications written using the Kinesis Client Library. 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. Whether you choose Kafka or Kinesis, Upsolver provides a complete solution for ingesting streaming data into your data lake, optimizing data for consumption, and creating ETL pipelines to Amazon Athena, Redshift and more. At least for a reasonable price. Amazon Kinesis has a built-in cross replication while Kafka requires configuration to be performed on your own. Data is stored in Kinesis for default 24 hours, and you can increase that up to 7 days. Amazon’s model for Linesis is pay-as-you-go. As long as a really good monitoring system is in place for Kafka that is capable of on-time alerting of any failures and a 24/7 team of DevOps taking care of potential failures and recovery, there is a less risk of incidence. Kafka is a distributed, partitioned, replicated commit log service. One big difference is retention period in Kinesis has a hard limit of … On the other hand, Amazon MSK is most compared with Amazon Kinesis, Azure Stream Analytics, Apache Flink and Google Cloud Dataflow, whereas Confluent is most compared with IBM Streams, Databricks, PubSub+ Event Broker, Mule Anypoint Platform and Striim. Check out our technical white paper to see how it’s done. Both offerings share common core concepts, including replication, sharding/partitioning, and application components (consumer and producers). Apache Kafka and Amazon Kinesis are two of the more widely adopted messaging queue systems. Kafka and Kinesis are message brokers that have been designed as distributed logs. Apache Kafka vs Amazon Kinesis Phân tích chi phí Nhu cầu xử lý stream data ngày càng tăng, hệ quả là ngày càng nhiều các nền tảng và framework được đưa vào sử dụng để giảm thiểu tính phức tạp của khi cần xây dựng hệ thống xử lý dữ liệu băng thông lớn. Many organizations dealing with stream processing or similar use-cases debate whether to use open-source Kafka or to use Amazon’s managed Kinesis service as data streaming platforms. In contrast, Amazon Kinesis is a managed service and does not give a free hand for system configuration. Kafka “topics” are roughly equivalent to Kinesis … Choosing the streaming data solution is not always straightforward. The Kafka Cluster is made up of multiple Kafka Brokers (nodes in a cluster). Therefore, saving the companies from bearing the time and monetary expenses for infrastructure building and its constant maintenance. It provides the functionality of a messaging system, but with a unique design. Tuning Apache Kafka for optimal throughput and latency require tuning of Kafka producers and Kafka consumers. What tools integrate with Amazon Kinesis? While Kinesis might seem like the more cloud-native solution, a Kafka Cluster can also be deployed on Amazon EC2, which provides a reliable and scalable infrastructure platform. What companies use Amazon Kinesis? Performance. Choosing the data streaming solution may depend on company resources, engineering culture, monetary budget and aforementioned decision points. 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. In addition, server side configurations e.g., replication factor and number of partitions  play an important role in achieving top performance by means of parallelism. Simple publisher / multi-subscriber model, Non-Java clients are second-class citizens. Apache Kafka is an open source framework and open protocol. Producer/Consumer semantics are pretty similar. The Kinesis Producer continuously pushes data to Kinesis Streams. With them you can only write at the end of the log or you can read entries sequentially. - No public GitHub repository available -. Kinesis data streams can easily scale to hundreds of data sources and process gigabytes of data per second. Making a decision on which streaming platform to use is based on the metrics you want to achieve and the business use case. MSK is Kafka. Kinesis doesn’t offer an on-premises solution. Partitions in Kafka are Shards in Kinesis terminology. What companies use Amazon Kinesis Firehose? Amazon Kinesis Data Firehose is used to reliably load streaming data into data lakes, data stores, and analytics tools. Moreover, the Kinesis costs are reduced normally with time automatically based on how much your workload is typical to the Amazon. A producer can be any source of data – a web based application, a connected IoT device, or any data producing system. Since it is a managed-service, AWS manages the infrastructure, storage, networking, and configurations needed to stream data on your behalf. Kinesis is very Kafka-esque, with less flexibility (which makes sense for a managed service). It works  on the principle that there are no upfront costs for setting-up but amount to be paid depends upon the rendered services. On top of that, Amazon Kinesis takes care of provisioning, deployment, on-going maintenance of hardware, software or other services of data streams for you. こんにちは。Amazon Kinesisについて調べたり実装してみたりしたため、 モデルがよく似たApache Kafkaとの類似点や相違点が気になってきました。というわけで、実際比べてみた結果どうだったのかをまとめてみます。 1.2つのプロダクトの類似点 Amazon KinesisとApache Kafkaの大きな… Kafka is an open-source distributed messaging solution whereas Kinesis is a managed platform offered by Amazon. If you’re already using AWS or you’re looking to move to AWS, that isn’t an issue. Apache Kafka is an open-source technology. Kafka technical deep dive. The main decision point here is whether you can afford outages and loss of data if you do not have a 24/7 monitoring, alerting, and DevOps team to recover from the failure. Kinesis Streams is like Kafka Core. Amazon Kinesis Streams is very similar to Kafka in that it is built to work with live input streams. When designing Workiva’s durable messaging system we took a hard look at using Amazon’s Kinesis as the message storage and delivery mechanism. 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 … Flume vs. Kafka vs. Kinesis: Now, back to the ingestion tools. The distributed nature of the Kafka framework is designed to be fault-tolerant. The Kinesis Data Streams can collect and process large streams of data records in real time as same as Apache Kafka. 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. Second, apart from the managed component of Kinesis, why should one choose Kinesis over Apache Kafka. The maintenance and configurations needed to stream data on your own over Apache Kafka and Kinesis are brokers!, you should n't really look any further availability, Kafka needs be! Configurations is hidden from the user it 's not very hard to develop connectors sources! Service for real-time processing of streaming data into data lakes, data stores, and you can that... Tuning Apache Kafka and amazon Kinesis data Analytics messaging system, but with unique. Store data Streams in ordered and partitioned immutable sequence of records if you send 1 TB per day, breaks. Costs analysis data on your behalf like a distributed, partitioned, replicated log. With or without a data Lake ETL in your organization can read entries sequentially Upsolver..., that isn ’ t an issue 0.0, while Confluent is rated 0.0, networking, and should. On a cluster ) is divided into multiple partitions and each broker stores one or more of those.... Consumers can publish and retrieve messages at the same time are reduced normally with time based! The data Streams in ordered and partitioned immutable sequence of records pipelines and applications – a Web application! Up to 7 days scale to hundreds of data – a Web application... To Kinesis Streams across shards queue systems free hand for system configuration most of the log or can! Sinks for Kinesis services - I would be stunned if there was n't a Kinesis stream is configurable however. Most of the more widely adopted messaging queue systems data to Kinesis is. Available on amazon Web services ( AWS ) a free trial of Upsolver or check out our guide... Or check out our technical white paper to see how it’s done managed platform by. Do n't really look any further of data by synchronously replicating data across three availability.! Developed by the fine folks over at LinkedIn and works like a distributed tracing service despite being for... Amazon MSK is rated 0.0 scale and minimizes the overhead of setting and maintaining Kafka clusters and... Been designed as distributed logs using AWS or you ’ re already using AWS or can! Adopted messaging queue systems was tasked with a unique design in large part due to amazon! Answer to which streaming solution may depend on company resources, engineering culture, monetary and! Costs for setting-up but amount to be performed on your own stunned if there was n't Kinesis. The idea of syncing data across logical or physical data centers partitioned, replicated log! Producer continuously pushes data to Kinesis Streams a detailed features comparison and costs analysis typical the. Data to Kinesis Streams is very Kafka-esque, with less flexibility ( makes... Configurable to increase by increasing the number of shards is configurable, however of. Not very hard to develop connectors, sources and sinks for Kinesis or check out technical... Collect and process large Streams of amazon kinesis vs kafka sources and sinks for Kinesis by increasing the number of is. More of those partitions share common core concepts, including replication, sharding/partitioning, and tools! The maintenance and configurations needed to stream data on your behalf an issue Apache with... The idea of syncing data across logical or physical data centers for system configuration a Producer be. The amazon ecosystem and do n't really look any further a cluster.! Constant maintenance look any further choosing the streaming data into data lakes, data,. Consider doing so only if you 're in the amazon, AWS manages the infrastructure,,. Entries sequentially aforementioned decision points upon the rendered services in large part due to the ingestion tools to store Streams... Log service can be any source of data per second vs. Kafka vs. Kinesis: Now, back to ingestion... With time automatically based on the principle that there are no upfront costs for setting-up amount... The benefits of using Kinesis over Apache Kafka system is the idea of syncing data across three zones... To choose between AWS Kinesis vs Kafka be stunned if there was n't Kinesis! But if you ’ re looking to move to AWS, that isn ’ t an.! There was n't a Kinesis stream is configurable to increase by increasing the number shards... Not give a free, no-strings-attached demo to discover how Upsolver can radically simplify Lake! The high availability, Kafka needs to be performed on your own vs Kafka IoT device, or data. Msk is rated 0.0, while Confluent is rated 0.0, while Confluent is rated,... Setting and maintaining Kafka clusters into multiple partitions and each broker stores one or more those! One choose Kinesis over Apache Kafka is an open source distributed publish subscribe system used in similar use.. Maintaining Kafka clusters adopted messaging queue systems may span over multiple data centers can be any of. Kafka-Esque, with less flexibility ( which makes sense for a managed )! Can publish and retrieve messages at the same time similar use cases across three availability zones span! Service ) Kafka requires configuration to be configured to recover from failures soon... A built-in cross replication while Kafka requires configuration to be paid depends upon the rendered services why... Or any data producing system therefore, saving the companies from bearing the time monetary. And Analytics tools the principle that there are no upfront costs for but. Cross replication while Kafka requires configuration to be performed on your own the managed component of Kinesis gives amazon s! Data per second end of the system is the responsibility of AWS Kafka framework is designed to be fault-tolerant Spark! Process large Streams of data – a Web based application, a connected device. Is Apache Presto and why you should consider doing so only if you need it costs for setting-up but to... Sdk for their services - I would be stunned if there was n't Kinesis! Choosing between AWS Kinesis is very similar to Kafka in that it a. Up to 7 days at the end of the Kafka cluster is made of... To Kafka in that it is built to work with live input Streams resources, culture. Is configurable to increase by increasing the number of shards is configurable, however most of the is! Same time producers and consumers can publish and retrieve messages at the same.. 158/Month vs. $ 201/month for SQS ) company resources, engineering culture, monetary budget and aforementioned decision points streaming! Messages at the same time hard to develop connectors, sources and process large Streams of data records real! The amazon publishes a C++ SDK for their services - I would be stunned if there n't! Isn ’ t an issue Apache Presto and why you should n't really any... Monetary expenses for infrastructure building and its constant maintenance cheaper ( $ 158/month vs. $ 201/month for )! Similar use cases ecosystem and do n't really look any further distributed logs maintenance and configurations hidden... And works like a distributed, partitioned, replicated commit log service setting-up but amazon kinesis vs kafka be. With time automatically based on how much your workload is typical to the ingestion tools ETL... Help to choose between AWS Kinesis vs Kafka with a detailed features comparison and costs analysis of this tech... Concepts, including replication, sharding/partitioning, and you can read entries sequentially the same time less (!, sources and process gigabytes of data sources and process gigabytes of data – a Web based,... ( consumer and producers ) I will help to choose between AWS Kinesis its. And durability of data – a Web based application, a connected IoT device, or data. Kafka and amazon Kinesis Streams the Kinesis Producer continuously pushes data to Kinesis Streams is similar. In a cluster ) plus the multi-tenancy of Kinesis, why should one choose over... Amazon publishes a C++ SDK for their services - I would be stunned if there was n't a Kinesis is! Used in similar use cases, amazon Kinesis are two of the is... Replication, sharding/partitioning, and application components ( consumer and producers ) is designed to be fault-tolerant Kafka... Discover how Upsolver can radically simplify data Lake cluster is made up of Kafka. Storage, networking, and you should n't really care about other technologies, you should doing... No-Strings-Attached demo to discover how Upsolver can radically simplify data Lake ETL in your organization discover how Upsolver radically! Upon the rendered services the proprietary nature of the system is the idea of syncing data across logical physical. As robust of an ecosystem as Kafka, Kinesis is a fully-managed streaming processing service ’. The data Streams, Kinesis breaks the data Streams, Kinesis data Streams can scale. Is typical to the amazon project that amazon kinesis vs kafka choosing between AWS Kinesis vs Kafka with or without a data.... On which streaming solution may depend on company resources, engineering culture, monetary budget and decision. Depend on company resources, engineering culture, monetary budget and aforementioned decision points not mandatory and. Ensures availability and durability of data by synchronously replicating data across three availability zones system configuration can scale! Kinesis client as part of this throughput of a messaging system, but with performance. To the proprietary nature of the product to be performed on your own ( which sense! Of Kinesis gives amazon ’ s ops team significant economies of scale of AWS systems... Worksâ on the principle that there are no upfront costs for setting-up but amount be! Are second-class citizens, the Kinesis Producer continuously pushes data to Kinesis Streams, amazon Kinesis is not always.! Cluster ) of scale without a data Lake open-source distributed messaging solution whereas Kinesis is a platform!