The high availability of the system is the responsibility of AWS. 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. Compare Amazon Kinesis and Apache Kafka. When designing Workiva’s durable messaging system we took a hard look at using Amazon’s Kinesis as the message storage and delivery mechanism. Apache Kafka is an open source framework and open protocol. Both offerings share common core concepts, including replication, sharding/partitioning, and application components (consumer and producers). Kinesis is very easy to set up and scale and minimizes the overhead of setting and maintaining Kafka clusters. 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. Partitions in Kafka are Shards in Kinesis terminology. What companies use Amazon Kinesis Firehose? Multiple producers and consumers can publish and retrieve messages at the same time. 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. Amazon Kinesis Data Firehose is used to reliably load streaming data into data lakes, data stores, and analytics tools. 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). 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. Amazon Kinesis has a built-in cross replication while Kafka requires configuration to be performed on your own. Kinesis data streams can easily scale to hundreds of data sources and process gigabytes of data per second. 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). That being said, it's not very hard to develop connectors, sources and sinks for Kinesis. 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. Introduction. 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. Kafka is a distributed, partitioned, replicated commit log service. Second, apart from the managed component of Kinesis, why should one choose Kinesis over Apache Kafka. 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. It provides the functionality of a messaging system, but with a unique design. A producer can be any source of data – a web based application, a connected IoT device, or any data producing system. Kafka is an open-source distributed messaging solution whereas Kinesis is a managed platform offered by Amazon. The Kinesis Producer continuously pushes data to Kinesis Streams. 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. 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. MSK is Kafka. The key advantage of AWS Kinesis is its deep integration into AWS ecosystem. Kinesis ensures availability and durability of data by synchronously replicating data across three availability zones. The Kinesis Data Streams can collect and process large streams of data records in real time as same as Apache Kafka. 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. Similar to partitions in Kafka, Kinesis breaks the data streams across Shards. 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. As with most tech decisions, there is no single right answer to which streaming solution to use. Kinesis is a fully-managed streaming processing service that’s available on Amazon Web Services (AWS). Kafka runs on a cluster in a distributed environment, which may span over multiple data centers. 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. Apache Kafka is an open-source technology. In Kafka, you are responsible for installing and managing clusters, and you also are responsible for ensuring high availability, durability, and failure recovery. 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. Data is stored in Kinesis for default 24 hours, and you can increase that up to 7 days. 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