{"id":78286,"date":"2021-04-08T05:31:23","date_gmt":"2021-04-08T05:31:23","guid":{"rendered":"https:\/\/www.whizlabs.com\/blog\/?p=78286"},"modified":"2023-11-30T21:14:41","modified_gmt":"2023-12-01T02:44:41","slug":"aws-kinesis-data-streams-vs-aws-kinesis-data-firehose","status":"publish","type":"post","link":"https:\/\/www.whizlabs.com\/blog\/aws-kinesis-data-streams-vs-aws-kinesis-data-firehose\/","title":{"rendered":"AWS Kinesis Data Streams vs AWS Kinesis Data Firehose"},"content":{"rendered":"<p><i><span style=\"font-weight: 400;\">AWS Kinesis is the favorable choice for applications that use streaming data. Explore\u00a0AWS kinesis data streams vs AWS kinesis data firehose right now!<\/span><\/i><\/p>\n<p><span style=\"font-weight: 400;\">The AWS ecosystem has constantly been expanding with the addition of new offerings alongside new functionalities. Amazon introduced AWS Kinesis as a highly available channel for communication between data producers and data consumers. It serves as a formidable passage for streaming messages between the data producers and data consumers.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Data producers could come from almost any source of data such as social network data, mobile app data, system or weblog data, telemetry from connected IoT devices, financial trading information, and geospatial data. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">On the other hand, data consumers would include references to data processing and storage applications such as Amazon Simple Storage Service (S3), Apache Hadoop, ElasticSearch, and Apache Storm.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Users could access different services with Amazon Kinesis, such as Kinesis Video Streams, Amazon Kinesis Data Streams, AWS Kinesis Data Firehose, and Kinesis Data Analytics. The following discussion aims to discuss the differences between Data Streams and Data Firehose.<\/span><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_76 ez-toc-wrap-left counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #ea7e02;color:#ea7e02\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #ea7e02;color:#ea7e02\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-kinesis-data-streams-vs-aws-kinesis-data-firehose\/#An_Overview_of_AWS_Kinesis\" >An Overview of AWS Kinesis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-kinesis-data-streams-vs-aws-kinesis-data-firehose\/#What_are_AWS_Kinesis_Data_Streams_and_Data_Firehose\" >What are AWS Kinesis Data Streams and Data Firehose?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-kinesis-data-streams-vs-aws-kinesis-data-firehose\/#Understanding_the_Architecture_%E2%80%93_AWS_Kinesis_Data_Streams_vs_Data_Firehose\" >Understanding the Architecture &#8211; AWS Kinesis Data Streams vs. Data Firehose<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-kinesis-data-streams-vs-aws-kinesis-data-firehose\/#Data_Streams\" >Data Streams<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-kinesis-data-streams-vs-aws-kinesis-data-firehose\/#Data_Firehose\" >Data Firehose<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-kinesis-data-streams-vs-aws-kinesis-data-firehose\/#Comparison_between_Amazon_Kinesis_Data_Streams_and_Data_Firehose\" >Comparison between Amazon Kinesis Data Streams and Data Firehose<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.whizlabs.com\/blog\/aws-kinesis-data-streams-vs-aws-kinesis-data-firehose\/#Summary\" >Summary<\/a><\/li><\/ul><\/nav><\/div>\n<h3><span class=\"ez-toc-section\" id=\"An_Overview_of_AWS_Kinesis\"><\/span>An Overview of AWS Kinesis<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Before discussing the differences between Kinesis data streams and Firehose, it is important to understand Kinesis first. Amazon Kinesis is a significant feature in AWS for easy collection, processing, and analysis of video and data streams in real-time environments.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">AWS Kinesis helps in real-time data ingestion with support for data such as video, audio, IoT telemetry data, application logs, analytics applications, website clickstreams, and machine learning applications. It supports effective data processing and analysis with instant response and does not have to wait for collecting all data for starting the processing work.<\/span><\/p>\n<blockquote><p>Read <a href=\"https:\/\/www.whizlabs.com\/blog\/what-is-aws-kinesis\/\"><strong>What Is AWS Kinesis? From Basics to Advanced<\/strong><\/a>!<\/p><\/blockquote>\n<h3><span class=\"ez-toc-section\" id=\"What_are_AWS_Kinesis_Data_Streams_and_Data_Firehose\"><\/span>What are AWS Kinesis Data Streams and Data Firehose?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">AWS Kinesis Data Streams and Firehose are the two distinct capabilities of Amazon Kinesis, which empower it for data streaming and analytics. However, the debate between Kinesis Data Streams and Firehose has been one of the prominent points of discussion recently. Let us find out the differences between Amazon Kinesis Data Stream and Firehose to understand their individual significance.\u00a0<\/span><\/p>\n<p><b>AWS Kinesis Data Streams<\/b><span style=\"font-weight: 400;\"> is the real-time data streaming service in Amazon Kinesis with high scalability and durability. It can help in continuously capturing multiple gigabytes of data every second from multiple sources. The higher customizability with Kinesis Data Streams is also one of the profound highlights.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">As a matter of fact, it is the ideal choice for developers involved in developing custom applications or streaming data according to special needs. On the other hand, the benefits of customizability come at the price of manual provisioning and scaling. Generally, data is set up for 24 hours of availability in a stream while also ensuring that users could achieve data availability for almost 7 days.\u00a0<\/span><\/p>\n<p><b>AWS Kinesis Data Firehose<\/b><span style=\"font-weight: 400;\"> provides the facility of loading data streams into AWS data stores. Kinesis Data Firehose provides the simplest approach for capturing, transforming, and loading data streams into AWS data stores.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"> The automatic management of scaling in the range of gigabytes per second, along with support for batching, encryption, and compression of streaming data, are also some crucial features in Amazon Kinesis Data Firehose. Firehose also helps in streaming to RedShift, S3, or ElasticSearch service, to copy data for processing by using additional services.\u00a0<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Understanding_the_Architecture_%E2%80%93_AWS_Kinesis_Data_Streams_vs_Data_Firehose\"><\/span>Understanding the Architecture &#8211; AWS Kinesis Data Streams vs. Data Firehose<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The first point of comparison between the two key capabilities of AWS Kinesis would refer to the architecture. The explanations on architecture of AWS Kinesis Data Streams and Firehose can show how they are different from each other.\u00a0<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Data_Streams\"><\/span><b>Data Streams<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">In the case of data streams, data producers enter the records into Kinesis data streams or KDS. Then, AWS offers the Kinesis Producer Library or KPL for simplifying producer application development. In addition, it also helps in achieving higher write throughput to a particular Kinesis data stream.\u00a0<\/span><\/p>\n<p><img decoding=\"async\" class=\"aligncenter wp-image-78251 size-full\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-2.png\" alt=\"aws Kinesis Data Streams\" width=\"2240\" height=\"1260\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-2.png 2240w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-2-300x169.png 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-2-1024x576.png 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-2-768x432.png 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-2-1536x864.png 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-2-2048x1152.png 2048w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-2-747x420.png 747w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-2-640x360.png 640w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-2-681x383.png 681w\" sizes=\"(max-width: 2240px) 100vw, 2240px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">The Kinesis data stream is basically a collection of shards, with each shard featuring a sequence of data records. Data records feature a sequence number, partition key, and a data blob with size of up to 1 MB. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">The data blob is generally an immutable sequence of bytes. Consumers could then obtain records from KDS for processing. Subsequently, users can build applications by using AWS Kinesis Data Analytics, Kinesis Client Library, or Kinesis API.\u00a0<\/span><\/p>\n<blockquote><p>Start preparing for AWS Certified Cloud Practitioner Certifications today with 9+ hours training online training videos and 21+ labs <a href=\"https:\/\/www.whizlabs.com\/aws-certified-cloud-practitioner\/online-course\/\">today<\/a>!<\/p><\/blockquote>\n<h3><span class=\"ez-toc-section\" id=\"Data_Firehose\"><\/span><b>Data Firehose<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The operations of Kinesis Data Firehose start with data producers sending records to delivery streams of Firehose. Kinesis Data Firehose delivery stream is the underlying component for operations of Kinesis Firehose. The delivery stream helps in automatically delivering data to the specified destination, such as Splunk, S3, or RedShift.\u00a0<\/span><\/p>\n<p><img decoding=\"async\" class=\"aligncenter size-full wp-image-78250\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-1.png\" alt=\"Amazon Kinesis Data Firehose\" width=\"2240\" height=\"1260\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-1.png 2240w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-1-300x169.png 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-1-1024x576.png 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-1-768x432.png 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-1-1536x864.png 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-1-2048x1152.png 2048w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-1-747x420.png 747w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-1-640x360.png 640w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2021\/04\/AWS-Kinesis-1-681x383.png 681w\" sizes=\"(max-width: 2240px) 100vw, 2240px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Users have the option of configuring <a href=\"https:\/\/www.whizlabs.com\/aws-certifications\/\" target=\"_blank\" rel=\"noopener\">AWS<\/a> Kinesis Firehose for transforming data before its delivery. You should activate data transformation on Kinesis Firehose with the creation of your delivery stream. Now, Kinesis Data Firehose can invoke the user\u2019s Lambda function for transforming the incoming source data. It also ensures the delivery of transformed data to all the desired destinations.\u00a0<\/span><\/p>\n<blockquote><p>Try 3-<strong>Full Length Mock Exams<\/strong> with 195 Unique Questions for AWS Certified Data Analytics Certifications <a href=\"https:\/\/www.whizlabs.com\/aws-certified-data-analytics-specialty\/practice-tests\/\">here<\/a>!<\/p><\/blockquote>\n<h3><span class=\"ez-toc-section\" id=\"Comparison_between_Amazon_Kinesis_Data_Streams_and_Data_Firehose\"><\/span>Comparison between Amazon Kinesis Data Streams and Data Firehose<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Based on the differences in architecture of AWS Kinesis Data Streams and Data Firehose, it is possible to draw comparisons between them on many other fronts. Here are some of the notable pointers for comparing Kinesis Data Streams with Kinesis Data Firehose.\u00a0<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h4><b>Objective<\/b><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The fundamental objective of the services also plays a crucial role in differentiating data streams vs. Firehose comparison. The basic purpose of the tools can exhibit a profound difference between them. Data Streams is a low latency streaming service in AWS Kinesis with the facility for ingesting at scale. On the other hand, Kinesis Firehose aims to serve as a data transfer service. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">The primary purpose of Kinesis Firehose focuses on loading streaming data to Amazon S3, Splunk, ElasticSearch, and RedShift.\u00a0<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h4><b>Provisioning\u00a0<\/b><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Provisioning is also an important concern when it comes to differentiating between two technical solutions. Kinesis Data Streams work as a managed service and offer profound levels of flexibility in terms of customization. However, the cost of customization becomes clearly evident with KDS due to the need for manual provisioning.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Users must employ manual configuration for shards to ensure proper provisioning of KDS. On the other hand, Kinesis Data Firehose comes forward as a fully managed service. Therefore, users don\u2019t have to worry about any administrative burden when it comes to using Kinesis Firehose.\u00a0<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h4><b>Data Storage<\/b><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The effectiveness of data storage is also one of the unique differentiators that separate AWS Kinesis services from each other. In the case of data streams, you can configure data storage for holding data from one to seven days. On the contrary, Firehose does not provide any facility for data storage.\u00a0<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h4><b>Processing\u00a0<\/b><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The processing power of data streaming services is one of the critical factors for establishing their significance. The processing capabilities of AWS Kinesis Data Streams are higher with support for real-time processing. Users could avail almost 200ms latency for classic processing tasks and around 70ms latency for enhanced fan-out tasks. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">On the other hand, Kinesis Data Firehose features near real-time processing capabilities. Furthermore, the processing capabilities of Firehose depend considerably on buffer size or buffer time, which could be a minimum of 60 seconds.<\/span><\/p>\n<blockquote><p><span style=\"font-weight: 400;\">All set to take the AWS Certified Data Analytics \u2013 Specialty Exam? Try <strong><a href=\"https:\/\/www.whizlabs.com\/aws-certified-data-analytics-specialty\/free-test\/\">Free Test<\/a><\/strong> before the real exam!\u00a0<\/span><\/p><\/blockquote>\n<ul>\n<li aria-level=\"1\">\n<h4><b>Replay Capability<\/b><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Another notable pointer for differentiating AWS Kinesis services refers prominently to replay capability. As a matter of fact, replay capability establishes a clear difference between KDS and AWS Kinesis Data Firehose. KDS provides support for replay capability, while Kinesis Firehose does not offer any support for replay capability.<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h4><b>Scaling<\/b><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The differences in the Streams vs. Firehose debate also circle around to the factor of scaling capabilities. Data streams impose the burden of managing the scaling tasks manually through configuration of shards. On the contrary, users don\u2019t have to worry about scaling with Firehose as it offers automated scaling. In the case of Kinesis Firehose, users get the advantage of automated scaling according to the demand of users.\u00a0<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h4><b>Producers<\/b><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">As discussed already, data producers are an important addition to the ecosystem of AWS Kinesis services. Both KDS and Firehose present a similar connection in the case of data producers as they imply the need to write code for producers. Data streams are compatible with SDK, IoT, Kinesis Agent, CloudWatch, and KPL. On the other hand, Kinesis Firehose provides support for Kinesis Agent, IoT, KPL, CloudWatch, and Data Streams.\u00a0<\/span><\/p>\n<ul>\n<li aria-level=\"1\">\n<h4><b>Consumers\u00a0<\/b><\/h4>\n<\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The final and most important differentiator between AWS Kinesis services, data streams, and Firehose refers to support for data consumers. AWS Kinesis Data Streams features open-ended support for data consumers. Therefore, it can work with multiple consumers and destinations. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">At the same time, KDS also shows support for Spark and KCL. On the contrary, AWS Kinesis Data Firehose follows a closed-ended model for data consumers. Firehose is responsible for managing data consumers and does not offer support for Spark or KCL.\u00a0<\/span><\/p>\n<h4><b>Difference Table<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">Here is a look at the differences between AWS Kinesis Data Streams and Data Firehose in the table as follows,<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td><\/td>\n<td>\n<h5 style=\"text-align: center;\"><b>Kinesis Data Streams<\/b><\/h5>\n<\/td>\n<td>\n<h5 style=\"text-align: center;\"><b>Kinesis Data Firehose<\/b><\/h5>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<p style=\"text-align: center;\"><b>Objective<\/b><\/p>\n<\/td>\n<td><span style=\"font-weight: 400;\">AWS Kinesis service for low-latency streaming and data ingestion at scale.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Data transfer service for loading streaming data into Amazon S3, Splunk, ElasticSearch, and RedShift.<\/span><\/td>\n<\/tr>\n<tr>\n<td>\n<p style=\"text-align: center;\"><b>Provisioning<\/b><\/p>\n<\/td>\n<td><span style=\"font-weight: 400;\">Managed service yet requires configuration for shards.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Completely managed service without the need for any administration.<\/span><\/td>\n<\/tr>\n<tr>\n<td>\n<p style=\"text-align: center;\"><b>Data Storage<\/b><\/p>\n<\/td>\n<td><span style=\"font-weight: 400;\">Option for configuring storage for one to seven days.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">No facility for data storage.<\/span><\/td>\n<\/tr>\n<tr>\n<td>\n<p style=\"text-align: center;\"><b>Processing<\/b><\/p>\n<\/td>\n<td><span style=\"font-weight: 400;\">Real-time processing capabilities with almost 200ms latency for classic tasks and almost 70ms latency for enhanced fan-out tasks.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Near real-time processing capabilities, depending on the buffer size or minimum buffer time of 60 seconds.\u00a0<\/span><\/td>\n<\/tr>\n<tr>\n<td>\n<p style=\"text-align: center;\"><b>Scaling<\/b><\/p>\n<\/td>\n<td><span style=\"font-weight: 400;\">Data Streams imply the need for manual management of scaling through configuration of shards.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Firehose offers the facility of automated scaling, according to the demand of users.\u00a0<\/span><\/td>\n<\/tr>\n<tr>\n<td>\n<p style=\"text-align: center;\"><b>Replay Capabilities<\/b><\/p>\n<\/td>\n<td><span style=\"font-weight: 400;\">Features support relay capabilities.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">No support for relay capability.<\/span><\/td>\n<\/tr>\n<tr>\n<td>\n<p style=\"text-align: center;\"><b>Data Producers<\/b><\/p>\n<\/td>\n<td><span style=\"font-weight: 400;\">Depends on the need to write code for a producer with support for SDK, IoT, Kinesis Agent, CloudWatch, and KPL.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Depends on the need to write code for a producer with support for Kinesis Agent, IoT, KPL, CloudWatch, and Data Streams.<\/span><\/td>\n<\/tr>\n<tr>\n<td>\n<p style=\"text-align: center;\"><b>Data Consumers<\/b><\/p>\n<\/td>\n<td><span style=\"font-weight: 400;\">Features open-ended model for consumers with support for multiple consumers and destinations. It also provides support for Spark and KCL.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Features close-ended model for consumers and is subject to management by Firehose. It does not provide any support for Spark or KCL.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span style=\"font-weight: 400;\">\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Summary\"><\/span>Summary<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">On a concluding note, it is quite clear that AWS Kinesis services have unique differences between them on certain factors. The simple objectives, support for scaling, data storage, and processing power are some of the crucial differentiators in this discussion. The differences between AWS Kinesis Data Streams and Firehose could help users in making the ideal choice of streaming service. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">The constantly changing needs of application developers could find a reliable support in the form of an ideal choice for streaming data to and from their applications. So, it is important to reflect on the functionalities of services in Amazon Kinesis in detail before making a choice.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>AWS Kinesis is the favorable choice for applications that use streaming data. Explore\u00a0AWS kinesis data streams vs AWS kinesis data firehose right now! The AWS ecosystem has constantly been expanding with the addition of new offerings alongside new functionalities. Amazon introduced AWS Kinesis as a highly available channel for communication between data producers and data consumers. It serves as a formidable passage for streaming messages between the data producers and data consumers.\u00a0 Data producers could come from almost any source of data such as social network data, mobile app data, system or weblog data, telemetry from connected IoT devices, financial 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Gumaste","author_link":"https:\/\/www.whizlabs.com\/blog\/author\/pavan\/"},"uagb_comment_info":8,"uagb_excerpt":"AWS Kinesis is the favorable choice for applications that use streaming data. Explore\u00a0AWS kinesis data streams vs AWS kinesis data firehose right now! The AWS ecosystem has constantly been expanding with the addition of new offerings alongside new functionalities. Amazon introduced AWS Kinesis as a highly available channel for communication between data producers and data&hellip;","_links":{"self":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/78286","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/users\/13"}],"replies":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/comments?post=78286"}],"version-history":[{"count":13,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/78286\/revisions"}],"predecessor-version":[{"id":92346,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/78286\/revisions\/92346"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media\/78287"}],"wp:attachment":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media?parent=78286"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/categories?post=78286"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/tags?post=78286"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}