{"id":29856,"date":"2021-11-25T06:15:19","date_gmt":"2021-11-25T06:15:19","guid":{"rendered":"https:\/\/codeandpepper.com\/?p=29856"},"modified":"2022-05-26T11:55:46","modified_gmt":"2022-05-26T11:55:46","slug":"analytics-on-amazon-aws-big-data","status":"publish","type":"post","link":"https:\/\/codeandpepper.com\/analytics-on-amazon-aws-big-data\/","title":{"rendered":"Data Analytics on Amazon Web Services &#8211; AWS Big Data"},"content":{"rendered":"\n<p><strong>Analytics on Amazon<\/strong> are inextricably linked to AWS Big Data. With the huge amount of information traveling through digital products every single second, you need a reliable set of tools to properly implement <strong>analytics on AWS<\/strong>. Luckily, we know how to do that and will tell you in this article.&nbsp;<\/p>\n\n\n\n<!--more-->\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/AWS_Big_Data.jpeg\" alt=\"Analytics on Amazon\" class=\"wp-image-31658\" width=\"864\" height=\"450\" srcset=\"https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/AWS_Big_Data.jpeg 864w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/AWS_Big_Data-300x156.jpeg 300w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/AWS_Big_Data-768x400.jpeg 768w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/AWS_Big_Data-80x42.jpeg 80w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/AWS_Big_Data-119x62.jpeg 119w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/AWS_Big_Data-361x188.jpeg 361w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/AWS_Big_Data-433x226.jpeg 433w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/AWS_Big_Data-192x100.jpeg 192w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/AWS_Big_Data-720x375.jpeg 720w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/AWS_Big_Data-432x225.jpeg 432w\" sizes=\"auto, (max-width: 864px) 100vw, 864px\" \/><\/figure><\/div>\n\n\n<h4>Table of contents<\/h4>\n\n\n<ul class=\"wp-block-list\"><li><a href=\"#Analytics on AWS \u2013 gathering and processing\">Analytics on AWS \u2013 gathering and processing<\/a><\/li><li><a href=\"#Analytics on AWS \u2013 visualization and analysis\">Analytics on AWS \u2013 visualization and analysis<\/a><\/li><li><a href=\"#Your role as the client in the AWS Big Data analytics process\">Your role as the client in the AWS Big Data analytics process<\/a><\/li><li><a href=\"#\u2026 and justice for all\">\u2026 and justice for all<\/a><\/li><\/ul>\n\n\n\n<p>With data, you can make business decisions and steer your business in a new direction. You can also slightly adjust the course, making changes invisible to users but impacting them in meaningful ways. To utilize <strong>the power of analytics on AWS<\/strong>, you will need to collect the data, store it, process it, make an analysis and visualize the results.<\/p>\n\n\n\n<p>That\u2019s why <strong>we always recommend gathering data from the beginning<\/strong>. This is the easiest and most important step. The real challenge comes with processing data and creating valuable business insights.<\/p>\n\n\n\n<section id=\"Analytics on AWS \u2013 gathering and processing\" class=\"hidden-block\"><\/section>\n\n<style type=\"text\/css\">\n  #Analytics on AWS \u2013 gathering and processing {\n    visibility: hidden !important;\n  }\n<\/style>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-analytics-on-aws-gathering-and-processing\">Analytics on AWS \u2013 gathering and processing<\/h2>\n\n\n\n<p>For data collection, we use <a href=\"https:\/\/aws.amazon.com\/pinpoint\" rel=\"nofollow\">Amazon Pinpoint<\/a>. We use this service in projects that require analysis similar to Google Analytics. We have sent events from the frontend (like tapping a button or sorting out a table by date) and set up Pinpoint with Amplify library. The service lets us gather, display and sort events using multiple filters and present them to you in a useful fashion.&nbsp;<\/p>\n\n\n\n<p>We also connect to a service called <a href=\"https:\/\/aws.amazon.com\/kinesis\/data-firehose\" rel=\"nofollow\">Amazon Kinesis Data Firehose<\/a>. With it, we can direct the data stream, data stores and analytics services, making them available and useful across the board, within the entire application. The very first service that benefits from this funnel is <a href=\"https:\/\/aws.amazon.com\/s3\" rel=\"nofollow\">Amazon Simple Storage Service<\/a> (S3).<\/p>\n\n\n\n<p>By analogy, if something happens on the backend (like a new user registration or login process), the traffic goes through <a href=\"https:\/\/aws.amazon.com\/cognito\" rel=\"nofollow\">Amazon Cognito<\/a>. These events can be intercepted by <a href=\"https:\/\/aws.amazon.com\/lambda\" rel=\"nofollow\">AWS Lambda<\/a>. Later on, we can publish them in other microservices, using services like Amazon EventBridge, for example. This will help other microservices to get data and communicate its status between them. What happened, who triggered the action, what information changed, and what other microservices should now do. This is the very core of analytics on Amazon. Microservices should alway be independent. Other microservices publish information or events about what happened, but any given microservice always decides what it should do in a given situation.<\/p>\n\n\n\n<p>With both: frontend and backend covered by AWS-related microservices; we can safely control the data flow for many projects coming our way. We know what user triggers what action in the system, what events are triggered because of the action, and what the potential threat level is if the user is unauthorized. The information can be stored (via cookies, for example) and recognized on different browsers and different devices.&nbsp;<\/p>\n\n\n\n<p>When it comes to <strong>exporting data<\/strong>, we do it through the Amazon S3 service. We can download the information from the console through <a href=\"https:\/\/aws.amazon.com\/cli\" rel=\"nofollow\">Amazon Command Line Interface<\/a> (CLI) or we can create a specialized application programming interface (API) for it.&nbsp;<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"742\" height=\"564\" src=\"https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0.png\" alt=\"AWS big data\" class=\"wp-image-31664\" srcset=\"https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0.png 742w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0-300x228.png 300w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0-80x61.png 80w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0-82x62.png 82w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0-275x209.png 275w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0-328x249.png 328w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0-132x100.png 132w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0-720x547.png 720w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0-432x328.png 432w\" sizes=\"auto, (max-width: 742px) 100vw, 742px\" \/><\/figure><\/div>\n\n\n\n<section id=\"Analytics on AWS \u2013 visualization and analysis\" class=\"hidden-block\"><\/section>\n\n<style type=\"text\/css\">\n  #Analytics on AWS \u2013 visualization and analysis {\n    visibility: hidden !important;\n  }\n<\/style>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-analytics-on-aws-visualization-and-analysis\">Analytics on AWS \u2013 visualization and analysis<\/h2>\n\n\n\n<p>The simplest way to visualize data is to use <a href=\"https:\/\/aws.amazon.com\/quicksight\" rel=\"nofollow\">the Amazon QuickSight<\/a> microservice. This data should be taken from somewhere, so we use <a href=\"https:\/\/aws.amazon.com\/athena\" rel=\"nofollow\">Amazon Athena<\/a> to draw from the AWS Big Data lake stored in S3. Athena can \u201cask\u201d files (millions of records stored in JSON) about specifically needed information or an event. Athena does that with SQL, but not by copying data. The service creates a metatable with all necessary records. QuickSight asks various sources about the data and creates visualizations.<\/p>\n\n\n\n<p>This is only one possible solution for that type of request. There are numerous services for transforming data and delivering results and handy visualizations. However, Athena can \u201crefuse\u201d cooperation, if data is delivered in the wrong format or without proper formatting. To overcome this challenge, we use other types of tools for an <strong>ETL (extract, transform, load).<\/strong><\/p>\n\n\n\n<p>For \u201cspecial operations\u201d we use <a href=\"https:\/\/aws.amazon.com\/redshift\" rel=\"nofollow\">Amazon Redshift<\/a>. It\u2019s a warehouse that\u2019s free of limitations that traditionally understood databases struggle with. Typically, they are not useful for heavy lifting in data analytics; on AWS or otherwise. With a million records and a simple task like a summary of all banking transactions, we can safely say you\u2019re covered. But if the amount is higher (and it usually is), you need a whole day to process and analyze that.&nbsp;<\/p>\n\n\n\n<p>That\u2019s where a warehouse comes in. It\u2019s a distributed database. A node, if you will take all these transactions and give them (one portion at a time) to other nodes that can process and analyze a smaller chunk. This significantly improves business operations by dramatically reducing the time needed to produce results.&nbsp;<\/p>\n\n\n\n<p>Another useful tool is called <a href=\"https:\/\/aws.amazon.com\/emr\" rel=\"nofollow\">Amazon Elastic Map Reduce<\/a> (EMR). It can also tackle the load by dividing it on a massive scale and analyzing data step by step.<\/p>\n\n\n\n<p>We also use <a href=\"https:\/\/aws.amazon.com\/cloudwatch\" rel=\"nofollow\">Amazon CloudWatch<\/a> for monitoring S3 requests and more. It\u2019s a powerful tool for DevOps, engineers, developers, and managers. We can sneak peek into the system-wide performance changes and optimize resource utilization for the optimal outcome and performance.<\/p>\n\n\n\n<p>Finally, we use <a href=\"https:\/\/aws.amazon.com\/xray\" rel=\"nofollow\">Amazon X-Ray<\/a> for analytics and debugging applications. We can monitor the data flow between various microservices and respond accordingly when the load is too much or something goes wrong and there\u2019s a need for troubleshooting. We can identify bottlenecks, which is useful, especially for AWS Big Data analytics, where there\u2019s a lot of information. Some of it can plug the system; this is the remedy.&nbsp;<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0-1.png\" alt=\"Analytics on AWS\" class=\"wp-image-31666\" width=\"824\" height=\"570\" srcset=\"https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0-1.png 824w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0-1-300x208.png 300w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0-1-768x531.png 768w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0-1-80x55.png 80w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0-1-90x62.png 90w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0-1-302x209.png 302w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0-1-360x249.png 360w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0-1-145x100.png 145w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0-1-720x498.png 720w, https:\/\/codeandpepper.com\/wp-content\/uploads\/2021\/11\/pasted-image-0-1-432x299.png 432w\" sizes=\"auto, (max-width: 824px) 100vw, 824px\" \/><\/figure><\/div>\n\n\n\n<section id=\"Your role as the client in the AWS Big Data analytics process\" class=\"hidden-block\"><\/section>\n\n<style type=\"text\/css\">\n  #Your role as the client in the AWS Big Data analytics process {\n    visibility: hidden !important;\n  }\n<\/style>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-your-role-as-the-client-in-the-aws-big-data-analytics-process\"><strong>Your role as the client in the AWS Big Data analytics process<\/strong><\/h2>\n\n\n\n<p>If you want to give us your business, we have a little homework for you. We would like to know what your endgame is? What is the business justification for gathering data? <strong>What kind of data do you want to have, process, and display?<\/strong> We think about Amazon QuickSight here, since it\u2019s the primary and natural first-choice option. <strong>What metrics are important to you?<\/strong> A number of daily registering users, fee percentage per user (in a subscription model of your product)?<\/p>\n\n\n\n<p>There can also be a situation when you have a nearly completed frontend. Maybe you already have implemented button placement across the application but you wonder about the effectiveness of the design. We can gather data for you and if the application is underperforming, suggest <a href=\"https:\/\/codeandpepper.com\/services\/ux-design-agency\/\">changes in the UX<\/a> \/ <a href=\"https:\/\/codeandpepper.com\/services\/ui-design-company\/\">UI design<\/a>.&nbsp;<\/p>\n\n\n\n<section id=\"\u2026 and justice for all\" class=\"hidden-block\"><\/section>\n\n<style type=\"text\/css\">\n  #\u2026 and justice for all {\n    visibility: hidden !important;\n  }\n<\/style>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-and-justice-for-all\">\u2026 and justice for all<\/h2>\n\n\n\n<p>The fundamental problem with data comes not with the volume, although it can be a challenge on its own. The real issue lies in the processing department. How can you effectively dig through a load of information and adjust your digital product accordingly to the market\u2019s expectations? <a href=\"https:\/\/codeandpepper.com\/services\/cloud-computing-software-development-and-consulting\">Cloud computing software development<\/a> can help with that, but it all starts with you.<\/p>\n\n\n\n<p>To do your product and your data justice, we need to know your end game and intentions behind gathering information. Analytics on Amazon can be supported with Amazon Web Services, Big Data support on our part, and lots of talks on a daily scrum in between. But what we really want to know is how we can help. Aside from the already mentioned, there are lots of tools to choose from (See our post: <a href=\"https:\/\/codeandpepper.com\/best-data-management-and-business-intelligence-tools\/\">Top 11 Data Analytics and Business Intelligence Tools for FinTech<\/a>). We can gladly help you with development and optimization.<\/p>\n\n\n\n\n\n<section id=\"contact\" class=\"contact-block block common-block \">\n  <div class=\"container\">\n\n\n    \n  <svg class=\"wave\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"280\" height=\"9\" viewBox=\"0 0 280 9\">\n    <path fill=\"currentColor\" fill-rule=\"evenodd\" d=\"M0 3.6c2.88 0 4.18-.668 5.824-1.515C7.724 1.108 9.879 0 13.998 0c4.12 0 6.274 1.108 8.175 2.085 1.644.847 2.943 1.515 5.823 1.515 2.88 0 4.179-.668 5.824-1.515C35.72 1.108 37.874 0 41.995 0c4.12 0 6.274 1.108 8.174 2.085 1.645.847 2.945 1.515 5.824 1.515 2.88 0 4.18-.668 5.826-1.515C63.719 1.108 65.873 0 69.993 0c4.12 0 6.274 1.108 8.174 2.085 1.645.847 2.945 1.515 5.823 1.515 2.88 0 4.18-.668 5.825-1.515C91.715 1.108 93.87 0 97.99 0c4.12 0 6.273 1.108 8.174 2.085 1.645.847 2.945 1.515 5.823 1.515 2.88 0 4.178-.668 5.824-1.515 1.9-.977 4.054-2.085 8.173-2.085 4.12 0 6.273 1.108 8.174 2.085 1.646.847 2.945 1.515 5.825 1.515 2.88 0 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Luckily, we know how to do that and will tell you in this article.&nbsp;<\/p>\n","protected":false},"author":20413,"featured_media":31658,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1688],"tags":[2074,173,2072],"class_list":["post-29856","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-fintech-business","tag-microservices","tag-software-development-process","tag-software-infrastructure"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Data Analytics on Amazon Web Services - AWS Big Data | Code &amp; Pepper<\/title>\n<meta name=\"description\" content=\"Analytics on Amazon is key to success when going with AWS cloud. 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