{"id":28101,"date":"2021-04-08T15:00:15","date_gmt":"2021-04-08T14:00:15","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-gb\/industry\/blog\/?p=28101"},"modified":"2022-02-10T21:05:43","modified_gmt":"2022-02-10T20:05:43","slug":"using-power-bi-and-logic-apps-to-analyse-social-media-streams","status":"publish","type":"post","link":"https:\/\/cm-edgetun.pages.dev\/en-gb\/industry\/blog\/technetuk\/2021\/04\/08\/using-power-bi-and-logic-apps-to-analyse-social-media-streams\/","title":{"rendered":"Using Power BI and Logic Apps\u200b to analyse social media streams"},"content":{"rendered":"<p><img loading=\"lazy\" decoding=\"async\" class=\"attachment-full size-full webp-format\" src=\"https:\/\/cm-edgetun.pages.dev\/en-gb\/industry\/blog\/wp-content\/uploads\/sites\/22\/2020\/01\/CloudblogsBitTemplateHeaderNew.jpg\" alt=\"An illustration of a cloud made of puzzle pieces, with a drawing of Bit the Raccoon to the right of the image.\" width=\"1920\" height=\"700\" data-orig-srcset=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2020\/01\/CloudblogsBitTemplateHeaderNew.jpg 1920w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2020\/01\/CloudblogsBitTemplateHeaderNew-300x109.jpg 300w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2020\/01\/CloudblogsBitTemplateHeaderNew-1024x373.jpg 1024w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2020\/01\/CloudblogsBitTemplateHeaderNew-768x280.jpg 768w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2020\/01\/CloudblogsBitTemplateHeaderNew-1536x560.jpg 1536w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2020\/01\/CloudblogsBitTemplateHeaderNew-330x120.jpg 330w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2020\/01\/CloudblogsBitTemplateHeaderNew-800x292.jpg 800w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2020\/01\/CloudblogsBitTemplateHeaderNew-400x146.jpg 400w\" data-orig-src=\"https:\/\/cm-edgetun.pages.dev\/en-gb\/industry\/blog\/wp-content\/uploads\/sites\/22\/2020\/01\/CloudblogsBitTemplateHeaderNew.jpg\"><\/p>\n<p>There are numerous technologies that can be utilised to stream, analyse and visualise tweets on Azure, including traditional Big Data technologies such as Hadoop, Kafka, Spark, Storm, No SQL DBs and more. However, there are also a number of PAAS services that can be utilised to achieve the same result, with much less coding, configuration and setup effort and improved maintenance and HA\/DR capabilities.<\/p>\n<p>One simple pattern for twitter analytics is this:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"attachment-full size-full webp-format\" src=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics1.png\" alt=\"A diagram showing one potential way of analysing social media - using logic apps, sql database and cognitive services to analyse Twitter, and display results in Power BI.\" width=\"1617\" height=\"480\" data-orig-srcset=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics1.png 1617w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics1-300x89.png 300w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics1-1024x304.png 1024w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics1-768x228.png 768w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics1-1536x456.png 1536w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics1-330x98.png 330w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics1-800x237.png 800w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics1-400x119.png 400w\" data-orig-src=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics1.png\"><\/p>\n<p><strong>LogicApp<\/strong>&nbsp;is used to pull the data from Twitter periodically using the inbuilt Twitter connector, the&nbsp;<strong>Cognitive Services Text API<\/strong>&nbsp;can be called from within&nbsp;<strong>LogicApp<\/strong>&nbsp;and the enriched data inserted into&nbsp;<strong>SQL DB,<\/strong>&nbsp;and&nbsp;<strong>Power BI<\/strong>&nbsp;can be used to generate aggregates\/measures\/KPIs and to visualise the data.&nbsp;<strong>Cognitive Services<\/strong>&nbsp;provides a variety of machine learning services which are extremely useful for social media analytics such as sentiment analysis, moderation (less blushes if the profanity is automatically removed), language translation and intent (whether they asking a question or making a complaint).<\/p>\n<ul>\n<li><strong>Logic Apps -&gt; Twitter Connect,<\/strong><\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"attachment-full size-full webp-format\" src=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics2.jpg\" alt=\"A screenshot of the Logic Apps Designer screen, showing which tweets will be pulled in using certain search terms.\" width=\"965\" height=\"409\" data-orig-srcset=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics2.jpg 965w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics2-300x127.jpg 300w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics2-768x326.jpg 768w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics2-330x140.jpg 330w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics2-800x339.jpg 800w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics2-400x170.jpg 400w\" data-orig-src=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics2.jpg\"><\/p>\n<ul>\n<li><strong>Logic Apps -&gt; Cognitive Services,<\/strong><\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"attachment-full size-full webp-format\" src=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics3.jpg\" alt=\"A screenshot showing Logic Apps Designer stating that Text Analytics will be run for each tweet pulled in.\" width=\"946\" height=\"705\" data-orig-srcset=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics3.jpg 946w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics3-300x224.jpg 300w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics3-768x572.jpg 768w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics3-330x246.jpg 330w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics3-800x596.jpg 800w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics3-400x298.jpg 400w\" data-orig-src=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics3.jpg\"><\/p>\n<ul>\n<li><strong>Logic Apps -&gt; SQL Database<\/strong><\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"attachment-full size-full webp-format\" src=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics4.jpg\" alt=\"A screenshot showing the results of Text Analysis will be added to a row in a table.\" width=\"956\" height=\"462\" data-orig-srcset=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics4.jpg 956w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics4-300x145.jpg 300w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics4-768x371.jpg 768w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics4-330x159.jpg 330w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics4-800x387.jpg 800w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics4-400x193.jpg 400w\" data-orig-src=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics4.jpg\"><\/p>\n<ul>\n<li><strong>Power BI -&gt; SQL Database<\/strong><\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"attachment-full size-full webp-format aligncenter\" src=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics5.jpg\" alt=\"A screenshot showing the creation of a new Azure SQL Database.\" width=\"467\" height=\"508\" data-orig-srcset=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics5.jpg 467w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics5-276x300.jpg 276w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics5-230x250.jpg 230w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics5-322x350.jpg 322w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics5-400x435.jpg 400w\" data-orig-src=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics5.jpg\"><\/p>\n<p>This is an awesome architecture for rapidly prototyping a solution and allows for business rules\/logic to be changed rapidly. However, for a production architecture it has weaknesses:<\/p>\n<ul>\n<li>Aggregate generation in&nbsp;<strong>Power BI<\/strong>&nbsp;allows for rapid rule changes but doesn\u2019t allow those rules to be shared across multiple reports, requiring the rules to be recreated in each report. This has obvious data governance and quality concerns. These calculations should be done in the DB, so that a consistent set of rules can be applied across all reports. An example PowerBI dashboard is shown below (this is available as a Power Bi Solution Template):<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"attachment-full size-full webp-format\" src=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/social2.jpg\" alt=\"A screenshot showing an example dashboard in Power BI.\" width=\"1243\" height=\"670\" data-orig-srcset=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/social2.jpg 1243w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/social2-300x162.jpg 300w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/social2-1024x552.jpg 1024w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/social2-768x414.jpg 768w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/social2-330x178.jpg 330w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/social2-800x431.jpg 800w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/social2-400x216.jpg 400w\" data-orig-src=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/social2.jpg\"><\/p>\n<ul>\n<li><strong>Logic Apps<\/strong>&nbsp;is exposed directly to the Twitter API. While it&#8217;s possible to add code buffering logic and time delays into Logic Apps to handle high volume tweets, other components are available.<\/li>\n<li>This is hot path analytics only. There\u2019s no provision for cold\/batch analytics.<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"attachment-full size-full webp-format\" src=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics6.png\" alt=\"An updated diagram for Tweet analysis, including Stream Analytics, Blob Storage and a SQL Database.\" width=\"1366\" height=\"601\" data-orig-srcset=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics6.png 1366w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics6-300x132.png 300w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics6-1024x451.png 1024w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics6-768x338.png 768w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics6-330x145.png 330w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics6-800x352.png 800w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics6-400x176.png 400w\" data-orig-src=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics6.png\"><\/p>\n<p>The architecture can be enhanced as per the above, with the addition of&nbsp;<strong>Event Hub<\/strong>,&nbsp;<strong>Stream Analytics<\/strong>&nbsp;and&nbsp;<strong>Blob Storage:<\/strong><\/p>\n<ul>\n<li><strong>Event Hub<\/strong>&nbsp;uncouples the Twitter connectivity and acts as a highly scalable buffer for incoming tweets to prevent the overloading of components downstream. It can stream to multiple outputs so it could also stream a raw feed to&nbsp;<strong>Blob Storage,<\/strong>&nbsp;but this would lack the enrichment from sentiment analysis etc.<\/li>\n<li>Similarly to&nbsp;<strong>Logic Apps<\/strong>,&nbsp;<strong>Stream Analytics<\/strong>&nbsp;can make direct calls to&nbsp;<strong>Cognitive Services<\/strong>&nbsp;to perform sentiment analysis, moderation etc. However, Stream Analytics can also perform aggregations over multiple criteria and time windows and push the results in&nbsp;<strong>SQL DB.<\/strong>&nbsp;It can also stream a raw feed (with or without enrichment) into&nbsp;<strong>Blob storage<\/strong>. Data can also be streamed directly into&nbsp;<strong>Power BI<\/strong>&nbsp;to provide real-time reporting, rather than&nbsp;<em>near<\/em>&nbsp;real-time reporting.<\/li>\n<li>In-memory columnar storage can be utilised in&nbsp;<strong>SQL DB,<\/strong>&nbsp;with&nbsp;triggers used to calculate additional measures. This minimises the complexity of queries and the workload of&nbsp;<strong>Power BI,<\/strong>&nbsp;significantly decreasing the refresh time of reports which is critical in near-realtime reporting. It also ensures consistency of business rules for KPIs across multiple reports as the rules are stored in the DB and not the report.<\/li>\n<li>Passing the data into&nbsp;<strong>Blob Storage<\/strong>&nbsp;enables the data to be picked up by other Azure Data Services for additional \u201ccold path\u201d analytical processing or on-premise analytical systems. However using&nbsp;<strong>Blob storage<\/strong>&nbsp;is the equivalent of dumping data into files on a file share on-premise; There are more sophisticated, functionality richer services such as&nbsp;<strong>Azure Data Lake<\/strong>&nbsp;or&nbsp;<strong>Azure CosmosDB<\/strong>&nbsp;that would be better choices. Blob storage does, however, make an excellent transitional staging area before batch moving the data into Azure Data Lake. Just don\u2019t try and use&nbsp;<strong>Blob Storage<\/strong>&nbsp;as a \u201cData Lake\u201d or you will most likely end up with a \u201cData swamp\u201d or \u201cDigital Data Land Fill\u201d. Once in the&nbsp;<strong>Data Lake<\/strong>&nbsp;the data can be further utilised and analysed by&nbsp;<strong>Azure Data Lake analytics<\/strong>,&nbsp;<strong>Machine Learning Studio\/Workbench<\/strong>,&nbsp;<strong>HDInsight<\/strong>,&nbsp;<strong>SQL Data Warehouse<\/strong>,&nbsp;<strong>Databricks<\/strong>&nbsp;and&nbsp;<strong>Azure Search,<\/strong>&nbsp;depending on the use case. This is illustrated below:<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"attachment-full size-full webp-format\" src=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics7.png\" alt=\"An expanded diagram showing the process with further additions, including machine learning, data lake analytics and HDInsight.\" width=\"1377\" height=\"723\" data-orig-srcset=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics7.png 1377w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics7-300x158.png 300w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics7-1024x538.png 1024w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics7-768x403.png 768w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics7-1200x630.png 1200w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics7-600x315.png 600w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics7-330x173.png 330w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics7-800x420.png 800w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics7-400x210.png 400w\" data-orig-src=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics7.png\"><\/p>\n<p>Additional workflow processing for scenarios such as responding to customer complaints and queries, identifying and pursuing leads, modifying marketing campaigns, enhancing events experience can handled either by:<\/p>\n<ul>\n<li>Hot path processing utilising&nbsp;<strong>Logic Apps<\/strong>&nbsp;to process a real-time stream from&nbsp;<strong>Stream Analytics<\/strong>. This allows the stream to be pre-processed before passing into&nbsp;<strong>Logic Apps,<\/strong>&nbsp;minimising the workload on&nbsp;<strong>Logic Apps<\/strong>.&nbsp;<strong>Logic Apps<\/strong>&nbsp;can make additional calls to cognitive and other services as well providing conditional workflow management and passing the message for appropriate processing downstream by, for example, a Call handler, notification hub, email, a tweet, or enhancing&nbsp;<strong>bot<\/strong>&nbsp;interaction. A 2nd&nbsp;<strong>Event Hub<\/strong>&nbsp;should be utilised between&nbsp;<strong>Stream Analytics<\/strong>&nbsp;and&nbsp;<strong>Logic Apps<\/strong>&nbsp;to provide a buffer between the two and allow&nbsp;<strong>Logic Apps<\/strong>&nbsp;to consume messages at its own cadence.<\/li>\n<\/ul>\n<p><strong>And\/Or<\/strong><\/p>\n<ul>\n<li>Cold path processing utilising, for example,&nbsp;<strong>Azure Data Lake Analytics<\/strong>&nbsp;to analyse and pull data. Using parameters or machine learning, data can be pulled out of the cold data store and pushed onto a message queue for appropriate processing downstream.<\/li>\n<\/ul>\n<p>These options are illustrated below:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"attachment-full size-full webp-format\" src=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics8.png\" alt=\"An updated diagram showing even more options, such as outputting analytics directly to bots, customer specialists or emails.\" width=\"1294\" height=\"785\" data-orig-srcset=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics8.png 1294w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics8-300x182.png 300w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics8-1024x621.png 1024w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics8-768x466.png 768w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics8-330x200.png 330w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics8-800x485.png 800w, https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics8-400x243.png 400w\" data-orig-src=\"https:\/\/cm-edgetun.pages.dev\/en-us\/industry\/blog\/wp-content\/uploads\/sites\/22\/2021\/04\/twitteranalytics8.png\"><\/p>\n<p>The decision point on the 2 methods is down to the use case and what the response time and analytics window for tweets are. For instance, altering a marketing campaign based on tweets received over a week would be an appropriate cold path use case. Responding to a customer complaint or query is an appropriate hot path use case.<\/p>\n<p>&nbsp;<\/p>\n<h2>Resources<\/h2>\n<ul>\n<li><a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/paths\/create-use-analytics-reports-power-bi?ocid=AID3020565\" target=\"_blank\" rel=\"noopener noreferrer\">Create and use analytics reports with Power BI<\/a><\/li>\n<li><a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/get-data-power-bi?ocid=AID3020565\" target=\"_blank\" rel=\"noopener noreferrer\">Get data with Power BI Desktop<\/a><\/li>\n<li><a href=\"https:\/\/docs.microsoft.com\/en-us\/learn\/modules\/explore-data-power-bi?ocid=AID3020565\" target=\"_blank\" rel=\"noopener noreferrer\">Explore data in Power BI<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>There are numerous technologies that can be utilised to stream, analyse and visualise tweets on Azure, including traditional Big Data technologies such as Hadoop, Kafka, Spark, Storm, No SQL DBs and more. However, there are also a number of PAAS services that can be utilised to achieve the same result, with much less coding, configuration and setup effort and improved maintenance and HA\/DR capabilities.<\/p>\n","protected":false},"author":430,"featured_media":21801,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"ep_exclude_from_search":false,"_classifai_error":"","_classifai_text_to_speech_error":"","footnotes":""},"categories":[594],"post_tag":[307,1631,519],"content-type":[],"coauthors":[1260],"class_list":["post-28101","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technetuk","tag-power-bi","tag-power-platform","tag-technet-uk"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Using Power BI and Logic Apps\u200b to analyse social media streams - Microsoft Industry Blogs - United Kingdom<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cm-edgetun.pages.dev\/en-gb\/industry\/blog\/technetuk\/2021\/04\/08\/using-power-bi-and-logic-apps-to-analyse-social-media-streams\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Using Power BI and Logic Apps\u200b to analyse social media streams - Microsoft Industry Blogs - United Kingdom\" \/>\n<meta property=\"og:description\" content=\"There are numerous technologies that can be utilised to stream, analyse and visualise tweets on Azure, including traditional Big Data technologies such as Hadoop, Kafka, Spark, Storm, No SQL DBs and more. 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