{"id":2946825,"date":"2025-11-04T10:47:37","date_gmt":"2025-11-04T18:47:37","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2946825"},"modified":"2025-11-04T11:22:00","modified_gmt":"2025-11-04T19:22:00","slug":"from-data-to-insight-exploring-the-power-of-arcgis-in-microsoft-fabric-analysis","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis\/analytics\/from-data-to-insight-exploring-the-power-of-arcgis-in-microsoft-fabric-analysis","title":{"rendered":"From Data to Insight: Exploring the Power of ArcGIS in Microsoft Fabric Analysis"},"author":342532,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23341],"tags":[779142,766972,779152,780882,25631],"industry":[],"product":[421922,36761,778902],"class_list":["post-2946825","blog","type-blog","status-publish","format-standard","hentry","category-analytics","tag-arcgis-for-microsoft-fabric","tag-arcgis-for-power-bi","tag-arcgis-geoanalytics-for-microsoft-fabric","tag-microsoft-fabric","tag-spatial-analysis","product-arcgis","product-power-bi","product-geoanalytics-fabric"],"acf":{"authors":[{"ID":342532,"user_firstname":"Sarah","user_lastname":"Battersby","nickname":"Sarah Battersby","user_nicename":"sbattersby","display_name":"Sarah Battersby","user_email":"sbattersby@esri.com","user_url":"","user_registered":"2023-07-17 22:33:34","user_description":"Sarah is a Product Manager for ArcGIS GeoAnalytics Engine. She has a PhD in Geography \/ Cognitive Science from UC Santa Barbara, and enjoys finding ways to make spatial technologies easier to use, understand, and trust.","user_avatar":""},{"ID":393501,"user_firstname":"Akshay","user_lastname":"Harshe","nickname":"Akshay Harshe","user_nicename":"aharshe","display_name":"Akshay Harshe","user_email":"AHarshe@esri.com","user_url":"","user_registered":"2025-08-11 17:19:33","user_description":"","user_avatar":""},{"ID":297282,"user_firstname":"Sean","user_lastname":"McGinnis","nickname":"Sean McGinnis","user_nicename":"smcginnis","display_name":"Sean McGinnis","user_email":"SMcGinnis@esri.com","user_url":"","user_registered":"2021-12-15 14:33:46","user_description":"Sean McGinnis is the Principal Product Manager for the ArcGIS for Microsoft product group at Esri. He partners with organizations to observe and understand customer needs and works closely with other teams to prioritize and align Microsoft capabilities and integrations with ArcGIS.","user_avatar":""},{"ID":352692,"user_firstname":"Mariia","user_lastname":"Lubinova","nickname":"Mariia Lubinova","user_nicename":"mlubinova","display_name":"Mariia Lubinova","user_email":"mlubinova@esri.com","user_url":"","user_registered":"2024-02-07 23:40:22","user_description":"Mariia is a Product Marketing Manager on Esri's Spatial Analytics & Data Science team, with a background in Marketing and Business Development.","user_avatar":""}],"short_description":"Unlock the power of where in your data with Esri\u2019s integrations across Microsoft Fabric.","flexible_content":[{"acf_fc_layout":"content","content":"

ArcGIS for Microsoft Fabric enables organizations to leverage location analytics to drive insight generation within the Microsoft Fabric analytics environment.\u00a0 With Esri\u2019s integrations across Fabric, we can bring location context to your analytics workflows, unlocking the power of where in your data.<\/p>\n

In this blog post we will demonstrate analytics at scale using ArcGIS GeoAnalytics for Fabric and then show how to leverage those analytics for insight generation using ArcGIS for Power BI.<\/p>\n

In our example, we will explore a set of oil and gas pipeline data to understand their relationships to competitors, critical infrastructure, and environmental habitats of interest.\u00a0 At scale, these analyses benefit from the performant computing, analytics, and reporting capabilities within Microsoft Fabric, and allow an organization to drive insight, reduce risk, and elevate their operational awareness.\u00a0 With critical infrastructure data and analyses like we work with in this post, reducing time to decision is a priority.\u00a0 Time to decision needs to be minutes, not hours or days, but the scale of the data makes this challenging.\u00a0 ArcGIS GeoAnalytics for Fabric leverages a scalable analytic platform to slice through large volumes of geospatial data in the time needed for insight.<\/p>\n

But, driving insights isn\u2019t just about being able to analyze large volumes of data; we also need to be able to put our results to work to generate information products that put exploration and insight generation in the hands of everyone across the organization.<\/p>\n

Let\u2019s take a look at what we can do!<\/p>\n"},{"acf_fc_layout":"content","content":"

Data<\/strong><\/h2>\n"},{"acf_fc_layout":"content","content":"

For this example, our main dataset is oil and gas pipelines from Rextag Energy DataLink<\/a>. This dataset is the largest and most complete database on energy infrastructure assets in North America. With it, we are working with more than 1.5 million pipeline segments across North America.<\/p>\n

Organizations rarely have a single location where all data is stored, and no one wants to duplicate data unnecessarily to make that data accessible. Microsoft Fabric can help tame these data challenges by allowing access to data across a variety of platforms.\u00a0 With the ArcGIS integrations, we can open even more doors and allow easier access to spatial data, including your data within the Esri ecosystem.<\/p>\n

Using GeoAnalytics for Fabric, we can bring in data from a variety of data sources<\/a>\u00a0and analyze them together.\u00a0 For instance, if we have our company pipeline dataset in ArcGIS Enterprise, we can read the data as a feature service and analyze it in relation to other contextual datasets read from OneLake in Microsoft Fabric.<\/p>\n

While we will just show a few data sources and analytic capabilities with our examples, GeoAnalytics for Fabric can work with a wide range of spatial and non-spatial data sources, and has more than 180 spatial SQL functions<\/a>, track functions<\/a>, and tools<\/a> to tailor your analytics to meet your organizational needs.\u00a0 There is no moving of data, we simply read it into our Spark data frames and start generating insights. This sets us up for exploratory analysis of the results within Fabric using Power BI.<\/p>\n"},{"acf_fc_layout":"content","content":"

Analyzing our pipeline situation<\/strong><\/h2>\n"},{"acf_fc_layout":"content","content":"

To understand the pipeline data, we can start with understanding where our company pipelines intersect competitors.\u00a0 We can do this using the ST_Intersects<\/a> and ST_Intersection<\/a> functionality. This can tell us which segments have overlaps, and where these occur.\u00a0 To calculate this between almost 19,000 company pipelines and more than 1.4 million competitor pipelines takes just over 28 seconds in Fabric!<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2946832,"id":2946832,"title":"pipeline_intersects","filename":"pipeline_intersects.jpg","filesize":170704,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/pipeline_intersects.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis\/analytics\/from-data-to-insight-exploring-the-power-of-arcgis-in-microsoft-fabric-analysis\/pipeline_intersects","alt":"GeoAnalytics for Fabric pipeline analysis","author":"342532","description":"","caption":"Company pipelines in black with competitor pipelines in grey dashed lines. Points of intersection are identified with red triangles.","name":"pipeline_intersects","status":"inherit","uploaded_to":2946825,"date":"2025-11-04 00:07:34","modified":"2025-11-04 01:51:14","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1810,"height":531,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/pipeline_intersects-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/pipeline_intersects.jpg","medium-width":464,"medium-height":136,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/pipeline_intersects.jpg","medium_large-width":768,"medium_large-height":225,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/pipeline_intersects.jpg","large-width":1810,"large-height":531,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/pipeline_intersects-1536x451.jpg","1536x1536-width":1536,"1536x1536-height":451,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/pipeline_intersects.jpg","2048x2048-width":1810,"2048x2048-height":531,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/pipeline_intersects-826x242.jpg","card_image-width":826,"card_image-height":242,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/pipeline_intersects.jpg","wide_image-width":1810,"wide_image-height":531}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

We can scale this out and look at larger problems, for instance, where our company pipelines intersect with or are near to critical transportation infrastructure, such as roads<\/a> or railways.\u00a0 In this case, using GeoAnalytics for Fabric we can identify the over 59,000 points of intersection between our 19,000 company pipelines and more than 16 million<\/em> road segments in just under two minutes! This type of analysis is important to understand potential risks to the pipeline network from incidents along the transportation corridors, as well as to improve coordination with local agencies.<\/p>\n

Since incidents don\u2019t just impact a point location, we might also want to scale this up even further and identify all of the pipeline segments that are nearby<\/em> to the roads.\u00a0 In this case, we set a threshold of 100 meters, and identified all of the locations where the pipelines were within this distance of the roads \u2013 in just over one minute of analysis.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2946834,"id":2946834,"title":"pipeline_dwithin","filename":"pipeline_dwithin.jpg","filesize":66014,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/pipeline_dwithin.jpg","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis\/analytics\/from-data-to-insight-exploring-the-power-of-arcgis-in-microsoft-fabric-analysis\/pipeline_dwithin","alt":"GeoAnalytics for Fabric pipeline network analysis","author":"342532","description":"","caption":"100 meter distance around pipeline network.","name":"pipeline_dwithin","status":"inherit","uploaded_to":2946825,"date":"2025-11-04 00:08:55","modified":"2025-11-04 01:51:48","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1315,"height":316,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/pipeline_dwithin-213x200.jpg","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/pipeline_dwithin.jpg","medium-width":464,"medium-height":112,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/pipeline_dwithin.jpg","medium_large-width":768,"medium_large-height":185,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/pipeline_dwithin.jpg","large-width":1315,"large-height":316,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/pipeline_dwithin.jpg","1536x1536-width":1315,"1536x1536-height":316,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/pipeline_dwithin.jpg","2048x2048-width":1315,"2048x2048-height":316,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/pipeline_dwithin-826x198.jpg","card_image-width":826,"card_image-height":198,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/pipeline_dwithin.jpg","wide_image-width":1315,"wide_image-height":316}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

We could perform these same types of analysis with other datasets of interest, such as critical environmental habitats, so that we can understand the potential regulatory issues for our pipelines near to habitats, or to have rapid details on impacted species and habitats in case of an incident.\u00a0 In the next section we will set up our data to interactively explore this analysis of pipeline proximity to critical habitats.<\/p>\n"},{"acf_fc_layout":"content","content":"

Sharing our results<\/strong><\/h2>\n"},{"acf_fc_layout":"content","content":"

To use the results of our analysis more broadly for data modeling or interactive exploration, the datasets generated using GeoAnalytics for Fabric can be written to Fabric\u2019s OneLake or back into your Esri environment for further use and sharing across your organization.
\nThe analysis we performed with GeoAnalytics for Fabric to assess proximity to critical habitats resulted in two datasets that we saved to OneLake and ArcGIS Online so that they were accessible for a variety of downstream uses.<\/p>\n

First, the enriched pipeline data with all attributes for modeling or reporting were saved in OneLake. This makes the dataset accessible to organizational users across the Fabric workspace and will be used directly in Power BI as a data layer. This dataset does not have geometry enabled but has key attributes that will link it to geometry in Power BI.<\/p>\n

We also wrote a dataset directly as a feature service in ArcGIS Online. This dataset has geometry for visualization and can be used for mapping and modeling across the Esri ecosystem, but can also link to our Delta table in Power BI to facilitate map-based exploration in our dashboard.<\/p>\n

Together, these datasets allow users to visualize the data on a map using ArcGIS for Power BI alongside other Power BI report charts and tables, enabling stakeholders to engage with the data, ask informed questions, and make more informed decisions.<\/p>\n"},{"acf_fc_layout":"content","content":"

Visualization and analysis in Power BI<\/strong><\/h2>\n"},{"acf_fc_layout":"content","content":"

Now let\u2019s take a look at how we can use these value-added datasets that were generated through analysis with GeoAnalytics for Fabric.\u00a0 In this section, we will demonstrate picking those results up to visualize the results to generate an interactive dashboard that can be used across our organization to evaluate the situation of our pipeline infrastructure relative to critical habitats.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2946864,"id":2946864,"title":"PBI Report - Critical habitats within 100m or pipelines 1 (1)","filename":"PBI-Report-Critical-habitats-within-100m-or-pipelines-1-1.gif","filesize":25252443,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/PBI-Report-Critical-habitats-within-100m-or-pipelines-1-1.gif","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis\/analytics\/from-data-to-insight-exploring-the-power-of-arcgis-in-microsoft-fabric-analysis\/pbi-report-critical-habitats-within-100m-or-pipelines-1-1","alt":"Power BI dashboard showing critical habitats near pipelines","author":"352692","description":"","caption":"Power BI dashboard showing critical habitats near pipelines","name":"pbi-report-critical-habitats-within-100m-or-pipelines-1-1","status":"inherit","uploaded_to":2946825,"date":"2025-11-04 00:44:54","modified":"2025-11-04 01:04:11","menu_order":0,"mime_type":"image\/gif","type":"image","subtype":"gif","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1520,"height":856,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/PBI-Report-Critical-habitats-within-100m-or-pipelines-1-1-213x200.gif","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/PBI-Report-Critical-habitats-within-100m-or-pipelines-1-1.gif","medium-width":464,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/PBI-Report-Critical-habitats-within-100m-or-pipelines-1-1.gif","medium_large-width":768,"medium_large-height":433,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/PBI-Report-Critical-habitats-within-100m-or-pipelines-1-1.gif","large-width":1520,"large-height":856,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/PBI-Report-Critical-habitats-within-100m-or-pipelines-1-1.gif","1536x1536-width":1520,"1536x1536-height":856,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/PBI-Report-Critical-habitats-within-100m-or-pipelines-1-1.gif","2048x2048-width":1520,"2048x2048-height":856,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/PBI-Report-Critical-habitats-within-100m-or-pipelines-1-1-826x465.gif","card_image-width":826,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/PBI-Report-Critical-habitats-within-100m-or-pipelines-1-1.gif","wide_image-width":1520,"wide_image-height":856}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Our goal was to create a dashboard that not only helps us understand the potential regulatory issues for our pipelines near habitats, but also provides rapid details on impacted species and habitats in case of an incident. By leveraging interactive maps and visualizations, we can craft a narrative that tells a cohesive story about our pipeline infrastructure and its relationship to surrounding habitats. This enables effective collaboration and insight-sharing with our team, facilitating a more comprehensive understanding of the complex issues at hand.<\/p>\n

To get the data in shape for interactive exploration in Power BI, we start by connecting to our two data sources in OneLake and ArcGIS Online to set up the necessary data relationships. \u00a0We have geometries in our feature service in ArcGIS Online and our critical attributes in OneLake. Using a common key, we can join these two datasets together by setting up a relationship in Power BI.<\/a><\/p>\n

 <\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2946961,"id":2946961,"title":"ArcGIS for Power BI Analysis tab_new3","filename":"ArcGIS-for-Power-BI-Analysis-tab_new3.png","filesize":83350,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/ArcGIS-for-Power-BI-Analysis-tab_new3.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis\/analytics\/from-data-to-insight-exploring-the-power-of-arcgis-in-microsoft-fabric-analysis\/arcgis-for-power-bi-analysis-tab_new3","alt":"ArcGIS for Power BI Analysis tab","author":"352692","description":"Join Power BI data and a feature layer for visualization and analysis in a Power BI dashboard","caption":"","name":"arcgis-for-power-bi-analysis-tab_new3","status":"inherit","uploaded_to":2946825,"date":"2025-11-04 19:18:44","modified":"2025-11-04 19:20:30","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":866,"height":651,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/ArcGIS-for-Power-BI-Analysis-tab_new3-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/ArcGIS-for-Power-BI-Analysis-tab_new3.png","medium-width":347,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/ArcGIS-for-Power-BI-Analysis-tab_new3.png","medium_large-width":768,"medium_large-height":577,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/ArcGIS-for-Power-BI-Analysis-tab_new3.png","large-width":866,"large-height":651,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/ArcGIS-for-Power-BI-Analysis-tab_new3.png","1536x1536-width":866,"1536x1536-height":651,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/ArcGIS-for-Power-BI-Analysis-tab_new3.png","2048x2048-width":866,"2048x2048-height":651,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/ArcGIS-for-Power-BI-Analysis-tab_new3-619x465.png","card_image-width":619,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/ArcGIS-for-Power-BI-Analysis-tab_new3.png","wide_image-width":866,"wide_image-height":651}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Now that we have our datasets linked, we can generate our dashboard and create various visuals, such as bar charts and graphs, to illustrate the extent of damage to critical or endangered species within 100 meters of our pipelines if an incident occurs.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2946842,"id":2946842,"title":"PowerBI dashboard","filename":"PowerBI-dashboard.png","filesize":638941,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/PowerBI-dashboard.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis\/analytics\/from-data-to-insight-exploring-the-power-of-arcgis-in-microsoft-fabric-analysis\/powerbi-dashboard","alt":"PowerBI dashboard","author":"342532","description":"","caption":"Creating an interactive dashboard in Power BI to show the relationship of pipelines and critical habitats","name":"powerbi-dashboard","status":"inherit","uploaded_to":2946825,"date":"2025-11-04 00:16:39","modified":"2025-11-04 01:52:53","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1430,"height":731,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/PowerBI-dashboard-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/PowerBI-dashboard.png","medium-width":464,"medium-height":237,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/PowerBI-dashboard.png","medium_large-width":768,"medium_large-height":393,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/PowerBI-dashboard.png","large-width":1430,"large-height":731,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/PowerBI-dashboard.png","1536x1536-width":1430,"1536x1536-height":731,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/PowerBI-dashboard.png","2048x2048-width":1430,"2048x2048-height":731,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/PowerBI-dashboard-826x422.png","card_image-width":826,"card_image-height":422,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/PowerBI-dashboard.png","wide_image-width":1430,"wide_image-height":731}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

By creating an interactive and intuitive dashboard, we can facilitate effective communication across teams, allowing everyone to explore the data, identify key trends, and gain a deeper understanding of the potential risks.<\/p>\n"},{"acf_fc_layout":"content","content":"

Conclusion<\/strong><\/h2>\n

This blog demonstrated how ArcGIS for Microsoft Fabric can be used to analyze oil and gas pipeline data, understanding their relationships to competitors, critical infrastructure, and environmental habitats. The capabilities shown here are not limited to the energy sector, they can be applied to multiple business problems across various industries, significantly enhancing decision-making by providing deeper insights into complex spatial data. ArcGIS GeoAnalytics for Fabric enables organizations to integrate, transform, enrich, and drive location analytics at scale, driving insights and informed decision-making. By combining ArcGIS GeoAnalytics for Fabric and ArcGIS for Power BI, teams can analyze complex spatial data, visualize critical relationships, and share insights effectively across the organization.<\/p>\n"}],"related_articles":"","show_article_image":false,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/11\/ArcGIS-for-Fabric-card.png","wide_image":false},"yoast_head":"\nExploring the Power of ArcGIS in Microsoft Fabric Analysis<\/title>\n<meta name=\"description\" content=\"Analytics at scale using ArcGIS GeoAnalytics for Fabric and how to leverage those analytics for insight generation using ArcGIS for Power BI.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis\/analytics\/from-data-to-insight-exploring-the-power-of-arcgis-in-microsoft-fabric-analysis\" \/>\n<meta property=\"og:locale\" content=\"en_US\" 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