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    <title>topic Uber Rides Analysis in Data Stories Gallery</title>
    <link>https://community.fabric.microsoft.com/t5/Data-Stories-Gallery/Uber-Rides-Analysis/m-p/4868645#M15237</link>
    <description>&lt;P&gt;&lt;SPAN class=""&gt;&lt;span class="lia-unicode-emoji" title=":sparkles:"&gt;✨&lt;/span&gt;Project Highlights&lt;span class="lia-unicode-emoji" title=":sparkles:"&gt;✨&lt;/span&gt;:&lt;/SPAN&gt;&lt;BR /&gt;&lt;SPAN&gt;&lt;span class="lia-unicode-emoji" title=":diamond_with_a_dot:"&gt;💠&lt;/span&gt; Created a&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class=""&gt;&lt;A href="https://www.linkedin.com/search/results/all/?keywords=%23lakehouse&amp;amp;origin=HASH_TAG_FROM_FEED" target="_blank" rel="noreferrer noopener"&gt;#Lakehouse&lt;/A&gt;&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;(Data Lake) and&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class=""&gt;&lt;A href="https://www.linkedin.com/search/results/all/?keywords=%23warehouse&amp;amp;origin=HASH_TAG_FROM_FEED" target="_blank" rel="noreferrer noopener"&gt;#Warehouse&lt;/A&gt;&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;in the Microsoft Fabric.&lt;/SPAN&gt;&lt;BR /&gt;&lt;SPAN&gt;&lt;span class="lia-unicode-emoji" title=":diamond_with_a_dot:"&gt;💠&lt;/span&gt; Ingest five different data sources (JSON, CSV, Excel, SQL, and XML) to the Lakehouse by leveraging&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class=""&gt;&lt;A href="https://www.linkedin.com/search/results/all/?keywords=%23datapipelines&amp;amp;origin=HASH_TAG_FROM_FEED" target="_blank" rel="noreferrer noopener"&gt;#DataPipelines&lt;/A&gt;&lt;/SPAN&gt;&lt;SPAN&gt;.&lt;/SPAN&gt;&lt;BR /&gt;&lt;SPAN&gt;&lt;span class="lia-unicode-emoji" title=":diamond_with_a_dot:"&gt;💠&lt;/span&gt; Created a&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class=""&gt;&lt;A href="https://www.linkedin.com/search/results/all/?keywords=%23dataflow&amp;amp;origin=HASH_TAG_FROM_FEED" target="_blank" rel="noreferrer noopener"&gt;#Dataflow&lt;/A&gt;&lt;/SPAN&gt;&lt;SPAN&gt;, the data was pulled from Lakehouse to dataflow and perform data transformations and publish it to warehouse.&lt;/SPAN&gt;&lt;BR /&gt;&lt;SPAN&gt;&lt;span class="lia-unicode-emoji" title=":diamond_with_a_dot:"&gt;💠&lt;/span&gt; Created a final data pipeline (&lt;/SPAN&gt;&lt;SPAN class=""&gt;&lt;A href="https://www.linkedin.com/search/results/all/?keywords=%23invokepipeline&amp;amp;origin=HASH_TAG_FROM_FEED" target="_blank" rel="noreferrer noopener"&gt;#InvokePipeline&lt;/A&gt;&lt;/SPAN&gt;&lt;SPAN&gt;) and orchestrated with source pipeline and transformation pipeline to get final output.&lt;/SPAN&gt;&lt;BR /&gt;&lt;SPAN&gt;&lt;span class="lia-unicode-emoji" title=":diamond_with_a_dot:"&gt;💠&lt;/span&gt; By leveraging OneLake, data was pulled to Power BI desktop and crafted a report.&lt;/SPAN&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;SPAN class=""&gt;&lt;span class="lia-unicode-emoji" title=":memo:"&gt;📝&lt;/span&gt; Report Highlights:&lt;/SPAN&gt;&lt;BR /&gt;&lt;SPAN class=""&gt;&lt;span class="lia-unicode-emoji" title=":house:"&gt;🏠&lt;/span&gt; Home Tab:&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;It's crafted in a way that how the uber carrying a ride from home to destination. By using this tab, we can navigate to other tabs.&lt;/SPAN&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;SPAN class=""&gt;&lt;span class="lia-unicode-emoji" title=":chart_increasing:"&gt;📈&lt;/span&gt; Trend Analysis:&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;We can see few key KPIs and current year (CY) v/s last year (LY) trends in the same visual also we can see numbers as well in a tabular format.&lt;/SPAN&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;SPAN class=""&gt;&lt;span class="lia-unicode-emoji" title=":money_bag:"&gt;💰&lt;/span&gt;Revenue:&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;We can see revenue by top rider, user and location. There are other insights those will give revenue by ride time, ride type and ride day. Additionally, we can see comparison of revenue growth over the time.&lt;/SPAN&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;SPAN class=""&gt;&lt;span class="lia-unicode-emoji" title=":taxi:"&gt;🚕&lt;/span&gt; Rides:&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;Rides by top rider, driver and location. There are other insights those will give rides by time, type and day. Additionally, we can see comparison of rides growth over the time.&lt;/SPAN&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;SPAN class=""&gt;🧑‍&lt;span class="lia-unicode-emoji" title=":airplane:"&gt;✈️&lt;/span&gt; Driver info:&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;We can see multiple driver related metrics like driver status, vehicle details, ride details, revenue details and the most important rating &lt;span class="lia-unicode-emoji" title=":star:"&gt;⭐&lt;/span&gt; is also here.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&lt;SPAN class="reportid hidden"&gt;eyJrIjoiODM1NmU1ZmEtODY0Yy00MzBiLWFlZGMtODlmMGJhY2M3OGNiIiwidCI6Ijk0ZWFkZTY1LTQ4NDEtNDIxNC05NjkxLTFiM2NkYWU1YTM3NyJ9&lt;/SPAN&gt;&lt;/P&gt;</description>
    <pubDate>Fri, 07 Nov 2025 04:55:10 GMT</pubDate>
    <dc:creator>ajaybabuinturi</dc:creator>
    <dc:date>2025-11-07T04:55:10Z</dc:date>
    <item>
      <title>Uber Rides Analysis</title>
      <link>https://community.fabric.microsoft.com/t5/Data-Stories-Gallery/Uber-Rides-Analysis/m-p/4868645#M15237</link>
      <description>&lt;P&gt;&lt;SPAN class=""&gt;&lt;span class="lia-unicode-emoji" title=":sparkles:"&gt;✨&lt;/span&gt;Project Highlights&lt;span class="lia-unicode-emoji" title=":sparkles:"&gt;✨&lt;/span&gt;:&lt;/SPAN&gt;&lt;BR /&gt;&lt;SPAN&gt;&lt;span class="lia-unicode-emoji" title=":diamond_with_a_dot:"&gt;💠&lt;/span&gt; Created a&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class=""&gt;&lt;A href="https://www.linkedin.com/search/results/all/?keywords=%23lakehouse&amp;amp;origin=HASH_TAG_FROM_FEED" target="_blank" rel="noreferrer noopener"&gt;#Lakehouse&lt;/A&gt;&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;(Data Lake) and&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class=""&gt;&lt;A href="https://www.linkedin.com/search/results/all/?keywords=%23warehouse&amp;amp;origin=HASH_TAG_FROM_FEED" target="_blank" rel="noreferrer noopener"&gt;#Warehouse&lt;/A&gt;&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;in the Microsoft Fabric.&lt;/SPAN&gt;&lt;BR /&gt;&lt;SPAN&gt;&lt;span class="lia-unicode-emoji" title=":diamond_with_a_dot:"&gt;💠&lt;/span&gt; Ingest five different data sources (JSON, CSV, Excel, SQL, and XML) to the Lakehouse by leveraging&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class=""&gt;&lt;A href="https://www.linkedin.com/search/results/all/?keywords=%23datapipelines&amp;amp;origin=HASH_TAG_FROM_FEED" target="_blank" rel="noreferrer noopener"&gt;#DataPipelines&lt;/A&gt;&lt;/SPAN&gt;&lt;SPAN&gt;.&lt;/SPAN&gt;&lt;BR /&gt;&lt;SPAN&gt;&lt;span class="lia-unicode-emoji" title=":diamond_with_a_dot:"&gt;💠&lt;/span&gt; Created a&amp;nbsp;&lt;/SPAN&gt;&lt;SPAN class=""&gt;&lt;A href="https://www.linkedin.com/search/results/all/?keywords=%23dataflow&amp;amp;origin=HASH_TAG_FROM_FEED" target="_blank" rel="noreferrer noopener"&gt;#Dataflow&lt;/A&gt;&lt;/SPAN&gt;&lt;SPAN&gt;, the data was pulled from Lakehouse to dataflow and perform data transformations and publish it to warehouse.&lt;/SPAN&gt;&lt;BR /&gt;&lt;SPAN&gt;&lt;span class="lia-unicode-emoji" title=":diamond_with_a_dot:"&gt;💠&lt;/span&gt; Created a final data pipeline (&lt;/SPAN&gt;&lt;SPAN class=""&gt;&lt;A href="https://www.linkedin.com/search/results/all/?keywords=%23invokepipeline&amp;amp;origin=HASH_TAG_FROM_FEED" target="_blank" rel="noreferrer noopener"&gt;#InvokePipeline&lt;/A&gt;&lt;/SPAN&gt;&lt;SPAN&gt;) and orchestrated with source pipeline and transformation pipeline to get final output.&lt;/SPAN&gt;&lt;BR /&gt;&lt;SPAN&gt;&lt;span class="lia-unicode-emoji" title=":diamond_with_a_dot:"&gt;💠&lt;/span&gt; By leveraging OneLake, data was pulled to Power BI desktop and crafted a report.&lt;/SPAN&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;SPAN class=""&gt;&lt;span class="lia-unicode-emoji" title=":memo:"&gt;📝&lt;/span&gt; Report Highlights:&lt;/SPAN&gt;&lt;BR /&gt;&lt;SPAN class=""&gt;&lt;span class="lia-unicode-emoji" title=":house:"&gt;🏠&lt;/span&gt; Home Tab:&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;It's crafted in a way that how the uber carrying a ride from home to destination. By using this tab, we can navigate to other tabs.&lt;/SPAN&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;SPAN class=""&gt;&lt;span class="lia-unicode-emoji" title=":chart_increasing:"&gt;📈&lt;/span&gt; Trend Analysis:&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;We can see few key KPIs and current year (CY) v/s last year (LY) trends in the same visual also we can see numbers as well in a tabular format.&lt;/SPAN&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;SPAN class=""&gt;&lt;span class="lia-unicode-emoji" title=":money_bag:"&gt;💰&lt;/span&gt;Revenue:&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;We can see revenue by top rider, user and location. There are other insights those will give revenue by ride time, ride type and ride day. Additionally, we can see comparison of revenue growth over the time.&lt;/SPAN&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;SPAN class=""&gt;&lt;span class="lia-unicode-emoji" title=":taxi:"&gt;🚕&lt;/span&gt; Rides:&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;Rides by top rider, driver and location. There are other insights those will give rides by time, type and day. Additionally, we can see comparison of rides growth over the time.&lt;/SPAN&gt;&lt;BR /&gt;&lt;BR /&gt;&lt;SPAN class=""&gt;🧑‍&lt;span class="lia-unicode-emoji" title=":airplane:"&gt;✈️&lt;/span&gt; Driver info:&lt;/SPAN&gt;&lt;SPAN&gt;&amp;nbsp;We can see multiple driver related metrics like driver status, vehicle details, ride details, revenue details and the most important rating &lt;span class="lia-unicode-emoji" title=":star:"&gt;⭐&lt;/span&gt; is also here.&lt;/SPAN&gt;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&lt;SPAN class="reportid hidden"&gt;eyJrIjoiODM1NmU1ZmEtODY0Yy00MzBiLWFlZGMtODlmMGJhY2M3OGNiIiwidCI6Ijk0ZWFkZTY1LTQ4NDEtNDIxNC05NjkxLTFiM2NkYWU1YTM3NyJ9&lt;/SPAN&gt;&lt;/P&gt;</description>
      <pubDate>Fri, 07 Nov 2025 04:55:10 GMT</pubDate>
      <guid>https://community.fabric.microsoft.com/t5/Data-Stories-Gallery/Uber-Rides-Analysis/m-p/4868645#M15237</guid>
      <dc:creator>ajaybabuinturi</dc:creator>
      <dc:date>2025-11-07T04:55:10Z</dc:date>
    </item>
    <item>
      <title>Re: Uber Rides Analysis</title>
      <link>https://community.fabric.microsoft.com/t5/Data-Stories-Gallery/Uber-Rides-Analysis/m-p/4869232#M15239</link>
      <description>&lt;P&gt;Thanks for Sharing &lt;a href="javascript:void(0)" data-lia-user-mentions="" data-lia-user-uid="753601" data-lia-user-login="ajaybabuinturi" class="lia-mention lia-mention-user"&gt;ajaybabuinturi&lt;/a&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Fri, 07 Nov 2025 14:37:48 GMT</pubDate>
      <guid>https://community.fabric.microsoft.com/t5/Data-Stories-Gallery/Uber-Rides-Analysis/m-p/4869232#M15239</guid>
      <dc:creator>NagaSuresh</dc:creator>
      <dc:date>2025-11-07T14:37:48Z</dc:date>
    </item>
    <item>
      <title>Re: Uber Rides Analysis</title>
      <link>https://community.fabric.microsoft.com/t5/Data-Stories-Gallery/Uber-Rides-Analysis/m-p/4871689#M15257</link>
      <description>&lt;P&gt;The design is very organized and makes the information easy to understand&lt;/P&gt;</description>
      <pubDate>Tue, 11 Nov 2025 06:09:34 GMT</pubDate>
      <guid>https://community.fabric.microsoft.com/t5/Data-Stories-Gallery/Uber-Rides-Analysis/m-p/4871689#M15257</guid>
      <dc:creator>Nabha-Ahmed</dc:creator>
      <dc:date>2025-11-11T06:09:34Z</dc:date>
    </item>
    <item>
      <title>Re: Uber Rides Analysis</title>
      <link>https://community.fabric.microsoft.com/t5/Data-Stories-Gallery/Uber-Rides-Analysis/m-p/4998274#M15717</link>
      <description>&lt;P&gt;Nice Dashboard&lt;/P&gt;</description>
      <pubDate>Thu, 05 Feb 2026 17:12:08 GMT</pubDate>
      <guid>https://community.fabric.microsoft.com/t5/Data-Stories-Gallery/Uber-Rides-Analysis/m-p/4998274#M15717</guid>
      <dc:creator>MohamadJavith</dc:creator>
      <dc:date>2026-02-05T17:12:08Z</dc:date>
    </item>
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