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Build Real-Time Applications with Databricks Streaming 

Databricks
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In this presentation, we will study a recent use case we implemented recently. In this use case we are working with a large, metropolitan fire department. Our company has already created a complete analytics architecture for the department based upon Azure Data Factory, Databricks, Delta Lake, Azure SQL and Azure SQL Server Analytics Services (SSAS). While this architecture works very well for the department, they would like to add a real-time channel to their reporting infrastructure.
This channel should serve up the following information: •The most up-to-date locations and status of equipment (fire trucks, ambulances, ladders etc.)
• The current locations and status of firefighters, EMT personnel and other relevant fire department employees
• The current list of active incidents within the city The above information should be visualized through an automatically updating dashboard. The central component of the dashboard will be map which automatically updates with the locations and incidents. This view should be as real-time as possible and will be used by the fire chiefs to assist with real-time decision-making on resource and equipment deployments.
In this presentation, we will leverage Databricks, Spark Structured Streaming, Delta Lake and the Azure platform to create this real-time delivery channel.
Connect with us:
Website: databricks.com
Facebook: / databricksinc
Twitter: / databricks
LinkedIn: / databricks
Instagram: / databricksinc Databricks is proud to announce that Gartner has named us a Leader in both the 2021 Magic Quadrant for Cloud Database Management Systems and the 2021 Magic Quadrant for Data Science and Machine Learning Platforms. Download the reports here. databricks.com/databricks-nam...

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3 авг 2024

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Комментарии : 8   
@tiffanys_life374
@tiffanys_life374 10 дней назад
very genuine and detailed video! Appreciate for sharing! Love it
@SB-yb3us
@SB-yb3us 2 года назад
Cool demo, thank you!
@suniguha
@suniguha Год назад
Excellent presentation Sir! Thank you.
@apoorvsrivastava7121
@apoorvsrivastava7121 2 года назад
Super kool We use airflow to orchestrate data as we only have Daily and weekly data and adf ui is not that good to monitor, but we also have created an ingestion monitoring notebook so when ever our adf is trigger db is also updated for that particular file and we have some view in powerbi we also create our dashboard according to business logic when we implement in external table of databricks (delta) but ur presentation already give some idea how we could improve Thanks for your presentation 👍
@zelsu5646
@zelsu5646 2 года назад
Awesome demo really!!!!
@apoorvsrivastava7121
@apoorvsrivastava7121 2 года назад
Where could I find notebooks code etc for this demo
@moesmael
@moesmael Год назад
Very interesting presentation. But what I did not understand is. When you connected Power BI, did you connect it to the Data lake folder Gold or silver , or you read directly from unit_status delta Lake in Data-bricks. Your reply is highly appreciated.
@film-masti-777
@film-masti-777 9 месяцев назад
Good tutorial video. 1 question: instead of merging into the final target table from source dataframe (for each microbatch), can't we leverage: Autoloader's writeStream with outputMode("complete") ?
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