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Welcome to our session on how to harness the potential of SQL streaming analytics in Databricks, a unified data analytics platform. We will explore how we could utilize SQL in Databricks to gain real-time insights from our data.
What is Streaming Analytics?
Streaming analytics involves analysing data in real-time as it comes in. Data could be streaming in from various sources like IoT devices, user interaction events, financial transactions, and more. Append it to your existing datasets; let’s look at the syntax:
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CREATE OR REPLACE TABLE events ( date DATE, eventId STRING, eventType STRING, data STRING) USING DELTA LOCATION ‘/delta/events’ |
Utilizing SQL for Streaming Analytics
Databricks provides a Stream processing API that lets you write SQL queries that continuously process incoming data. Here is an example of how you can write a streaming SQL query:
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CREATE OR REPLACE TABLE default.per_device_raw_data USING DELTA SELECT device_type, device_operational_status, COUNT(*) FROM deviceIoTdata GROUP BY device_type, device_operational_status; |
You can see that the data processed by the query is always available in the ‘per_device_raw_data’ table, kept up to date as new data streams in.
Real-time Insights with Databricks
With Databricks, you can collect real-time insights through live dashboards. Here is an example of SQL you might run regularly to keep the dashboard data up-to-date:
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SELECT device_type, device_operational_status, COUNT(*) FROM deviceIoTdata WHERE device_operational_status = 'faulty' GROUP BY device_type, device_operational_status; |
Conclusion
With SQL streaming analytics in Databricks, real-time insights from data become a reality rather than a possibility. Stay ahead with instant updates and make your business more dynamic and responsive.