From the course: Azure Spark Databricks Essential Training
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Azure Databricks for churn analysis
From the course: Azure Spark Databricks Essential Training
Azure Databricks for churn analysis
- [Instructor] Now, let's apply our Pipelining to a business scenario, Churn Analysis. You might have data scientists working with multiple Machine Learning algorithms, because they're going to evaluate and tune them, both for efficiency effectiveness, but also for performance overhead. You'll be working with visualizations. You'll probably have Streaming data, Meta data, and Historical data. So, what might that look like with Databricks Azure in a Pipeline? Here's one possible case. You might remember that in this course, we took a look at a new capability called Structured Streaming. So, in this case, we're bringing in some Twitter data. We probably would bring in more information than Twitter for a real live customer churn out, unless we we're Twitter, so you'd have more data sources. And you can see that in Ingest, Prepare, and Analyze section of the Pipeline, we have Databricks that is playing the central role. It's interacting with the Azure Event Hubs, and there are multiple…
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