Persisting data from Amazon Kinesis using Amazon Kinesis Firehose is a popular pattern for streaming projects. However, building real-time analytics on these data introduces challenges, including managing the format, size and frequency of the files created. This session will present an end-to-end use case for deploying machine learning streaming analytics at-scale using Structured Streaming on Databricks. We will deploy a high-volume Kinesis producer, persist the data to S3 using Kinesis Firehose, partition and write the data using Parquet, create a machine learning model and, finally, query and visualize the data in real time. Key takeaways include: – Create a Kinesis producer – Persist to S3 using Kinesis Firehose – ETL, machine learning, and exploratory data analysis using Structured Streaming