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Using Apache Spark Structured Streaming on Azure Databricks for Predictive Maintenance
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Talk by: Jan-Philipp Simen (ZEISS Digital Innovation Partners)
We use Apache Spark Structured Streaming on the Databricks Unified Analytics Platform to process live data and Spark MLlib to train models for predicting machine failure. Structured Streaming and MLlib combined in the Zeiss Measuring Capability App allows users to stay on top of all relevant machine information and to know at a glance if a machine is capable of performing reliably. We will demonstrate how Azure Databricks allows us to easily schedule and monitor an increasing number of Spark jobs, continuously adding new features to our app.
About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business.
Connect with us:
We use Apache Spark Structured Streaming on the Databricks Unified Analytics Platform to process live data and Spark MLlib to train models for predicting machine failure. Structured Streaming and MLlib combined in the Zeiss Measuring Capability App allows users to stay on top of all relevant machine information and to know at a glance if a machine is capable of performing reliably. We will demonstrate how Azure Databricks allows us to easily schedule and monitor an increasing number of Spark jobs, continuously adding new features to our app.
About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business.
Connect with us:
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