The Databricks Lakehouse Platform is an open architecture that combines the best elements of data lakes and data warehouses. In this demo, we’ll show you how to build an IOT platform for predictive maintenance, ingesting sensor data from our wind turbine farm in real time. We’ll be able to deliver data and insights that would typically take months of effort on legacy platforms.

This demo covers the end-to-end lakehouse platform:

• Ingest data from external systems in streaming (sensors/ERP) and then transform it using Delta Live Tables (DLT), a declarative ETL framework for building reliable, maintainable and testable data processing pipelines
• Secure your ingested data to ensure governance and security
• Leverage Databricks SQL and the warehouse endpoints to build a dashboard to analyze the ingested data and our wind farm productivity
• Build a machine learning model with Databricks AutoML to detect faulty wind turbines and trigger predictive maintenance operations
• Orchestrate all these steps with Databricks Workflows

To install the demo, get a free Databricks workspace and execute the following two commands in a Python notebook

%pip install dbdemos

import dbdemos
dbdemos.install('lakehouse-iot-platform', catalog='main', schema='dbdemos_iot_turbine')

Dbdemos is a Python library that installs complete Databricks demos in your workspaces. Dbemos will load and start notebooks, Delta Live Tables pipelines, clusters, Databricks SQL dashboards, warehouse models … See how to use dbdemos

Dbdemos is distributed as a GitHub project.

For more details, please view the GitHub README.md file and follow the documentation.
Dbdemos is provided as is. See the License and Notice for more information.
Databricks does not offer official support for dbdemos and the associated assets.
For any issue, please open a ticket and the demo team will have a look on a best-effort basis.