Technology Category
- Application Infrastructure & Middleware - Data Visualization
- Robots - Parallel Robots
Applicable Industries
- Buildings
- Cement
Applicable Functions
- Quality Assurance
Use Cases
- Building Automation & Control
- Construction Management
Services
- Testing & Certification
About The Customer
Honeywell is a global provider of industry-specific solutions, delivering products and services in the aerospace sector, control technologies for buildings and industry, and performance materials. Honeywell's Energy and Environmental Solutions division uses IoT sensors and other technologies to help businesses worldwide manage energy demand, reduce energy consumption and carbon emissions, optimize indoor air quality, and improve occupant well-being. Honeywell's solutions and services are used in millions of buildings around the world, requiring the company to collect and manage vast amounts of data from a multitude of sources.
The Challenge
Honeywell, a global provider of industry-specific solutions, is under increasing pressure to reduce energy use, lower costs, and improve efficiency. Their Energy and Environmental Solutions division uses IoT sensors and other technologies to help businesses manage energy demand, reduce energy consumption and carbon emissions, optimize indoor air quality, and improve occupant well-being. This requires Honeywell to collect vast amounts of data from millions of buildings worldwide. These buildings are equipped with thousands of sensors monitoring factors such as temperature, pressure, humidity, and air quality. In addition to this, data is also collected from external sources like weather and pollution data, and information about the buildings themselves. At peak times, Honeywell ingests between 200 to 1,000 events per second for any building, equating to billions of data points per day. Honeywell's existing data infrastructure was struggling to meet this demand, making it difficult for the data team to query and visualize the disparate data to provide customers with fast, high-quality information and analysis.
The Solution
Honeywell adopted Delta Live Tables (DLT) on the Databricks Lakehouse Platform to manage their vast and varied data. This platform allowed Honeywell's data team to ingest billions of rows of sensor data into Delta Lake and automatically build SQL endpoints for real-time queries and multilayer insights into data at scale. The Databricks Lakehouse Platform helped unify Honeywell's batch, streaming, structured, and unstructured data into one platform. Honeywell's data engineers were able to build and leverage their own ETL pipelines with Delta Live Tables, gaining insights and analytics quickly. These ETL pipelines could be reused regardless of the environment, and data could run in batches or streams. DLT also helped Honeywell establish standard log files to monitor and cost-justify its product pipelines. The use of the lakehouse as a shared workspace promoted teamwork and collaboration, while the ability to manage streaming data with low latency and better throughput improved accuracy and reduced costs.
Operational Impact
Quantitative Benefit
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