Real-time Analytics
ATAL Engineering Group needed a better way to support building energy analytics and optimization for large commercial properties because an on-premises analytics stack could not process historical building data fast enough or cheaply enough to support sustainable building operations at scale.
Reported impact
ATAL Engineering Group improved building energy analytics and optimization for large commercial properties with measurable results including 200% increase in analytics performance, 50% reduction in platform management time, and 80% decrease in downtime, while giving teams a faster path from governed data to production decisions.
ATAL Engineering Group uses Delta Lake and Amazon Bedrock to support building energy analytics and optimization for large commercial properties. The implementation replaced a legacy state where an on-premises analytics stack could not process historical building data fast enough or cheaply enough to support sustainable building operations at scale. Building sensor and operations data and historical energy consumption move through production data pipelines and land in Delta Lake and Amazon S3, giving teams one trusted data foundation. Central governance keeps access, lineage, and compliance consistent across downstream teams. Amazon Bedrock, Amazon SageMaker, and Amazon EMR operationalize models, agents, and analytics that deliver optimization recommendations and energy analytics dashboards.
Building sensor and operations data and historical energy consumption land on a shared platform, pipeline services clean and standardize them, governance controls enforce trusted access, and AI, model-serving, and analytics services deliver optimization recommendations and energy analytics dashboards.
If we need to analyze data over several years, we can maintain the same level of performance by scaling up the AWS platform,
The AWS team trained us on Amazon QuickSight, illustrating their ongoing technical support throughout the project and further reinforcing our satisfaction with AWS.
We chose AWS because of the maturity of its solutions and the availability of AWS developers in Hong Kong,
We want to improve the value of ATAL by prioritizing software development over fundamental infrastructure tasks,
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