Powering Enterprise Digital Transformation at Sunrun
公司规模
Large Corporate
地区
- America
国家
- United States
产品
- Google Cloud BigQuery
- Looker
技术栈
- Cloud Dataflow
- LookML
- BigQuery query engine
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Cost Savings
技术
- 平台即服务 (PaaS) - 数据管理平台
- 分析与建模 - 实时分析
适用行业
- 可再生能源
适用功能
- 商业运营
用例
- 预测性维护
- 供应链可见性(SCV)
服务
- 云规划/设计/实施服务
- 数据科学服务
关于客户
Sunrun is the #1 residential leader in solar power. Every six minutes someone installs a Sunrun solar system. Sunrun offers clean, reliable, affordable solar energy and battery storage solutions to help save the environment, and save their customers money. Between 2007 and 2019, Sunrun produced 7.4 B kilowatt-hours of clean energy. And during 2019 alone, Sunrun saved their typical customers 10-40% on their energy bills, resulting in $300 M total savings. As demand for clean, renewable energy grows, Sunrun faces the challenge of scaling operations, production, and services so they can continue to provide an exceptional customer experience while creating a more sustainable future.
挑战
Sunrun, a leading provider of residential solar power, was facing challenges in managing their growing volumes of data across installation operations, installed systems, customer operations, and sales. The company was using a legacy data stack that required IT and data team support for almost every internal data request. This reliance on IT and the data team drained time and resources with ad-hoc requests, changing requirements, and backlogs of reporting requests. Moreover, the data pipelines and infrastructure weren’t scaling to meet either data growth or increased demand for data access. The data team struggled to respond to changing data sets or new sources of data as quickly as the business demanded, and Sunrun's legacy Oracle data warehouse was not equipped to scale across growing analytics demands or unlock predictive insights with ease.
解决方案
Sunrun decided to migrate to Google Cloud’s smart analytics platform — including Looker and BigQuery — to reduce ETL complexity, run fast queries with ease, and make data accessible and trusted throughout the organization. Rather than build complicated data pipelines with complex ETL processes, Sunrun loaded most data directly into BigQuery without transformation. Sunrun leveraged the power of BigQuery and Cloud Dataflow to transform approximately 20% of the data available in BigQuery. However, the majority of data transformation occurred at query time through a combination of Looker’s Git-versioned data modeling layer, LookML, and the BigQuery query engine. This allowed Sunrun to avoid complicated, brittle, and expensive ETL processes, and simplified the data pipeline. Sunrun’s cloud migration was finished in only 18 months, and today they are 100% in the Cloud with improved access to trusted metrics for their executives and different required departments.
运营影响
数量效益
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