Google Cloud Platform Case Studies Powering Enterprise Digital Transformation at Sunrun

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	Powering Enterprise Digital Transformation at Sunrun Google Cloud Platform

Powering Enterprise Digital Transformation at Sunrun

Google Cloud Platform

Technology Category	Analytics & Modeling - Real Time Analytics Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries	Renewable Energy
Applicable Functions	Business Operation
Use Cases	Predictive Maintenance Supply Chain Visibility
Services	Cloud Planning, Design & Implementation Services Data Science Services
Challenge	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. Read More
About Customer	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. Read More
Solution	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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Real Time Analytics

Platform as a Service (PaaS) - Data Management Platforms

Applicable Industries

Renewable Energy

Applicable Functions

Business Operation

Use Cases

Predictive Maintenance

Supply Chain Visibility

Services

Cloud Planning, Design & Implementation Services

Data Science Services

Challenge

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.

About Customer

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.

Solution

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.

Impact #1	Sunrun has experienced a 50% reduction in data warehouse design time, ETL, and data modeling.
Impact #2	Modernizing and simplifying their architecture helped Sunrun reduce their entire data development cycle by 60%+ to enable accelerated decision-making.
Impact #3	Sunrun leverages a hub-and-spoke analytics model to provide self-service analytics across their core business, ensuring all metrics are governed and trusted.

Impact #1

Sunrun has experienced a 50% reduction in data warehouse design time, ETL, and data modeling.

Impact #2

Modernizing and simplifying their architecture helped Sunrun reduce their entire data development cycle by 60%+ to enable accelerated decision-making.

Impact #3

Sunrun leverages a hub-and-spoke analytics model to provide self-service analytics across their core business, ensuring all metrics are governed and trusted.

Benefit #1	50% reduction in data warehouse design time, ETL, and data modeling.
Benefit #2	60%+ reduction in data development cycle.
Benefit #3	60% gains in engineering time efficiency.

Benefit #1

50% reduction in data warehouse design time, ETL, and data modeling.

Benefit #2

60%+ reduction in data development cycle.

Benefit #3

60% gains in engineering time efficiency.

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Overview

Powering Enterprise Digital Transformation at Sunrun

Operational Impact

Quantitative Benefit