Quby

Customer Case Study

Quby is technology company behind Toon, the smart energy management device that gives people control over their energy usage, their comfort, the security of their homes, and more.

Industry

Consumer Technology

Vertical Use Case

Connected Home Platform – Leverage machine learning and IOT to create consumer-driven services that improve the home experience.

Technical Use Case

Data Ingest and ETL
Streaming
Machine Learning
Deep Learning

Customer Testimonial

The Challenges

On-premise Hadoop cluster was very difficult and costly to scale to meet their big data collection needs.
The amount of time to manage infrastructure was too high – often times a data engineer would spend 1-2 days clusters to ensure a new package was installed correctly and dependencies well managed.
Data scientists had a range of experience and backgrounds which impeded sharing and collaboration across the team.
Limited experience with Apache Spark, big data, and data science.

The Solution

Databricks provides Quby with a unified analytics platform that has fostered a collaborative environment across data science and engineering, allowing them to innovate faster.

Fully managed platform with automated cluster management simplifies the infrastructure and operations at any scale.
Support for multiple languages (SQL, Scala, Python, R) to ensure all team members are productive within the collaborative notebook environment.
Shared notebooks fostered collaboration, allowing them to develop code in a very systematic way.
Able to easily leverage the latest deep learning frameworks and technologies such as TensorFlow and Keras.

Databricks, through the power of Delta and Structured Streaming, allows us to deliver alerts to our product’s users with a very limited latency, so they’re able to react to problems within their home before it affects their comfort levels.

Steven Galsworthy: Head of Data Science at Quby

The Results

Databricks has provided Quby with a Unified Analytics Platform that allows both data engineers and data scientists to be productive with the data and to accelerate innovation with machine learning.

Able to manage entire data science technology stack with a very slim team./li>
Reduced the amount of time on DevOps work so that their team can focus on the data.
Cost saving features such as auto-scaling and Spot instances has reduced the operational costs of managing infrastructure, while still being able to process large amounts of data.

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