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Heetch's Elastic AI Strategy Development with Dataiku
Technology Category
- Analytics & Modeling - Machine Learning
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Cement
- Education
Applicable Functions
- Warehouse & Inventory Management
Use Cases
- Fraud Detection
- Transportation Simulation
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
The Challenge
Heetch, a French mobility company, was struggling with the management of large quantities of data gathered from drivers, passengers, and global operations. As the company grew, the costs of their data warehouse were spiraling out of control and performance was suffering due to the increasing volume of data. They needed a solution that would allow anyone in the organization to work with large amounts of data while also ensuring optimized resource allocation. The challenge was to find a way to leverage big data with good performance and at reasonable costs, which required serious computational power, optimized resource consumption, and isolated environments for development and production. Managing all these aspects was becoming increasingly complex for the organization.
The Customer
Heetch
About The Customer
Heetch is a French company founded in 2013 with the goal of making mobility more accessible by offering a smooth user experience. The company has grown quickly to 250 employees and has gathered troves of data from drivers, passengers, global operations, and more since its launch. However, they struggled to scale their ability to leverage that data. Five years in, data warehouse costs were spiraling out of control, and performance was suffering as the amount of data grew. The company needed to find a solution that would allow anyone across the organization to work with large amounts of data while also ensuring optimized resource allocation.
The Solution
Heetch chose Dataiku as their single platform for building data pipelines and processing raw data, paired with Looker for the seamless visualization and exploration of those flows. Dataiku and Kubernetes were used to address their primary pain point: leveraging data while maintaining good performance and reasonable costs. Thanks to Dataiku’s native integration with major cloud vendors’ managed Kubernetes services, Heetch was able to integrate their AWS EKS cluster very quickly and saw a drastic increase in value from their data. Teams can now easily offload resource-intensive workloads, like big Python and R jobs, and leverage the EKS cluster to distribute compute and run Spark jobs. To control costs, Heetch also used Dataiku for resource consumption optimization, differentiating CPU-vore clusters from Memory-vore clusters to optimize user experience and computation speed depending on the type of job launched.
Operational Impact
Quantitative Benefit
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