Lyft serves more than 30 million riders, 2 million drivers, and has facilitated over 1 billion rides since 2012. The company operates across the United States and Canada. Lyft employs some of the leading experts in infrastructure and analytics. However, its exploding growth forced the company to move quickly to keep up with the demands of customers, business leaders and the underlying systems that support the company's operations.
In this case study, learn how
Lyft collaborated with us to develop analytical models that enable and support significant growth to keep up with the demands of customers, business leaders, and underlying systems that support the company’s operations.
Lyft migrated existing process to connect BI tools to Redshift to perform complex analysis and ad hoc queries.
Petabytes of data on legacy systems was not accessible for analytics.
Costly and prohibitive delayed in real time data and analytical processing.
Unable to meet a level of agility critical to maintain a competitive advantage.
Proven expertise and deep knowledge in ML algorithms.
Strong experience in developing reliable predictive models.
Proof of Concept to establish the right modeling approach.
In order to address the performance issues, Lyft wanted to migrate legacy systems and move to scalable cloud architecture. In order to do this, they needed to move a large volume of data into AWS S3 buckets. Existing processes needed to be rewritten and users would need to be able to connect BI tools to Redshift to perform complex analysis and ad hoc queries.
They decided to collaborate with our team for the migration due to the consultancy’s solutions-oriented approach and library of pre-built frameworks. After completing a proof of concept to validate the new architecture, our team members were integrated into the Lyft team full time to orchestrate and troubleshoot the migration.
With scalable cloud architecture in place, business users were unleashed and could perform analytics in an environment that scaled to meet their peak needs. The reduced load on the infrastructure team also enabled them to focus on higher value activities.
Authorize analysts to access to over 2PB of legacy data.
Lower costs by 38% with improved DBA performance.
Automate workload management for increased agility and scalability.
Reduced load on the infrastructure team enabled focus on higher value activities.