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Case Study: Rivian - Enterprise Data Analytics on AWS Redshift and Tableau

Leading Edge Data Analytics platform on AWS Cloud to address the key information needs and analytical insights for multiple business groups

About Rivian Automotive

Rivian-KPI PartnersRivian is an American electric vehicle automaker and automotive technology company founded in 2009. Rivian is building an electric sport utility vehicle and a pickup truck on a skateboard platform that can support future vehicles or be adopted by other companies

Rivian Automotive’s Business Needs

Rivian Automotive is in the early stages of releasing vehicles to customers. In the pre-production stage, Rivian needs to have centralized reporting on multiple source systems from ERP, Vehicle Testing, Claims, Vehicle data on the go, etc.

Rivian Automotive’s Selection Process

Rivian Automotive selected KPI over multiple vendors through a rigorous RFP process. KPI’s expertise in AWS Cloud, Tableau, Data Engineering was the key differentiator for Rivian Automotive. In addition, KPI’s blended shore model helped minimize cost and risk for Rivian Automotive.

What KPI Delivered

  • AWS Redshift-based Analytics system for data sourced from Facilities Operating System, Google Maps API, Sharepoint, Pre Order Portal, Salesforce, Amazon Connect, SAP, Rivian Web Site, Google Analytics, Workday, greenhouse, JAMA, JIRA, PLATO, GenSuite, Vehicle Cloud
  • KPI delivered data engineering and data analytics solutions in AWS Redshift for batch and real-time data by extracting data from a wide variety of source systems
  • Developed a deployment tool that helps to migrate Tableau workbooks among projects on the Tableau servers automatically
  • Built server audit reports which track tableau server health by tracking CPU usage, and Disk usage
  • Helped Quality, Finance, and Vehicle tracking teams to automate their requirements by converting some of the complex Excel reports to Tableau

Business Benefits

  • Redshift as analytics database where any business users can connect and have detailed insights into current and historical data
  • Robust Data engineering frameworks to extract any new data source and integrate to an existing analytics platform
  • Automated data quality checks to ensure data lake is accurate prior to ETL loads
  • Implemented Airflow to run 200+ jobs which include scheduling and monitoring the ETL jobs
  • Data unload to Athena enabled users to have more access to historical data
  • Automated Tableau maintenance activities with KPI's automation utilities deployed on Tableau server
  • Improved user adoption of Tableau

Tags: Case Study, Tableau, Amazon Redshift, AWS Glue, Balachandra Sridhara, AWS Cloud, AWS Lambda, Python pipelines, Step Functions, CloudWatch, Kinesis Stream, Salesforce REST API, Airflow

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