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Enterprise Data Analytics on AWS Redshift and Tableau

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About Automotive and Logistics Company

KPI's client is an American electric vehicle automaker and automotive technology company founded in 2009. Client 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

Technology

  • Amazon RedShift KPI Partners
  • Tableau KPI Partners-Mar-18-2022-08-16-21-82-AM
  • AmazonKinesis KPI Partners
  • AMazon Cloudwatch
  • AWS Lamda KPI Partners
  • AmazonKinesis KPI Partners
  • Airflow KPI Partners-1
  • Python_KPI Partners


Business Needs

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

 

Selection Process

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


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, client website, 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

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