How AWS helps Variant employ a driver-first data-driven strategy
summary
Variant builds a serverless Variant Data Lake on AWS, ingesting IoT, telematics, call center and vendor APIs into S3, converting to Parquet, governing with Lake Formation and querying with Glue, Athena and QuickSight. Pipelines use EventBridge, Step Functions, Lambda, DMS, RabbitMQ and EKS patterns so standard ingestion that used to take days now deploys in hours. The system supports 4 trucks scaling to 1,800, codifies S3→Glue→Athena→QuickSight as a fixed layer, defines seven pipeline patterns, and starts measuring home-time and attrition to validate impact.
AI-written summary · about
chapters (7)
- 0:00Introduction to Variant
- 1:26Role of truck drivers
- 7:09Who is Variant
- 10:16Building a data lake
- 15:57Solution impact on business
- 21:35Why this matters
- 21:57Q&A session
related talks
44mBest practices for analytics and generative AI on AWS(ANT302)
28mTurn Data Into Insights
43mManaging a Data Lake on AWS
49mBuilding an Enterprise Data Lake Architecture on AWS
40mHow Teradata uses AWS to power enterprise analytics
42mBuild and automate a modern serverless data lake on
19mBuilding modern data lakes on Amazon S3
29mHow financial services companies derive greater value from data with AWS
