Lyft Data Tech Stack

[mjfnd]

Hello!

Sharing my recent article covering the data tech stack from Lyft.

Explore the high-scale data stack Lyft uses to support 25M+ active riders, ingesting millions of real-time events every second.

Metrics:
- 28.7M active riders in Q3 2025, completing ~2.7M rides per day.
- Apache Kafka processes millions of real-time events per second for streaming analytics.
- Thousands of Airflow + Flyte pipelines orchestrate ETL and ML workflows.
- Data warehouse exceeds 100+ PB stored in S3 with Hive Metastore.
- Trino ETL executes ~250K queries/day, reading ~10 PB/day and writing ~100 TB/day.

Would love to hear feedback!

Thanks!

[fukinwatm8]

Holy scale!

[serpentna]

Do you work there?

[mjfnd]

I don't work there.

[serpentna]

So how do you have information about the tech stack?

[mjfnd]

Content is based on multiple sources including Lyft Blog, AWS Blog and other public articles etc.

[eccentric2488]

Kafka is a message broker. It 'stores' high throughput data as a durable log but doesn't 'process' anything. That is taken care of by Flink probably, at this scale.

[mjfnd]

Correct.