---
title: "How Uber Built an Exabyte-Scale System for Data Processing"
url: https://daily.dev/posts/how-uber-built-an-exabyte-scale-system-for-data-processing-3wz2ejth7
source_url: https://blog.quastor.org/p/how-uber-built-an-exabyte-scale-system-for-data-processing-e6dc
type: article
source: "Quastor Daily"
published: 2025-01-16T17:14:19.346Z
updated: 2025-01-16T17:14:49.131Z
tags: ["data-engineering", "apache-spark", "data-processing", "etl"]
reading_time: 5
upvotes: 0
comments: 0
language: en
---

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# How Uber Built an Exabyte-Scale System for Data Processing

**[Quastor Daily](https://daily.dev/sources/quastor)** · 5 min read · 0 upvotes · 0 comments

## Summary

Uber handles its massive data needs with an Exabyte-scale ETL system, scaling their data processing with Apache Spark and a custom framework called Sparkle. The revamped architecture focuses on modularity, reliability, and observability, handling extensive data generated from their services. Key tools in their ETL processes include Apache Spark, dbt, Apache Airflow, AWS Glue, and Google Cloud Dataflow.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blog.quastor.org/p/how-uber-built-an-exabyte-scale-system-for-data-processing-e6dc>

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---

Tags: [#data-engineering](https://daily.dev/tags/data-engineering), [#apache-spark](https://daily.dev/tags/apache-spark), [#data-processing](https://daily.dev/tags/data-processing), [#etl](https://daily.dev/tags/etl)

[View this post on daily.dev](https://daily.dev/posts/how-uber-built-an-exabyte-scale-system-for-data-processing-3wz2ejth7)
