---
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-0yoenjvdl
source_url: https://blog.quastor.org/p/how-uber-built-an-exabyte-scale-system-for-data-processing
type: article
source: "Quastor Daily"
published: 2025-01-16T15:17:10.151Z
updated: 2025-01-16T17:14:50.099Z
tags: ["big-data", "uber", "apache-spark", "data-processing", "etl"]
reading_time: 6
upvotes: 1
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)** · 6 min read · 1 upvotes · 0 comments

## Summary

Uber has revamped its ETL (Extract, Transform, Load) platform to manage its massive data processing needs. They developed Sparkle, a modular framework built on top of Apache Spark, to simplify the development and testing of ETL jobs. This framework enables the use of best practices in data engineering while allowing Uber to manage their vast data pipelines more efficiently.

## 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>

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---

Tags: [#big-data](https://daily.dev/tags/big-data), [#uber](https://daily.dev/tags/uber), [#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-0yoenjvdl)