Meet the People Join the Team

Data

12 MAR

Hoodie: Uber Engineering’s Incremental Processing Framework on Hadoop

Uber Engineering's data processing platform team recently built and open sourced Hoodie, an incremental processing framework that supports our business critical data pipelines. In this article, we see how Hoodie powers a rich data ecosystem where external sources can be ingested into Hadoop in near real-time.

26 JUL

Why Uber Engineering Switched from Postgres to MySQL

Uber Engineering explains the technical reasoning behind its switch in database technologies, from Postgres to MySQL.

21 JUL

The Uber Engineering Tech Stack, Part II: The Edge and Beyond

The end of a two-part series on the tech stack that Uber Engineering uses to make transportation as reliable as running water, everywhere, for everyone, as of spring 2016.

19 JUL

The Uber Engineering Tech Stack, Part I: The Foundation

Uber’s mission is transportation as reliable as running water, everywhere, for everyone. Here's the first of a two-part series on the tech stack that Uber Engineering uses to make this happen.

3 MAY

Engineering Intelligence Through Data Visualization at Uber

The data visualization team in Uber Engineering delivers intelligence through crafting visual exploratory data analysis tools. Here's what some of these visualizations look like.

29 MAR

Streamific, the Ingestion Service for Hadoop Big Data at Uber Engineering

Here we look at Hadoop data ingestion, and how Uber Engineering streams diverse data into a cohesive layer for querying in near real-time using our in-house developed Streamific.

3 MAR

How Uber Thinks About Site Reliability Engineering

Uber’s mission is transportation as reliable as running water, for everyone, everywhere. This past month, Uber Engineering talked about what it takes to get site reliability engineering right.

16 FEB

How Uber Engineering Evaluated JSON Encoding and Compression Algorithms to Put the Squeeze on Trip Data

Imagine you have to store data whose massive influx increases by the hour. Your first priority, after making sure you can easily add storage capacity, is to try and reduce the data’s footprint to save space. But how? This is the story of Uber Engineering’s comprehensive encoding protocol and compression algorithm test and how this discipline saved space in our Schemaless datastores.

19 JAN

Using Triggers On Schemaless, Uber Engineering’s Datastore Using MySQL

The details and examples of Schemaless triggers, a key feature of the datastore that’s kept Uber Engineering scaling since October 2014. This is the third installment of a three-part series on Schemaless; the first part is a design overview and the second part is a discussion of architecture.

15 JAN

The Architecture of Schemaless, Uber Engineering’s Trip Datastore Using MySQL

How Uber’s infrastructure works with Schemaless, the datastore using MySQL that’s kept Uber Engineering scaling since October 2014. This is part two of a three-part series on Schemaless; part one is on designing Schemaless.

12 JAN

Designing Schemaless, Uber Engineering’s Scalable Datastore Using MySQL

The making of Schemaless, Uber Engineering’s custom designed datastore using MySQL, which has allowed us to scale from 2014 to beyond. This is part one of a three-part series on Schemaless.

28 JUL

Project Mezzanine: The Great Migration at Uber Engineering

What happens when you have to migrate hundreds of millions of rows of data and 100 services over several weeks with dozens of engineers, while simultaneously serving millions of rides? The story of how Uber moved to Mezzanine in 2014.

    Page 1 of 1