uber.com_engineering.rss.xml - sfeed_tests - sfeed tests and RSS and Atom files
(HTM) git clone git://git.codemadness.org/sfeed_tests
(DIR) Log
(DIR) Files
(DIR) Refs
(DIR) README
(DIR) LICENSE
---
uber.com_engineering.rss.xml (11233B)
---
1 <?xml version="1.0" encoding="utf-8" ?>
2 <rss version="2.0">
3 <channel>
4 <title>
5 <![CDATA[Engineering | Uber Blog]]>
6 </title>
7 <link>https://www.uber.com</link>
8 <description>
9 <![CDATA[]]>
10 </description>
11 <lastBuildDate>Fri Jan 31 2025 10:04:42 GMT+0000 (Coordinated Universal Time)</lastBuildDate>
12 <language>en</language>
13 <item>
14 <title>
15 <![CDATA[MySQL At Uber]]>
16 </title>
17 <link>
18 <![CDATA[https://www.uber.com/blog/mysql-at-uber/]]>
19 </link>
20 <pubDate>
21 <![CDATA[2025-01-30 14:00:00]]>
22 </pubDate>
23 <category>
24 <![CDATA[Backend]]>
25 </category>
26 <description>
27 <![CDATA[<p>How does Uber achieve 99.99% availability across 2,000+ MySQL® clusters? Learn how we manage our MySQL fleet at scale, from architecture to control plane optimizations.</p>
28 ]]>
29 </description>
30 </item>
31 <item>
32 <title>
33 <![CDATA[How Uber Uses Ray® to Optimize the Rides Business]]>
34 </title>
35 <link>
36 <![CDATA[https://www.uber.com/blog/how-uber-uses-ray-to-optimize-the-rides-business/]]>
37 </link>
38 <pubDate>
39 <![CDATA[2025-01-09 14:00:00]]>
40 </pubDate>
41 <category>
42 <![CDATA[Backend]]>
43 </category>
44 <description>
45 <![CDATA[<p>Large-scale computation is a major back end and infrastructure challenge for Uber to solve as we scale. We applied a compute engine called Ray® in Uber’s marketplace to improve computation efficiency and engineering productivity.</p>
46 ]]>
47 </description>
48 </item>
49 <item>
50 <title>
51 <![CDATA[Serving Millions of Apache Pinot™ Queries with Neutrino]]>
52 </title>
53 <link>
54 <![CDATA[https://www.uber.com/blog/serving-millions-of-apache-pinot-queries-with-neutrino/]]>
55 </link>
56 <pubDate>
57 <![CDATA[2024-12-11 14:00:00]]>
58 </pubDate>
59 <category>
60 <![CDATA[Data / ML]]>
61 </category>
62 <description>
63 <![CDATA[<p>At Uber, we serve 500 million Pinot queries every day. Learn how we optimized and built an internal fork of Presto to support query features like window functions and sub-queries, all while supporting sub-second latencies at thousands of QPS.</p>
64 ]]>
65 </description>
66 </item>
67 <item>
68 <title>
69 <![CDATA[Introducing the Prompt Engineering Toolkit]]>
70 </title>
71 <link>
72 <![CDATA[https://www.uber.com/blog/introducing-the-prompt-engineering-toolkit/]]>
73 </link>
74 <pubDate>
75 <![CDATA[2024-11-26 14:00:00]]>
76 </pubDate>
77 <category>
78 <![CDATA[Data / ML]]>
79 </category>
80 <description>
81 <![CDATA[<p>LLM iteration can happen with speed and safety! Explore how Uber launched a prompt toolkit for LLMs that helps engineers create, manage, and evaluate prompts with dynamic contextualization, batch generation, and robust safety checks.</p>
82 ]]>
83 </description>
84 </item>
85 <item>
86 <title>
87 <![CDATA[The Accounter: Scaling Operational Throughput on Uber’s Stateful Platform]]>
88 </title>
89 <link>
90 <![CDATA[https://www.uber.com/blog/the-accounter/]]>
91 </link>
92 <pubDate>
93 <![CDATA[2024-11-21 14:00:00]]>
94 </pubDate>
95 <category>
96 <![CDATA[Backend]]>
97 </category>
98 <description>
99 <![CDATA[<p>Uber slashed operational costs using The Accounter—an intelligent state manager that orchestrates large-scale tasks across our entire infrastructure, optimized for time and resources.</p>
100 ]]>
101 </description>
102 </item>
103 <item>
104 <title>
105 <![CDATA[Unified Checkout: Streamlining Uber’s Payment Ecosystem]]>
106 </title>
107 <link>
108 <![CDATA[https://www.uber.com/blog/unified-checkout/]]>
109 </link>
110 <pubDate>
111 <![CDATA[2024-11-14 14:00:00]]>
112 </pubDate>
113 <category>
114 <![CDATA[Backend]]>
115 </category>
116 <description>
117 <![CDATA[<p>From payment chaos to calm: Discover how Uber’s game-changing Unified Checkout System powers payment methods across every product line, worldwide.</p>
118 ]]>
119 </description>
120 </item>
121 <item>
122 <title>
123 <![CDATA[Presto® Express: Speeding up Query Processing with Minimal Resources]]>
124 </title>
125 <link>
126 <![CDATA[https://www.uber.com/blog/presto-express/]]>
127 </link>
128 <pubDate>
129 <![CDATA[2024-11-07 14:00:00]]>
130 </pubDate>
131 <category>
132 <![CDATA[Data / ML]]>
133 </category>
134 <description>
135 <![CDATA[<p>Slow Presto® queries can hinder data-driven operations. At Uber, we designed Presto express to achieve a 50% improvement in the end-to-end SLA for 70% of queries using query analysis, real-time insights, and resource isolation.</p>
136 ]]>
137 </description>
138 </item>
139 <item>
140 <title>
141 <![CDATA[Enabling Infinite Retention for Upsert Tables in Apache Pinot]]>
142 </title>
143 <link>
144 <![CDATA[https://www.uber.com/blog/enabling-infinite-retention-for-upsert-tables/]]>
145 </link>
146 <pubDate>
147 <![CDATA[2024-10-31 13:00:00]]>
148 </pubDate>
149 <category>
150 <![CDATA[Data / ML]]>
151 </category>
152 <description>
153 <![CDATA[<p>With contributions from Uber and others, Apache Pinot™ now supports deletion with upsert tables! Learn how Uber drove these advancements and how you can benefit from cost-efficient infinite retention.</p>
154 ]]>
155 </description>
156 </item>
157 <item>
158 <title>
159 <![CDATA[Streamlining Financial Precision: Uber’s Advanced Settlement Accounting System]]>
160 </title>
161 <link>
162 <![CDATA[https://www.uber.com/blog/ubers-advanced-settlement-accounting-system/]]>
163 </link>
164 <pubDate>
165 <![CDATA[2024-10-24 13:00:00]]>
166 </pubDate>
167 <category>
168 <![CDATA[Backend]]>
169 </category>
170 <description>
171 <![CDATA[<p>Discover how Uber’s cutting-edge settlement accounting system processes over 1.2 billion transactions monthly, ensuring precise financial tracking, preventing fraud, and managing regulatory compliance with unmatched efficiency.</p>
172 ]]>
173 </description>
174 </item>
175 <item>
176 <title>
177 <![CDATA[Open Source and In-House: How Uber Optimizes LLM Training]]>
178 </title>
179 <link>
180 <![CDATA[https://www.uber.com/blog/open-source-and-in-house-how-uber-optimizes-llm-training/]]>
181 </link>
182 <pubDate>
183 <![CDATA[2024-10-17 15:30:00]]>
184 </pubDate>
185 <category>
186 <![CDATA[Engineering]]>
187 </category>
188 <description>
189 <![CDATA[<p>Exploring beyond third-party LLMs, Uber leverages in-house LLM training to embed domain-specific knowledge and support GenAI applications. Embracing open-source solutions unlocks top-tier training throughput and GPU utilization.</p>
190 ]]>
191 </description>
192 </item>
193 <item>
194 <title>
195 <![CDATA[Genie: Uber’s Gen AI On-Call Copilot]]>
196 </title>
197 <link>
198 <![CDATA[https://www.uber.com/blog/genie-ubers-gen-ai-on-call-copilot/]]>
199 </link>
200 <pubDate>
201 <![CDATA[2024-10-10 13:00:00]]>
202 </pubDate>
203 <category>
204 <![CDATA[Data / ML]]>
205 </category>
206 <description>
207 <![CDATA[<p>Explore how Uber is leveraging Genie, its Generative AI-powered On-Call CoPilot, to transform on-call operations and empower engineering teams.</p>
208 ]]>
209 </description>
210 </item>
211 <item>
212 <title>
213 <![CDATA[Making Uber’s ExperimentEvaluation Engine 100x Faster]]>
214 </title>
215 <link>
216 <![CDATA[https://www.uber.com/blog/making-ubers-experiment-evaluation-engine-100x-faster/]]>
217 </link>
218 <pubDate>
219 <![CDATA[2024-10-03 13:00:00]]>
220 </pubDate>
221 <category>
222 <![CDATA[Backend]]>
223 </category>
224 <description>
225 <![CDATA[<p>Learn how Uber was able to reduce evaluation latencies by a factor of 100x in their Experimentation platform, which is used to empower decision making across the company by processing over 10 million evaluations per second.</p>
226 ]]>
227 </description>
228 </item>
229 <item>
230 <title>
231 <![CDATA[Preon: Presto Query Analysis for Intelligent and Efficient Analytics]]>
232 </title>
233 <link>
234 <![CDATA[https://www.uber.com/blog/preon/]]>
235 </link>
236 <pubDate>
237 <![CDATA[2024-09-26 13:00:00]]>
238 </pubDate>
239 <category>
240 <![CDATA[Data / ML]]>
241 </category>
242 <description>
243 <![CDATA[<p>Discover how to enable intelligent and efficient data analytics at Uber scale with Preon, a Presto Query Analysis service that unlocks insights for deduplicating queries, creating efficient table layouts, and more.</p>
244 ]]>
245 </description>
246 </item>
247 <item>
248 <title>
249 <![CDATA[How to Measure Design System at Scale]]>
250 </title>
251 <link>
252 <![CDATA[https://www.uber.com/blog/design-system-at-scale/]]>
253 </link>
254 <pubDate>
255 <![CDATA[2024-09-24 13:00:00]]>
256 </pubDate>
257 <category>
258 <![CDATA[Engineering]]>
259 </category>
260 <description>
261 <![CDATA[<p>Learn how Uber made a breakthrough in tracking design metrics across Figma, Android, and iOS with Design System Observability.</p>
262 ]]>
263 </description>
264 </item>
265 <item>
266 <title>
267 <![CDATA[QueryGPT – Natural Language to SQL Using Generative AI]]>
268 </title>
269 <link>
270 <![CDATA[https://www.uber.com/blog/query-gpt/]]>
271 </link>
272 <pubDate>
273 <![CDATA[2024-09-19 13:00:00]]>
274 </pubDate>
275 <category>
276 <![CDATA[Backend]]>
277 </category>
278 <description>
279 <![CDATA[<p>Discover how QueryGPT revolutionizes SQL query generation at Uber! Learn about the cutting-edge AI that turns natural language prompts into efficient SQL queries, boosting productivity at Uber. Dive into our journey of innovation and transformation. </p>
280 ]]>
281 </description>
282 </item>
283 <item>
284 <title>
285 <![CDATA[Transforming Executive Travel: Delegate Booking with Uber]]>
286 </title>
287 <link>
288 <![CDATA[https://www.uber.com/blog/executive-travel/]]>
289 </link>
290 <pubDate>
291 <![CDATA[2024-09-12 13:00:00]]>
292 </pubDate>
293 <category>
294 <![CDATA[Backend]]>
295 </category>
296 <description>
297 <![CDATA[<p>Find out how Uber for Business launched delegate profiles on Administrative Professionals Day, empowering executive assistants to manage executive travel, streamlining processes, and optimizing efficiency.</p>
298 ]]>
299 </description>
300 </item>
301 <item>
302 <title>
303 <![CDATA[DataMesh: How Uber laid the foundations for the data lake cloud migration]]>
304 </title>
305 <link>
306 <![CDATA[https://www.uber.com/blog/datamesh/]]>
307 </link>
308 <pubDate>
309 <![CDATA[2024-09-10 13:00:00]]>
310 </pubDate>
311 <category>
312 <![CDATA[Data / ML]]>
313 </category>
314 <description>
315 <![CDATA[<p>Learn how Uber is streamlining the Cloud migration of its massive Data Lake by incorporating key Data Mesh principles.</p>
316 ]]>
317 </description>
318 </item>
319 </channel>
320