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Nishith Agarwal

As seen in: Uber Engineering Blog
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Articles

Building a Large-scale Transactional Data Lake at Uber Using Apache Hudi

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By Nishith Agarwal, Thi Hiep Nguyen, Jeff Wolski, Mrina Natarajan
| Uber Engineering Blog
From ensuring accurate ETAs to predicting optimal traffic routes, providing safe, seamless transportation and delivery experiences on the Uber platform requires reliable, performant large-scale data storage and analysis. In 2016, Uber developed Apache Hudi, an incremental processing framework, to power business critical data pipelines at low latency and high efficiency.
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[VOTE] Graduate Apache Hudi (Incubating) as a TLP

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By Nishith Agarwal
| mail-archives.apache.org
+1 (non-binding) On 2020/05/16 16:07:08, Y Ethan Guo <ethan.guoyihua@gmail.com> wrote: > +1 (non-binding) > > On Sat, May 16, 2020 at 8:31 AM John D. Ament <johndament@apache.org> wrote: > > > +1 binding good work incubating and good luck! > > > > On Fri, May 15, 2020 at 9:50 PM Vinoth Chandar <vinoth@apache.org> wrote: > > > > > Hello all, > > > > > > I would like to call for a VOTE for graduating Apache Hudi (Incubating), > > as > > > a top level project.
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Consistent Data Partitioning through Global Indexing for Large Apache Hadoop Tables at Uber

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By Rene Schmidt, Jakob Thomsen, Nishith Agarwal, Kaushik Devarajaiah
| UBER
Data serves little purpose if we cannot find it. Looking up individual records in the 100-plus petabytes of data accumulated at Uber lets us perform updates and gather useful insights to help improve our services, such as delivering more accurate ETAs to riders and showing eaters their favorite food options.
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