Uber

“Those businesses themselves are not super tech intensive, right? They're just leveraging very basic building blocks”

“We have 25 people out in San Francisco just closed our Series B recently, and we have about a 175 customers around the world, including Twitter, Square, Match.com, OpenTable, Uber, Airbnb, Kickstarter and others.”

“Since we open sourced it, you know, roughly five years ago, Kafka is used in thousands of companies worldwide, from Uber, and LinkedIn, and Netflix, all the way to traditional enterprises like eBay, and PayPal, and Cisco, and Goldman Sachs.”

“Most of Uber's data consumers are actually these operations people.”

“We went from an ETL model, where we scraped from, like, the original source, transformed the data and loaded to Vertica, to, like, just an EL model, where we just, like, just copy the data as soon as possible into, like, Hadoop, and all the transformation can …”

“What we really created was, like, using HDFS, like, a data lake, where we basically copied the whole data sets from, like whatever we get from, like, analytical logs or, like, all our business data sources, too, into HDFS.”

“We practically did not have an infrastructure is what the honest reality is.”

“It works well, like, if you're, like, a ten-person company, or, like, even to a fifty-person company, when you're, like, scaling to, like, thousands of people, like, you actually need a proper negotiation in between.”

“Kafka was, in general, like, a nice way where people used to pipe the results of, like, their Spark jobs. But often cases, what they do is, like, they hit Kafka hard and bring Kafka down because they're trying to, like, actually send data from, like, hundred e…”

“We have something called cost accounting chargebacks is what we call it. So we actually try to, we have a lineage model where we try to figure out who's storing the data. And, ah, we actually kind of can go get a dollar amount of what we are storing.”

“There's a system called Streamific, which powers all of the data ingestion aggregation at this point.”

“Uber used to run this incentive program in China, and, ah, people are trying to game it, so they used to always have, like, similar trips simulated From their, ah, various different devices. So what we did was actually, like, try to find an algorithm where we …”

“If you use Uber and you say, like, I, you know, please pick me up at the, you know, Regency Cinema in Tribeca and take me to the Starbucks on 27th street, rather than typing addresses, That's all powered by Foursquare's global data set and they moved off Googl…”

“We power a lot of the location inside Bing. We power a lot of Apple Maps around the world. If you search in Uber or Tencent, Pinterest, and Snapchat, Samsung, Twitter, Garmin, these are among the 100,000 companies that now are using this technology set.”

“We look at some of our competitors, specifically Uber, there's a paper published again, either late last year or earlier this year, showing how many miles they ride empty on average for every single passenger mile that they do, and it's about between . Six and…”

“If you look at any good platform, Like Uber or Airbnb, it's not that they made life convenient, but they also increased the supply of things. Like with Airbnb, the supply of hotels or supply of basically places you can find significantly increases, or with Ube…”

“Most of the tooling is kind of home rolled. Best practices haven't emerged yet. We're seeing a lot of, especially large companies, roll their entire own stacks. So Facebook has FB Learner, Google has TFX. Uber has Michelangelo. Airbnb has Big Head.”

“I'd argue Uber is an ML company because they use it for demand management and pricing and everything else.”

“Uber had a very compelling culture under Travis with a very bad issue.”

“What they were doing is they were taking those algorithms from the seventies that do not work, but they were applying orders of magnitude, more data to it. So a lot of data on modern hardware, and they were getting superhuman results.”

“They were evaluating a variety of technologies for their logging infrastructure for their logging platform, which is being used by hundreds of developers every day for a variety of analytical workloads. And the current technology presented a bit of a cost. Con…”

“We've seen people build these workflows at different settings from, for instance, like hacks on projects at startups building, for instance, like track GPT, like, plugin over, like, your Slack or your Notion all the way to kind of, like, bigger companies, for …”

“We are taking this very powerful analytical database that's used by companies like Spotify and Netflix and Disney and Uber and eBay and Cloudflare, and we're building a managed service in the cloud that we're calling ClickHouse Cloud.”