Apache Spark, every mention
35 scenes (2019) · ← back to Apache Spark
tap a year for its mentions
every year 2019 anyone Sonal Chokshi 36Matei Zaharia 11Peter Levine 8Ali Ghodsi 7Prat Moghe 5Ion Stoica 4Michael Jordan 3Chandra Krintz 3Vijay Pande 2Steven Sinofsky 2
Verbatim, from the transcripts: the passages where Apache Spark comes up
How to Build an Open Source Business
- ▶ 13:57 Peter Levine Databricks and Spark is an example.
a16z Podcast | Containing the Monolith -- From Microservices to DevOps
- ▶ 1:51 Florian Leibert But of course, also data scientists will install notebooks, they'll install Spark, they'll install Hadoop, and then use it. 2 times in the scene
a16z Podcast | AI, from 'Toy' Problems to Practical Application
- ▶ 16:18 Joe Spisak There are a lot of big data SIs, and they've been using Hadoop and Spark, and they've been dabbling in machine learning and advanced analytics.
a16z Podcast | A New Lab Rises
- ▶ 2:31 Peter Levine One, of course, is Spark, which is the basis of Databricks. 4 times in the scene
- ▶ 11:19 Sonal Chokshi Like the precursor to Hadoop and then Spark.
- ▶ 22:16 Ion Stoica There are projects which hope to become a strong artifact to be used across in, in, in industry like Spark or Mesos or, uh, Tachyon.
- ▶ 26:16 Ion Stoica It's about, it's actually, it's also related to Spark.
a16z Podcast | The Changing Culture of Open Source
- ▶ 26:18 Sonal Chokshi Does this then leave the domain of, like, the really big visionary projects that come out of academia, like the way Spark came out of Amplab, Apache Spark, where you have an entire new set of companies being built?
a16z Podcast | The Storage Renaissance
- ▶ 0:28 Sonal Chokshi Which came out of the UC Berkeley Amp Lab, the birthplace of other industry-defining technologies such as Spark and Mesos.
- ▶ 13:51 Mike Matchett So Spark and in-memory approaches to machine learning really accelerate the opportunity to create and apply machine learning algorithms to just about every facet of human existence, not to overstate the case, but, uh, there really is a…
a16z Podcast | The Product Edge in Machine Learning Startups
- ▶ 12:04 AJ Shankar Spark, I think, is the go-toe one for now. 2 times in the scene
a16z Podcast | Software Programs the World
- ▶ 5:26 Marc Andreessen So for example, we've seen the rise of, in that category, we've seen the rise of Hadoop, and now the rise of Spark for distributed data processing.
a16z Podcast | Selling to Developers & Open Source Business Models
- ▶ 17:30 Peter Levine You know, one of the companies that were invested in, Arimo, formerly Adetow, builds machine learning and big predictive big data applications that sit on top of Spark and Hadoop installations. 2 times in the scene
a16z Podcast | Making the Most of the Data That Matters
- ▶ 17:46 Steven Sinofsky The University of California, Berkeley, well, has a, a whole variety of some of the leading technologies that like Spark has come out of there.
- ▶ 21:05 Roman Stanek Uh, extremely basic, because they actually look and feel like sheet of paper, like two-dimensional sheet of paper, and, uh, you know, so that's, that's the problem with analytics, that on one hand, we have, you know, very complex systems… 2 times in the scene
- ▶ 25:07 Gaurav Dhillon But people should be thinking about being able to use the new price performance of Hadoop, Spark, to obliterate their traditional data warehousing appliance.
a16z Podcast | AMPLab, the Power of Open Source, and the Future of Systems Software
- ▶ 0:05 Michael Copeland The place where Apache Spark was born, UC Berkeley's Amplab has not just created a major open source software platform, it's spun out more than its share of groundbreaking companies.
- ▶ 17:45 Michael Franklin Uh, he's the guy who did Spark.
- ▶ 19:41 Peter Levine Now that we're able to collect data, and now we're able to do real-time big data, a la Spark,
- ▶ 26:52 Haoyuan 'H.Y.' Li Um, so at Lamp Lab, we have other two, uh, projects like Mesos, Apache Mesos, and Apache Spark. 2 times in the scene
a16z Podcast | Big Data Goes Really Big
- ▶ 4:58 Prat Moghe The old stack, largely relational stack, Hadoop is a decimal point, Sparks just coming along.
- ▶ 17:56 Prat Moghe They'll usually get into AWS, they'll start playing around with Redshift, they'll start playing around with Hadoop, Spark, 4 times in the scene
a16z Podcast | A Conversation With the Inventor of Spark
- ▶ 0:03 Sonal Chokshi I'm Sonal, and I'm here today with Matei Zaharia, the CTO and co-founder of Databricks, which is the primary company driving and developing Spark. 6 times in the scene
- ▶ 0:57 unnamed speaker Before Spark, the most widely used system was probably MapReduce, which was, uh, uh, invented at Google and popularized through the, the open source Hadoop project. 5 times in the scene
- ▶ 5:25 Sonal Chokshi So one of the most interesting announcements that came out of the Spark Summit that I think a lot of people saw was the announcement that IBM is backing Spark.
- ▶ 9:08 Sonal Chokshi I would love to hear your thoughts about that and also how that applies to Spark. 6 times in the scene
- ▶ 10:28 Matei Zaharia So I should say, you know, from the beginning that, you know, we didn't, we certainly didn't, uh, imagine that Spark would be this widely used, uh, when we started. 4 times in the scene
- ▶ 13:37 Sonal Chokshi I've actually seen you share a chart that shows a really rich ecosystem growing around Spark and why that matters. 8 times in the scene
- ▶ 16:41 Matei Zaharia So it's actually one of the applications that I first tried to support in Spark was, uh, you know, the recommendation algorithm he was working on.
- ▶ 17:21 Matei Zaharia So, uh, you know, so as we saw, uh, Spark do very well in, in the, in the open source domain, we, we wanted to start a company around it to really harden it and to bring it to a much wider class of, uh, commercial users. 6 times in the scene
a16z Podcast | The Cool Stuff Only Happens at Scale
- ▶ 0:44 Chris Dixon Um, and so we have things like, I think of Hadoop and, and its successor, we, we think as a successor of Spark, as frameworks for doing distributed computing for a specific application, which is data processing.
- ▶ 6:26 Vijay Pande I really am looking forward to seeing where people will go, and I think Hadoop and Spark are a good example, but I think we need much more. 2 times in the scene
a16z Podcast | Making Sense of Big Data, Machine Learning, and Deep Learning
- ▶ 7:55 Sonal Chokshi I'm actually glad you brought up Hadoop, Christopher, because one of the things that I see a lot in reading about the big data space is a lot of myths and misconceptions around what Hadoop is, what Spark is, because now we talk a lot about… 4 times in the scene
- ▶ 13:50 Sonal Chokshi What does Spark do differently? 8 times in the scene
a16z Podcast | Why the Datacenter Needs an Operating System
- ▶ 0:23 Steven Sinofsky There are things like Kafka and Spark and MapReduce and Cassandra, and it's super, super