MapReduce, every mention
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tap a year for its mentions
every year anyone Matt Turck 9Mike Olson 8Tasso Argyros 7Stefan Groschupf 4John Rauser 4Neha Narkhede 3Chris Wiggins 3Paul Dix 2Jordan Tigani 2Todd Papaioannou 1
Verbatim, from the transcripts: the passages where MapReduce comes up
Dataiku's Secret to Scaling AI in Global Enterprises | Florian Douetteau, CEO, Dataiku
- ▶ 6:05 Florian Douetteau You had, uh, Google pushing new technologies, you had, like, those, uh, those days of MapReduce, those days of deep learning starting to work,
The Death of Big Data and Why It’s Time To Think Small | Jordan Tigani, CEO, MotherDuck
- ▶ 3:28 Jordan Tigani You know, after Google came out with, you know, MapReduce and, and GFS and Bigtable, kind of everybody's... 2 times in the scene
A Conversation with Chris Wiggins - Author of "How Data Happened"
- ▶ 20:10 Chris Wiggins Um, it was, so when I showed up at the New York Times in 2013, if you wanted to get your hands on data, you needed to write your own MapReduce jobs in Hive and hit buckets of unstructured JSON sitting in S three.
Not Just Another Cloud Database | SurrealDB Co-Founders Jaime & Tobie Morgan Hitchcock
- ▶ 8:27 Jaime & Tobie Morgan Hitchcock We wanted to have real-time data that was constantly changing, but at the same time, without having to run the MapReduce or large-scale analytics processes and workloads on that data, we wanted to pull out that data in real-time for…
Data Visibility & Control | BigID Co-Founder & CEO Dimitri Sirota
- ▶ 8:57 Dimitri Sirota Now, they may differ, so for instance, for Hadoop, we could do, like, MapReduce, uh, we could do, um, direct, uh, connectivity to, um, uh, uh, Hive or Spark we could use, but they all use native protocols to scan the underlying system.
Fireside Chat: Ali Ghodsi (Founder & CEO, Databricks) with Matt Turck (Partner, FirstMark)
- ▶ 2:50 Ali Ghodsi Every iteration of the data has to run a MapReduce job.
Dynamic Range Sharding with Spanner // Daniel Chia, Google Spanner (FirstMark's Data Driven NYC)
- ▶ 13:53 Daniel Chia And so when you spin up a MapReduce job with 50,000 workers and all try and write to the same little data range that previously had no data, that's not going to work very well because we haven't had time to split it yet.
Fireside Chat: Mike Tuchen, CEO of Talend (TLND) (FirstMark's Data Driven NYC)
- ▶ 15:38 Mike Tuchen So we built a, initially in our, in our initial technology, we built a, um, SQL, or excuse me, we did build a SQL one, but we started out with a Java compiler, um, and then we, um, did a SQL one, and then when Hadoop came along, um, we did…
3 Heretical Ideas on the Future of Data // Ajay Kulkarni, TimescaleDB (FirstMark's Data Driven NYC)
- ▶ 2:50 Ajay Kulkarni The relational databases they had in the past just couldn't scale for these workloads, so the new companies started developing the first big data non-relational systems, and this includes Google, who published the MapReduce paper and the…
Fireside Chat with Bob Muglia, CEO at Snowflake (FirstMark's Data Driven)
- ▶ 17:46 Bob Muglia And then the, the, the, the typical approaches that people have traditionally used with Hadoop, variations of MapReduce, um, are now being seen, I think, as, as, now that there are alternatives, such as Snowflake that are available, that…
Surveillance Platform for Banks // Mayur Thakur, Goldman Sachs (FirstMark's Data Driven)
- ▶ 16:53 Mayur Thakur We use MapReduce.
A Kafka-Powered Real-Time Streaming Platform // Neha Narkhede, Confluent [FirstMark's Data Driven]
- ▶ 23:52 Neha Narkhede Uh, a lot of these other stream processing systems, I didn't get a, uh, chance to get into the details. 3 times in the scene
Why Marketing is All About Data // Nitay Joffe, ActionIQ [FirstMark's Data Driven]
- ▶ 16:43 Nitay Joffe No SQL, no MapReduce, none of that kind of stuff.
A Fireside Chat with MapR CTO M.C. Srivas (Data Driven NYC / FirstMark)
- ▶ 20:58 M.C. Srivas We introduced JSON to Hadoop and Spark and in MapReduce and in Hive and everywhere.
The Acceleration of Innovation in Big Data w/ Stefan Groschupf, Datameer
- ▶ 1:16 Stefan Groschupf That's why we looked into the Google papers and implemented MapReduce and the HDFS, et cetera, et cetera.
- ▶ 4:38 Stefan Groschupf and then we built a MapReduce thing, and then, um, Peter Thiel, not sure, who knows Peter Thiel? 2 times in the scene
- ▶ 12:07 Stefan Groschupf Replacing MapReduce with TES or with Spark is really straightforward, um, especially if you use BI on top of Hadoop, um, because it basically automatically does it for you, but obviously you can't just rip out hardware and replace it,…
Oren Falkowitz, Area 1 Security // The Future of Cybersecurity (Hosted by FirstMark Capital)
- ▶ 4:19 Oren Falkowitz Maybe, you know, eight or nine years since Google published the Bigtable paper and the MapReduce paper and some of those early big data papers which have spawned, you know, this community and all the great technological revolution, now…
Ion Stoica, Databricks // Creating Apache Spark // Data Driven NYC (FirstMark Capital)
- ▶ 6:25 Matt Turck Uh, can you, uh, maybe help us understand, um, so this HDFS, which is the storage, uh, and MapReduce, which is, uh, the processing, 4 times in the scene
- ▶ 7:24 Matt Turck Yes, so perhaps more specifically, can you go through the, the, the key advantages of Spark over MapReduce? 4 times in the scene
Paul Dix, InfluxDB // Open-Source Time Series Database // Data Driven NYC (FirstMark Capital)
Chris Wiggins, NY Times // Data Science at The New York Times (Hosted by FirstMark Capital)
- ▶ 18:24 Chris Wiggins Elastic MapReduce, proper MapReduce in Java. 2 times in the scene
Mike Olson, Cloudera // The Cloudera Story (Hosted by FirstMark Capital)
- ▶ 4:55 Mike Olson Google published the Google File System and MapReduce papers, and like the entire database industry, I thought that this thing was a joke, man.
- ▶ 6:52 Matt Turck This Spark, this data flow, and, you know, maybe MapReduce is not what people need, and all of this is evolving.
- ▶ 9:33 Mike Olson These days when people talk about Hadoop, what they mean is HDFS and MapReduce, yeah, yarn for resource management. 5 times in the scene
- ▶ 14:39 Mike Olson The Hadoop that was then available, which was HDFS and MapReduce, was powerful, transformative.
- ▶ 18:18 Mike Olson The inventor of Hadoop, Google Ventures, the inventor of MapReduce and GFS took a stake.
Michael Rubenstein and Catherine Williams, App Nexus // Data Driven #31 // Nov 2014
- ▶ 10:18 Catherine Williams We started using, you know, Hadoop, MapReduce, Hive.
Mike Abbott, KPCB // Data Driven #30 // Oct 2014 (Hosted by FirstMark Capital)
- ▶ 12:56 Mike Abbott A third of the people that I recruited from Google was how could we, you know, let non-engineers run MapReduce jobs, ask more questions of the data, because if we let more non-engineers ask questions, there is a higher probability that we…
John Rauser, Pinterest // Big Data at Pinterest // Data Driven NYC (Hosted by FirstMark Capital)
- ▶ 1:16 John Rauser I'd walk them through an example of MapReduce at a level of simplicity that this audience would probably find comical. 4 times in the scene
Tobi Knaup, Mesosphere // Data Driven #29 // Sep 2014 (Hosted by FirstMark Capital)
- ▶ 3:33 Tobi Knaup And, um, so MapReduce, for example, was, uh, started as a, um, you know, Google wrote a paper about it.
Tasso Argyros, Aster Data // Data Driven NYC 24 // February 2014 (Hosted by FirstMark Capital)
- ▶ 2:47 Tasso Argyros A declarative language of SQL, right, and the procedural language of MapReduce.
- ▶ 14:39 Tasso Argyros In 2007, we had the brilliant idea of coming out with a SQL MapReduce Azure positioning, and of course, nobody knew what MapReduce back then, right? 6 times in the scene
Scott Sorensen, CTO of Ancestry.com // Data Driven 19 // October 2013 (Hosted by FirstMark Capital)
- ▶ 7:38 Scott Sorensen And MapReduce and R were introduced into our tool set at that time.
Mike Dauber, Battery Ventures // Data Driven NYC 19 // October 2013 (interviewed by Matt Turck)
- ▶ 2:52 Mike Dauber And then came 2003, 2004, and Google came out with their, you know, their big table MapReduce papers.
Panel: Continuuity, Sailthru and Visual Revenue // Data Driven NYC #7 // June 2012
- ▶ 15:17 Todd Papaioannou And two, MapReduce allows you to write a simple program that can scan all of that data.
Panel: Metamarkets, Kaggle and Quid // Data Driven NYC #4 // Mar 2012
- ▶ 49:39 unnamed speaker That it will scale, that it will map reduce, that whatever these words are.
Kirill Sheynkman, RTP Ventures // Data Driven #3 // Feb 2012 (Interviewed by Matt Turck)
- ▶ 4:56 Kirill Sheynkman It's, it's, it's pretty, ah, hairy stuff, but it's, it's a system that has APIs in Scala and in Java, and they're working on JavaScript and, and Python, where you can take a function and basically the MapReduce becomes three lines of code.