Apache Hadoop, every mention
28 scenes (2019) · ← back to Apache Hadoop
tap a year for its mentions
every year 2019 anyone Sonal Chokshi 7Roman Stanek 5Prat Moghe 5Christopher Nguyen 4Steven Sinofsky 3Matei Zaharia 3Gaurav Dhillon 3Chandra Krintz 3Peter Levine 2Ion Stoica 2
Verbatim, from the transcripts: the passages where Apache Hadoop comes up
Everyone is an Analyst: Opportunities in Operational Analytics
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.
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
- ▶ 4:13 Ion Stoica We started with Hadoop.
- ▶ 6:58 Ion Stoica From just supporting big data, cluster computing frameworks like Hadoop, it went to support
- ▶ 11:19 Sonal Chokshi Like the precursor to Hadoop and then Spark. 2 times in the scene
a16z Podcast | The Storage Renaissance
- ▶ 13:28 Mike Matchett When you had Hadoop and MapReduce, they could partition certain categories of problems and run them in parallel, but not really a lot of machine learning algorithms.
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
- ▶ 7:47 Roman Stanek You know, with all the investment in Hadoop and this and Hadoop that, you know, most companies are still data bankrupt. 4 times in the scene
- ▶ 10:28 Prat Moghe Because the moment you take that approach, it then becomes, like you were saying, it is a Hadoop store, you know, can I ask any question?
- ▶ 14:36 Roman Stanek Kind of that information, and it's, it is in Hadoop, and it is in, in Data Warehouse, and so on, but there is this kind of, you know, again, it's the last mile, and I'm not saying that we are kind of able to solve it, you know, generically…
- ▶ 17:56 Gaurav Dhillon So what we have now is a new population of user who is using the data from a Hadoop or something, and that person's a data scientist.
- ▶ 19:48 Steven Sinofsky But another one is that the, the, the gap between the CMO and, and the IT organization is often there's data missing, and that there's some source, like it could be geographic data, it could be like, wow, this report doesn't even list all… 3 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.
- ▶ 28:57 Gaurav Dhillon If your data's on premise, you should probably put your Hadoop or other kinds of analytics on premise.
a16z Podcast | What Software Developers (and Therefore Every Company) Need
- ▶ 29:26 Ben Uretsky Projects out there like, you know, Hadoop is pretty complicated, Cassandra, some of the big data stuff.
a16z Podcast | How Big Companies Can Get the Most From Silicon Valley
- ▶ 4:06 Elizabeth Weil And are you using Hadoop right now?
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, 3 times in the scene
a16z Podcast | A Conversation With the Inventor of Spark
- ▶ 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. 3 times in the scene
- ▶ 13:59 Matei Zaharia So, uh, in particular, uh, you know, one of the things we saw is many of the projects that were built on top of Hadoop, such as Hive, which is a SQL processing, uh, at scale and, uh, Pig and Mahout for machine learning are starting to run… 3 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. 2 times in the scene
- ▶ 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.
a16z Podcast | Making Sense of Big Data, Machine Learning, and Deep Learning
- ▶ 7:39 Christopher Nguyen But it wasn't until the advent of things like the Hadoop project, right?
- ▶ 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… 5 times in the scene
- ▶ 11:46 Christopher Nguyen And MapReduce, I don't mean in terms of the algorithm, but I mean the actual implementation with the Hadoop project, the Hadoop app MapReduce. 3 times in the scene
a16z Podcast | Why the Datacenter Needs an Operating System
- ▶ 3:33 Benjamin Heinemann Kafka and HDFS and Hadoop and Cassandra.