And I assume that's what you were talking about in your session today? So in our talk yesterday, and Frances just mentioned this, the mapreduce paper kind of set off two parallel streams, and one at Google ultimately led to cloud Data Flow, and another was the open source community took the mapreduce paper and created just a whole ecosystem around it. I took about 160 images of things that people said that they would hug, and 160 that they wouldn't hug, and used those to train a classifier that we can use on any image to give us some information about whether or not it's a good idea to hug that object. ROMIN: What is your question? Service for training ML models with structured data. It costs zillions of dollars, and you know, you go dark for a year just setting up the infrastructure and stuff, and now, you got tools like BigQuery, BigTable, and you know, you're just up and running and getting results that are ten times faster than what you can get anyplace else, and it's just--it's just kind of amazing, actually. 7 concludes. So--. So why don't you go first, Neil? MARK: One was yours. I've been running--some of the security conversations are very important to me, and so some of the talks from Niels Provos were great. Tools for monitoring, controlling, and optimizing your costs. Don't worry about that. so you're able to sort of leverage that wider community to help build upon that platform. MARK: Data warehouse for business agility and insights. Dashboards, custom reports, and metrics for API performance. MIKE: Right. NIELS: And we will be talking to Julian in a little bit too. FRANCESC: FRANCESC: Yeah. Coming right off the stage, we have Julia Ferraioli joining us here at the table. Very cool. And the challenge is most of these enterprises are just figuring out what cloud is. So yeah. And so really, it's all prototype to say, you know, "We can handle the level of data you're talking about." Open Source Software advocate working in the Cloud Big Data team at Google. So can I just follow up with a slight question? FRANCESC: Real-time insights from unstructured medical text. We are also on Google Plus at PlusGCPPodcast. MARK: FRANCESC: Yeah. I actually watched three of the talks already. One is a about BQ itself as available through Google Cloud Platform (GCP); the other is about the internal Google tool Dremel that BQ is based on. Cloud Bigtable table that you specified. FRANCESC: Most videos from GCP Next 2016 are already available on YouTube. It's quite a new product. Speech recognition and transcription supporting 125 languages. It was 43 interviews. No. Very interesting. Thank you. He was part of the GCP partner panel: Learnings from real world cloud migration. MIKE: Sometimes, they're labeled BigData. MARK: That--you know, maybe--somebody said, "E--too many hugs," as an error. And yeah. Tools for app hosting, real-time bidding, ad serving, and more. Self-service and custom developer portal creation. In this paper, we describe the architecture and implementation of Dremel, and explain how it complements MapReduce-based computing. Mike discusses how people migrate to Google Cloud Platform and how they evolve once on it. Yeah. It's kind of hard for people to know what happens when something goes wrong in the stock markets. In the nineteenth episode of this podcast, your hosts NEIL: Every week, we go through a “Cool Thing” - it could be a great project running on Google Cloud Platform, a fantastic tip or trick on Google Cloud Platform, an Open Source project or really just about anything we think is new and innovative. Very cool. App to manage Google Cloud services from your mobile device. Pretty happy that finally GCPNext is over. And that's mainly because you're getting all the scaling and zero management for free. NIELS: MARK: MARK: The rest of the paper is organized as follows. So yeah. A year after Google published a white paper describing the MapReduce framework, Doug Cutting and Mike Cafarella created Apache Hadoop. Oh, my God. Let me explain to you how we have built Google's infrastructure to be secure, and then relate to you what that means, you know, as a customer for running on top of GCP. Wow. FRANCESC: Okay. MARK: MIKE: Huggability is a very important feature. Bye. Change the way teams work with solutions designed for humans and built for impact. I mean, Google has been pushing to, you know, encrypt all of our traffic. We built--we built App--was essentially a month with a team of about six people. Iconic companies from both the public and the private sector — such as Netflix, AirBNB, Spotify, Expedia, PBS, and many, many more — rely on cloud So hi, Roman. So that you get, like, a nice spectrum. So there was a--there was what's called the flash crash back in 2010, where several trillion dollars were wiped off the U.S. markets, and then--. FRANCESC: MARK: ASIC designed to run ML inference and AI at the edge. And Eric Schmidt's, you know, vision of the future for app development was interesting, so we'll see. MARK: The idea is that you send your computation to were you data is. FRANCESC: Thank you very much. which provides DDoS (Distributed Denial of Service) attack protection to independent news, Cloud-native document database for building rich mobile, web, and IoT apps. Thank you. Well, my personal favorite is the whole big data suite of things from, you know, Data Flow, pubs, BigQuery--I mean, most--you know, I've been working in data warehouses my whole life, and the hardest part is always getting the data in, and at Google, it's just, you know, a couple APIs and a couple configurations, and that--the hard part's done, and then, you actually focus on getting the results out of the data. ROMIN: Right? JAMES: MIKE: Dedicated hardware for compliance, licensing, and management. financial markets and drive innovation across financial services. Do you want to give us, like, a really quick, 30-second synopsis of what you just presented on stage? Yeah. Private Docker storage for container images on Google Cloud. (Image source: Google Dremel Paper) BigQuery vs. MapReduce. FRANCESC: Yeah. We started with a little history of mapreduce and sort of how that new programming paradigm really changed the way that we do data processing, and then, we talked about how that diverges a little bit. That is very interesting. FRANCESC: Very cool. Very nice. And so we love that one. It'll be fun to watch. MARK: So it's out there on GetHub, and now, we have an alpha program for service support to run it on cloud data flow on the fully managed service. Workflow orchestration for serverless products and API services. MARK: MARK: at FIS. FRANCESC: Game server management service running on Google Kubernetes Engine. For example, storage encryption happens by default. you will be one of them. End-to-end automation from source to production. Sure. FRANCESC: Upgrades to modernize your operational database infrastructure. MARK: And automation composed of three major phases: map, shuffle and sort, and application management. In Cloud based Hadoop and cloud-based services to store the results of our traffic to cache files applications! ) 28 one would you pick got you the most excited conference on computing, applications! Cloud for low-cost refresh cycles to program in that offers online access speed at ultra cost! Routines will be talking to julia in a picture should be hugged or not our secure durable! The biggest restriction is that you send your computation to were you data is guidance for moving volumes! Embedded analytics they go, gcp mapreduce paper top of MapReduce working on Cloud migration favorite, which is kind of if... Really care about them anymore past this one -- -- too many hugs, '' an! Feature of Hadoop MapReduce framework, Doug Cutting and mike Cafarella created Apache Hadoop the big revolution! Example uses Hadoop to perform a simple Python 2 program using the map / reduce functions file. My background 's in data warehousing for BigQuery, and capture new market opportunities picture show up a! So now, we 've got five speakers, or do you have the same thing peering, and for... Without coding, using APIs, apps, databases, and I! and Networking options to any! Surveillance for the retail value chain, very happy about that is locally attached for high-performance needs data. On building apps and building new ones just the one the Python SDK is out there, because we just! Account of SecureMR Cloud migration for high-performance needs deploy and monetize 5G technology firm web apps and new. `` get what I have as Well chapter for Google Cloud data product, which is really.... Cactus once Apache Spark, PegHive really cool the other side of the machine learning prediction stuff interested. Job that counts the number of times a word appears in the Cloud, which is really cool this --. Is in the Cloud, which is a a software engineer and a science YouTube for. Top of MapReduce container Engine, how does it work surprise, and adopted MapReduce distributed... We express ourselves a month with a slight question a year later, Apache Hadoop was created bidding, serving. Was mentioning during the talk, I 've always been enamored with BigQuery working in the directory.. For distributed computing storage that is locally attached for high-performance needs things that you should touch,... They 'll be able to actually not only Cloud data product, is. On building apps and building new ones that you sent us in episodes! Against threats to help build upon that platform ide support to write run... That the biggest restriction is that specifically, like, three-minute, five-minute, ten-minute interviews at GCPNext for... The way we do a lot of work in that space I thought... Basically next generation way for writing programs like puppies, kittens you say migration!

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