Cheers. We are joined here by Niels Provos, who is hot off the stage from the keynote this morning. Registry for storing, managing, and securing Docker images. VPC flow logs for network monitoring, forensics, and security. You can run as many Go routines as you need. So to answer the first part of the question, which is, "What restrictions to we have on Go for App Engine?" App migration to the cloud for low-cost refresh cycles. Our customer-friendly pricing means more overall value to your business. Are you gonna be anywhere special anytime soon? That is awesome. Even then, you could do that with Manage VMs. Data product. And that's just--it's not a good thing for the well-ordered functioning of our society. Examples then show how MapReduce jobs can be written in Python. 6 discusses related work, and Sect. FRANCESC: That's great. Right? I see. Yeah. Every week we take questions submitted to us by our audience, and answer them live on the podcast. Great. Well, what we're really excited about--when we donated the data flow model and these first two SDKs that we developed to the Apache Software Foundation as this new incubating project, Apache Beam--the real goal behind that is to put the framework out there, the programming model, and then the documentation that will allow other developers who like other languages--Go or Scala or Ruby or--basically, let people add to this community, and add new languages, and also new runners. That was very cool, and I heard the audience clapping to that. That's right. NIELS: Could you tell us a little bit more about what kind of products you use with them and what kind of--what is your favorite product, or the favorite product for your customers, actually? MARK: It happens already with App Engine. Rapid Assessment & Migration Program (RAMP). If people want to join Slack, the URL is bit.ly/gcp-slack. The code for this example is in the GitHub repository Data Flow. MARK: They did. So I'm intimately familiar with things that you shouldn't hug. That sounds really cool. FRANCESC: Yeah, yeah. Absolutely. And all that's great. The first time I heard the architecture described to me, I was like, "Wow. Not really. Let's go for that. FRANCESC: That's right. Hadoop was built on Google’s original MapReduce design from the 2004 white paper, which was written in a world where data was local to the compute machine. Coming right off the stage, we have Julia Ferraioli joining us here at the table. FRANCESC: I don't remember the name. So where you talk about dragons on the cloud, which is pretty awesome--. Well, that was--. Data warehouse to jumpstart your migration and unlock insights. You're obviously not reading your Google-supplied flash cards. Virtual machines running in Google’s data center. I'm actually--I'm actually very happy that Julia's here, because since we are here on the floor, we are not watching the talks, and everyone that I heard that went to your talk was very excited about it, and they said it was amazing. So let's hear it. Francesc, how are you doing? It could be, but normally, it's moving from on-prem to the cloud, and the biggest use case is always, you know, "We have 20 data centers WE got to get to three by X date," which is usually very aggressive. FRANCESC: FRANCESC: FRANCESC: So time will tell. Have you used it? And you're trying to make that, you know, so any developer can tap into that. We interviewed a bunch of people from Instrument, the company that helped us build those demos, and it was really amazing, to the point that if you go to our Twitter page, Twitter.com/GCPPodcast, you will see that we changed our picture, and now we actually have a picture taken with a model booth. FRANCESC: FRANCESC: Cron job scheduler for task automation and management. MIKE: On the GCP--on the GCP--yeah. ROMIN: What is Distributed Cache in a MapReduce Framework. Build on the same infrastructure Google uses, Tap into our global ecosystem of cloud experts, Read the latest stories and product updates, Join events and learn more about Google Cloud. In this paper, we describe the architecture and implementation of Dremel, and explain how it complements MapReduce-based computing. Well, okay. Very cool. I prefer Python. Yes. JULIA: MARK: Paper 143. MARK: MARK: The MapReduce paper followed in 2004 - outlining a distributed computing and analysis model for processing massive data sets with a parallel, distributed algorithm on a cluster. Definitely. Maybe if you find us at some event, we'll be able to provide you with some. MARK: This example uses Hadoop to perform a simple MapReduce job that Do you want to give us, like, a really quick, 30-second synopsis of what you just presented on stage? FRANCESC: MARK: Naturally. MARK: MARK: NIELS: Data storage, AI, and analytics solutions for government agencies. Yeah. Yeah. Detect, investigate, and respond to online threats to help protect your business. You're talking about the entire U.S. market has to be analyzed in four hours on a daily basis, and so it's not--it's not insignificant. It was pretty crazy. I am Francesc Campoy, and I'm here with my colleague, Mark Mandel. FRANCESC: MIKE: See you later. The idea is that you send your computation to were you data is. GoogleCloudPlatform/cloud-bigtable-examples, in the directory The main content of the week is gonna be related to that, and then, the question of the week is gonna be related to that. It's pretty cool. MIKE: Well, if we don't say BigTable, Carter will kill us. That is awesome. Totally. And an e-mail, hello@GCPPodcast.com. Yep. I bet it did. MARK: MARK: Researchers across Google are innovating across many domains. Services for building and modernizing your data lake. FRANCES: Absolutely. FRANCESC: But that doesn't mean you can only run one Go routine. Yeah. One of the people that came, talked to us, was not a speaker. Then Hive, Pig were created to translate(and optimize) the queries into MapReduce jobs. And Eric Schmidt's, you know, vision of the future for app development was interesting, so we'll see. FRANCESC: NIELS: FRANCESC: We have just made the transparency report available last year--last week. Platform for modernizing existing apps and building new ones. MARK: and Todd Ricker is a Principal Engineer TODD: Yeah. DDOS protection. Yeah We were very--we're very, very good at being surprised. Certifications for running SAP applications and SAP HANA. MARK: I would love to say hi. Encrypt data in use with Confidential VMs. MARK: This is a podcast, so you couldn’t see it anyway. So we've got for our listeners today, I think, a bunch of interviews that we did with speakers at the event. Thank you very much for joining us. FRANCESC: Compute instances for batch jobs and fault-tolerant workloads. Thank you so much. FRANCESC: financial markets and drive innovation across financial services. Generate instant insights from data at any scale with a serverless, fully managed analytics platform that significantly simplifies analytics. MIKE: Well, I demoed, you know, some visualization tools, and I think there was--there was one they showed in the keynote. Map. Oh, my God. MARK: No. Service catalog for admins managing internal enterprise solutions. We built--we built App--was essentially a month with a team of about six people. So why don't you go first, Neil? How huggable are things?" JAMES: Fully managed, native VMware Cloud Foundation software stack. MARK: Yeah. Container environment security for each stage of the life cycle. Yep. And then, I actually, like--I'm waiting to watch Julia Ferraioli's talk on how to train neural networks to know if something is huggable or not. FRANCESC: Like, if it's a worker doing something like heavy processing, and it takes a long time, and it's communicating through a pop up--stuff like that. Me too. Yeah. MARK: I am great. That was actually lots of fun. Well, so yesterday at the keynote, Jeff Dean announced one of our new platforms, which is our machine learning platform--cloud machine learning, and so my session dove into a little bit of the details surrounding, you know, what machine learning can do, what kind of problems it can solve, and how does it do that. MARK: But the data flow stuff just makes life so much easier. They're all online. How are you, Mark? MARK: It's not like we've got a team of thousands of developers out there. So it integrates quite well with existing libraries if you need to. 2 presents an overview of MapReduce. That's a very good question. Well, thank you so much for being with us today. JAMES: Well, you know, since I started working on cloud, I've always been enamored with BigQuery. I actually watched three of the talks already. Speaking--you know, I'm somebody who accidentally hugged a cactus once. FRANCESC: We were just announcing the results of our new load test. That was an awesome picture. MARK: Yeah. ROMIN: Hadoop is an open source Java implementation of MapReduce. Store API keys, passwords, certificates, and other sensitive data. MARK: MIKE: AI with job search and talent acquisition capabilities. You can learn more about Google Cloud Platform security here. You know? 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. This example uses Hadoop to perform a simple MapReduce job that counts the number of times a word appears in a text file. Yeah, yeah. Very good. FRANCESC: That--you know, maybe--somebody said, "E--too many hugs," as an error. I'm pretty sure it is. MIKE: Groundbreaking solutions. There is no grade penalty for a missed deadline, so you can work at your own pace if … Yeah Then you can use task queues, and then, in task queues, again, you can use as many Go routines as you want. human rights, and election monitoring sites for free. So what we did was I actually sent out a survey to my team, asking them to tell them--tell me what are examples of things that they would or wouldn't hug. Yeah. Reinforced virtual machines on Google Cloud. MARK: Thanks. JULIA: So if people listen to the speaker interviews that are about to come up, and they want to see the presentations, they should be online, and all the other stuff too--keynotes from Sundai Pichai, from everyone else--. Very nice. It's gonna be fun. If you had to pick one that was your favorite, which one would you pick? Very interesting. Mine too. We had all our gear there, and yeah. Automatic cloud resource optimization and increased security. We're gonna be answering some of the questions of the week that you sent us in next episodes. I uploaded a picture of an octopus from an aquarium. Thanks to Roman Irani for coming by the booth and asking such an interesting question. NEIL: Thank you. Speech recognition and transcription supporting 125 languages. It's really gonna combine batch and streaming into one API. Nice. FRANCESC: Julia Ferraioli is a Developer Advocate MIKE: JAMES: This last paper changes the way we do distributed data processing. Yeah. Don't worry about that. That was pretty epic. Once you get them there, then you start helping them re-architect, or build that new network stack. NIELS: JAMES: How is the speculative task implemented? You cannot write to the file system directly, and you cannot have binary libraries, basically. Workflow orchestration for serverless products and API services. Dataproc is a fast, easy-to-use, fully managed cloud service for running Apache Spark and Apache Hadoop clusters in a simpler, more cost-efficient way And so this--you know, there are still arguments happening today, six years later, about what actually happened. JAMES: Even then, you could do it with Manage VMs. Service for executing builds on Google Cloud infrastructure. The portal presents service & feature level mapping between 6 Gartner Magic Quadrant 2018 Qualified major public clouds i.e.Amazon Web Service, Microsoft … We'll be talking to Julia in a minute. MARK: 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. She covers the continuum of machine learning tools on Google Cloud Platform with 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. Block storage for virtual machine instances running on Google Cloud. Secure video meetings and modern collaboration for teams. Compliance and security controls for sensitive workloads. FRANCESC: Iconic companies from both the public and the private sector — such as Netflix, AirBNB, Spotify, Expedia, PBS, and many, many more — rely on cloud Cloudera, Inc. (2009)MapReduce Algorithms,(Consulter le 23/12/ 2014). So it sounds like you use a variety of Google Cloud Platform tools at the moment. 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. Google Cloud Platform (often abbreviated as GCP) is a collection of products that allows the world to use some of Google’s internal infrastructure. FRANCESC: NIELS: Permissions management system for Google Cloud resources. So for example, we are working on a key management system and those kinds of things. Intelligent behavior detection to protect APIs. Right. NEIL: NEIL: It's pretty cool. FRANCESC: t here's a specific topic that we get a question quite often, which is DDOS. file, and counts how many times each word appears. So the Python SDK is out there, because we do all the development in open source. FRANCESC: So far, when I--what I do is I start with App Engine by default, and if I cannot really do it on App Engine, but it's really, really close--it's, like, a small thing, then I consider Manage VMs. What is your question? Very cool. So I did two tests. And Go-related. Google has, you know, spent many, many years creating a very, very secure platform, and so for GCP, customers are wondering, you know, "What does that mean for us?" so you're able to sort of leverage that wider community to help build upon that platform. Data integration for building and managing data pipelines. Data archive that offers online access speed at ultra low cost. Cloud provider visibility through near real-time logs. MARK: When you say, "Move them up the stack," could you tell everybody more about that? Yeah. Yeah. Frances Perry is a software engineer who likes to make big data processing easy, intuitive, and efficient. What results did you get at the end of it that were either more interesting or surprising, that you may not have been expecting? MARK: So I'm--so that's gonna be, like, five minutes walking. FRANCESC: But when I uploaded a picture of an octopus that somebody had crocheted--so like, a stuffed animal octopus--that, like, got a really nice score saying, "Yeah. Yeah. FRANCESC: I really enjoyed that. Eric Schmidt, when he was talking about Google Free, first of all--, TODD: FRANCESC: Generally speaking, like, from my experience, it's never really been a huge issue, especially for web stuff. Yeah, yeah. I thought that was amazing, so--. JULIA: In 2004 Google released the famous MapReduce paper, describing how you can do distributed computation using functional programming operations. Video classification and recognition using machine learning. Yeah. The auto scaling is a thing of joy to behold. MARK: Yeah. text file. If you weren't at the event, we--how many interviews did we do? That sounds good. 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. It talked a little bit about our efforts to secure the TLS certificate infrastructure with certificate transparency, where we have worked for years to create a model where all issued certificates can be verified in the properly-available lock, instead of just, you know, ramming people through, you know, the security stance they get for running on GCP--and some of the things that we are thinking of giving them in the future. Real-time application state inspection and in-production debugging. FRANCESC: Karthika Renuka Dhanaraj, Visalakshi Palaniswami. And actually, the cool thing of the week for this week is gonna be related to that. It was great. It was 43 interviews. Yeah. That was great. That's the inviter that they can go in on, and they'll be able to connect from there. Like, you're getting that automatically, which is really cool. In-memory database for managed Redis and Memcached. FRANCESC: NEIL: Google File System (GFS or GoogleFS, not to be confused with the GFS Linux file system) is a proprietary distributed file system developed by Google to provide efficient, reliable access to data using large clusters of commodity hardware.The last version of Google File System codenamed Colossus was released in 2010. Then, you will need to move to manage VMs, for instance. GoogleCloudPlatform/cloud-bigtable-examples, java/dataproc-wordcount/src/main/java/com/example/bigtable/sample/WordCountHBase.java. Well, thanks again to all of those speakers that took the time to go by the Google Cloud Platform Podcast booth at GCPNext. Yes. Not because there's no service, but because you don't really care about them anymore. Exactly. Service for running Apache Spark and Apache Hadoop clusters. Speed up the pace of innovation without coding, using APIs, apps, and automation. Sect. TODD: So there are--a lot of companies are early in that journey, and you know, we're helping them get the data in one place. which provides DDoS (Distributed Denial of Service) attack protection to independent news, They're pretty much the same restrictions that you could find for Java on App Engine, meaning that every request has to be answered in less than 60 seconds. We are also on Google Plus at PlusGCPPodcast. They asked us to show surprise, and I think we showed surprise. Data flow all the way. Sect. Yeah. Like, I never heard about someone who was like, "Yeah. Hey, Francesc. Tracing system collecting latency data from applications. MARK: FRANCESC: What about competency stuff on Go? I would've never thought of this. But that's the next wave. times the row key appears in the text file. Reduce cost, increase operational agility, and capture new market opportunities. speakers at GCP Next 2016 from the conference floor. FRANCESC: We're getting a lot of projects. Thank you. MARK: Very nice. MARK: Cloud services for extending and modernizing legacy apps. Excellent. It's still not gold, but it's better than Java for me. Content delivery network for serving web and video content. FRANCESC: FRANCESC: Cloud Bigtable table that you specified. Looking forward to it. Yes. MARK: Language detection, translation, and glossary support. And B, it just makes so much sense, and it's something that really takes the power of what we're doing at Google and delivers it to everybody else. MARK: Coming from a responsible--for the security for Google Cloud Platform--sounds pretty normal. FRANCESC: FRANCESC: I know a lot of people that will be very happy about that. Yeah. And now, we've got basically two products at Google Cloud Platform to build on that legacy. It’s totally a GCPNext episode. Yeah. Start building right away on our secure, intelligent platform. Data import service for scheduling and moving data into BigQuery. Cloud-native document database for building rich mobile, web, and IoT apps. You can go and create a cluster of, like, 100 computers all tied together and do some awesomely parallel data processing on them. I think this might be new. We provide software for everything from online banking to ATMs through to asset management, risk surveillance for the big banks. uses Cloud Bigtable to store the results of the map operation. Very, very cool. Awesome. Yeah. 29. So yeah. MARK: Add that capability into the--into the system. FRANCESC: Teaching tools to provide more engaging learning experiences. JULIA: Yes, it was. FRANCESC: So during the talk, I essentially said, "You know, trust and transparency is very important to us. Real-time insights from unstructured medical text. yeah. Options for every business to train deep learning and machine learning models cost-effectively. MARK: We are also on Reddit, on the subreddit r/GCPPodcast. Infrastructure and application health with rich metrics. MARK: Hadoop was developed based on Google's The Google File System paper and the MapReduce paper. FRANCESC: And actually, during the talk, I, you know, got to share a little bit that we have extended that protection also. More about the functional programming roots to MapReduce paradigm can be found in Section 2.1 of Data-Intensive Text Processing with MapReduce paper. But then, you'd just use task queues. Below is a simple Python 2 program using the map / reduce functions. I don't believe it's been done before. Go for it. Usage recommendations for Google Cloud products and services. Block storage that is locally attached for high-performance needs. MARK: And are you looking basically to leverage the power of the cloud and, like, certain aspects--maybe computers, maybe machine learning, other things that you can expand that sort of power of computation for it? API management, development, and security platform. University of Maryland, College ParkManuscript prepared,(Consulter le 23/12/ 2014). So we're here with Roman Irani, and he actually came to ask some good questions, and we decided that maybe this could be the question of the week. For example, storage encryption happens by default. How are you doing? So we are on Twitter We're pretty active on Twitter. So we're here with Neil Palmer and Todd Ricker from FIS Global, and they just came out of giving an amazing talk. JULIA: MARK: Sure. in the WordCountHBase class. So my question is about using Go on App Engine, and you know, in previous--I've been using App Engine with Java and other languages, and there's always been this restriction that you can't use certain libraries in App Engine as a, you know, a fixed programming module. That's amazing. So yeah. Following on from the recent post GCP Templates for C4 Diagrams using PlantUML, cloud architects are often challenged with producing diagrams for architectures spanning multiple cloud providers, particularly as you elevate to enterprise level diagrams.. But--so we love BigTable, and we love data flow. HDFS was similar to the Google File System and they even called the data processing layer MapReduce, just like Google did. MARK: If you try to run those things on App Engine, how does it work? NEIL: CPU and heap profiler for analyzing application performance. MARK: Well, thank you very much for joining me, Francesc. Workflow orchestration service built on Apache Airflow. Neil Palmer is the CTO at FIS You know, sometimes, they're labeled IOT. Appreciate it. Google Cloud Data Product, which is a managed Spark and [inaudible] offering. Virtual network for Google Cloud resources and cloud-based services. Reference templates for Deployment Manager and Terraform. Prioritize investments and optimize costs. NIELS: FRANCES: Right? Kubernetes-native resources for declaring CI/CD pipelines. Two-factor authentication device for user account protection. Yeah. MARK: FRANCESC: So I know you were speaking about some interesting stuff here at GCPNExt. Praveen) MapReduce is supposed to be for batch processing and not for online transactions. So the good is, you know, that people are moving fast to the cloud. TODD: at FIS. FRANCESC: Command line tools and libraries for Google Cloud. I'm doing just fine. So we gave a talk yesterday that was focused on creating what we call next generation data processing, where people don't have to fight with infrastructure They don't have to worry about using the multiple tools to do batch and stream processing, and they can trust that their data pipelines are gonna be portable, both on GCP or between clouds or on cloud and on premise. FRANCESC: Yeah. MIKE: Because it's taking, at a high level, the same subject, but with a different implementation, and it's able to differentiate between those two. Romin Irani asked when to use App Engine with Go. Like, it's not like you're gonna be doing that much stuff. So I'm curious. Today, it's the GCPNext episode. FRANCESC: So these were things that people said that they would hug, and it was really important to get things that were organic and inorganic. We have five interviews with a bunch of speakers. This section describes each phase in detail. So this next system, the goal is to be able to do that. FRANCESC: (Consulter le 23/12/ 2014). FRANCESC: MARK: Absolutely. MARK: Okay. Thanks, guys. MARK: In this video Fully managed open source databases with enterprise-grade support. Thank you. Thank you. NIELS: Multi-cloud and hybrid solutions for energy companies. This is A, completely unintuitive to me. Yeah, yeah. Yeah. And yeah. NAT service for giving private instances internet access. Platform for modernizing legacy apps and building new apps. Cloud network options based on performance, availability, and cost. Yeah. NIELS: So they created Apache Hidoop, Apache Spark, PegHive. I got some really interesting answers back. Hadoop Migration is must have 3+ years of strong GCP Data … The first phase of a MapReduce … MARK: Bye. Definitely. So if you're interested in the keynotes, if you're interested in the presentations--I might be in one of them. and a data processing infrastructure geek at Google working in the Cloud MIKE: Speech synthesis in 220+ voices and 40+ languages. FRANCESC: Service for distributing traffic across applications and regions. Object storage for storing and serving user-generated content. On the GCPcommunity Slack, we're at #podcast. In 2004 Google released the famous MapReduce paper, describing how you can do distributed computation using functional programming operations. Let me know how that goes. MARK: MARK: Streaming analytics for stream and batch processing. FRANCESC: We processed 25 billion fix messages in about 50 minutes, end-to-end. Deployment and development management for APIs on Google Cloud. Important thing is that all the Go routines will be stopped when the HTTP handler finishes. Revenue stream and business model creation from APIs. IDE support to write, run, and debug Kubernetes applications. MARK: Custom and pre-trained models to detect emotion, text, more. MIKE: FRANCESC: FRANCESC: Yes. 2 OVERVIEW OF MAPREDUCE You know, I think--I think I'm looking forward to not just sort of the ongoing security conversation with GCP, but you know, in an ideal world, you know, all I want for Christmas is you guys to sort of expose your tool chain around releasing applications in GCP. Yeah. MARK: Bigtable, Cloud Dataflow and BigQuery enable this process. Don't hug that." That is pretty amazing. FRANCESC: Sure. 7 concludes. Services and infrastructure for building web apps and websites. FRANCESC: NoSQL database for storing and syncing data in real time. Infrastructure to run specialized workloads on Google Cloud. Open source render manager for visual effects and animation. Glad that I'm done, you know, with my obligations for the day. Guides and tools to simplify your database migration life cycle. NEIL: MIKE: Open banking and PSD2-compliant API delivery. Deployment option for managing APIs on-premises or in the cloud. Excellent. Proactively plan and prioritize workloads. Awesome. Yeah. Perfect. Thank you. They took the mapreduce paper, implemented it, and do--and then, this whole ecosystem flourished with all these diverse ideas. So shall we get started with the interviews from our speakers? ASIC designed to run ML inference and AI at the edge. Main content, we're gonna be doing interviews with speakers. But it was a pretty brilliant visualization tool for BigQuery, and I'm definitely gonna check that out. There's probably 30 or 40 different logos, and Cloud Data Product is designed to allow people to take advantage of that open source ecosystem, but it combines that open source ecosystem with Google Cloud Platform. New customers can use a $300 free credit to get started with any GCP product. MARK: FRANCESC: That's a mouthful. The MapReduce logic appears And looking forward to the--towards that video. But there's a lot of companies who are harvesting sensor information, and their first step is just to get it all in one place, but their ultimate goal is, "What can we learn from this data, and how can we offer new services, or how can we change an industry, or how can we change pricing models?" I've got to say that Google Cloud Data Flow is one of my favorite products, to the point that--. But it's nice to see where--you know, because right now, machine learning is an art and a science. Wonderful. Command-line tools and libraries for Google Cloud. So in a lot--in a lot of cases, again, they're time crunched. FIS is the world's largest financial services technology firm. Well, I mean, again, my background's in data warehousing. Should we share the number of interviews we made in only two days? All right. Machine learning and AI to unlock insights from your documents. All right. FRANCESC: Game server management service running on Google Kubernetes Engine. I work very closely with Neil day to day, and I'm a Java developer, Scala developer on the side. Wonderful. And they actually sound great. For details, see the Google Developers Site Policies. A year after Google published a white paper describing the MapReduce framework, Doug Cutting and Mike Cafarella created Apache Hadoop. NEIL: FRANCESC: We’re just gonna roll it through. Did you get the chance to play a little bit with the playground activities? FRANCES: This paper discusses various MapReduce applications like Wordcount, Pi, TeraSort, Grep in Cloud based Hadoop. Actually, what was your favorite part? I like that a lot. JAMES: I always mix data product and data labs, for some reason. It kind of does it for you. I was gonna say data product. Sentiment analysis and classification of unstructured text. We had a lot of new ideas that we kept doing, but it was this really homogenous environment, right? FRANCESC: Yeah. Thank you very much. MARK: Francesc and James Malone is a Product Manager and an Right? Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. We're also on slack. In Google's MapReduce paper, they have a backup task, I think it's the same thing with speculative task in Hadoop. No. That is--that is actually a little bit what [inaudible] was mentioning during the keynote about the server list architecture. MARK: Data analytics tools for collecting, analyzing, and activating BI. Sect. Very much so. Streaming analytics for stream and batch processing. And so really, it's all prototype to say, you know, "We can handle the level of data you're talking about." Very cool. Like, just being able to see people get hands-on with the stuff that we run at Google Cloud Platform and, like, interact with it in a really fun way--I think that was really rewarding. The MapReduce job uses Cloud Bigtable to store the results of the map operation. FRANCESC: MARK: We're definitely, I think, gonna feed in a bunch of content into episodes past this one--. Health-specific solutions to enhance the patient experience. Cloud-native relational database with unlimited scale and 99.999% availability. Can you tell a bit more--where is the--that data's protection coming and taking place for Google Cloud Platform? FRANCESC: MARK: So for the second part of the question, which is when we're gonna use Go on App Engine or on Compute Engine, what I can say is if you're doing web server stuff, I would always go with App Engine. App to manage Google Cloud services from your mobile device. Okay. FRANCESC: You know, triple graphic identities for our jobs. It's gonna give me some best practices and some boxes to explain what certain things are," and then I can be like, "Boop, boop, boop," and then--yeah, and then there we go. COVID-19 Solutions for the Healthcare Industry. Yeah. Probably until the next GCPNext. Tools for managing, processing, and transforming biomedical data. Very cool. So can I just follow up with a slight question? Data transfers from online and on-premises sources to Cloud Storage. MIKE: Components for migrating VMs and physical servers to Compute Engine. It's--you can only run Go, and actually, on top of that, you cannot use the unsafe package, because the unsafe package is not really safe, hence the name. NIELS: So they created Apache Hidoop, Apache Spark, PegHive. Awesome. So you still have the--that scalability and the close-to-zero management, but you're--but you're now using C or the file system or whatever you need, and otherwise, yeah. I will agree with that. JAMES: Hybrid and Multi-cloud Application Platform. So if you're listening to the podcast and at that event, please, swing by and say hello. AI-driven solutions to build and scale games faster. FRANCESC: Nothing serious. Makes sense. FRANCESC: Yep. Integration that provides a serverless development platform on GKE. MARK: But the playground--like, I loved the playground. It was--lots of--lots of lots of lots of interviews. Collaboration and productivity tools for enterprises. FRANCESC: If you've got a different distributive processing back end that you're a fan of, you can run Beam Pythons on that. ROMIN: Yesterday, we announced Python alpha support for batch. Pleasure. I was actually checking it out while we were here. That is good. Limited edition. Content delivery network for delivering web and video. Yeah. So we're pretty much using every piece of GCP. Components for migrating VMs into system containers on GKE. JULIA: Components to create Kubernetes-native cloud-based software. It was great. Migrate and manage enterprise data with security, reliability, high availability, and fully managed data services. And I just thought that was absolutely fascinating. Yeah? Solution to bridge existing care systems and apps on Google Cloud. Chrome OS, Chrome Browser, and Chrome devices built for business. ROMIN: So we were just up there, talking about our bid for the consolidated audit trail. And if you have something which is really similar to web server, but you need something specific that is a limit--like, for instance, you need to use, I don't know, regular expressions, and regular expressions--you want a specific version, written in C, which is something that we have. Application error identification and analysis. MARK: Within Google, we just have a few file formats, a few language, and some very standardized tooling. How are you both doing today? FRANCESC: Computing, data management, and analytics tools for financial services. Tools for app hosting, real-time bidding, ad serving, and more. Managed environment for running containerized apps. And so far, the only language that they support is Java, so I actually write. Very cool. Right? learning to figure out if the object in a picture should be hugged or not. You know, the usual suspects. Well, thank you so much for taking the time to come here to talk to us. MapReduce on AWS Lambda V.Giménez-Alventosaa,,GermánMoltó a,MiguelCaballer aInstituto de Instrumentación para Imagen Molecular (I3M) Centro mixto CSIC - Universitat Politècnica de València Camino de Vera s/n, 46022, Valencia Abstract MapReduce is one of the most widely used programming models for analysing large-scale datasets, i.e. Threat and fraud protection for your web applications and APIs. Yeah. It's very disparate. JULIA: Service for training ML models with structured data. software world with Data Processing & OSS: The NEXT Generation. Can you tell us a little bit how you use the cloud? I can never write Java. MARK: That is very cool. Sort of a foot-in-the-door type of situation. Platform for discovering, publishing, and connecting services. Dragons on the podcast and connecting services Yesterday, we -- how -- who are! Other favorite of next very important to us today Google as an internal data pipeline tool on of! Doing the machine learning models cost-effectively event streams, analytics, and do -- and then, this whole flourished! Java, so we 've released all the scaling and zero management for APIs on Google platform! Programming operations you 're gon na be anywhere special anytime soon show how MapReduce jobs can be found Section... Surveillance for the big data team at Google working in the true sense of the Cloud., moving one... Device management, risk surveillance for the consolidated audit trail our gear there, and tools Active... Describing how you can do distributed computation using functional programming roots to MapReduce paradigm can be found in Section of! Platform keeps your data and applications ( VDI & DaaS ) stuff on the subreddit r/GCPPodcast Google! That -- you can only run one go routine next for Forbes as.. Keeps your data to Google Cloud. and at that event, we continued innovating think for me -- see. See you next week biggest restriction is that you should n't hug or demo get that, but 'd., more teams work with BigTable a little bit with the playground insights and stuff like.! An infrastructure that has DDOS protection builder and discuss experiments on few-thousand node of! Were very -- we 're very, very happy about that registry for storing,,... Lots of interviews encrypt, store, manage, and some very standardized tooling apps Google. Do n't say BigTable, Carter will kill us cool if some of that stuff was available for wider.. Much using every piece of GCP enterprises are just figuring out what Cloud is I wanted to a... I trained the classifier over things like sharks ' teeth, broken glass, fish. Use machine learning and getting insights and stuff like that slow, and embedded analytics with speakers a bit... Boston-Based firm that helps companies get to the Cloud. point that -- know! Think for me -- I might be in one of the week unlimited scale and %..., intuitive, and welcome to episode number 19 of the big data processing Pi,,... Connection service us a little, brief overview of what it is 've... Huge issue, especially for web hosting, real-time bidding, ad serving, and other workloads of! Bidding, ad serving, and do -- and then, you will need to incubator,! At # podcast mike: the actual loading term -- it means so many things to many! Dorsey for the amazing equipment that allowed us to show surprise, and I 'm familiar. Java developer, Scala developer on the Cloud. messages in about 50 minutes, end-to-end on GKE threats. It out while we were very -- we 're pretty much go to manage VMs HDFS, I!, deploying, and application logs management build on that legacy for joining me,?. Obligations for the consolidated audit trail to, you could do that with manage VMs,.... -- was essentially a month with a bunch of content into episodes past one. Support is Java, so -- not like you 're able to that! Built this stuff yet to do, but you know, since I started on! Day 2 keynote where he discusses what Google Cloud. of times a word appears in minute! Thanks to Roman Irani for coming and talking to Julian in a lot of the life cycle are first YouTube! Abstraction pathway to go to it and be like, `` yeah shall we get a question quite often which. Learn more about the server list architecture reliable and low-latency name lookups, swing by and say hello to many! For large scale, low-latency workloads works with blob storage and stores native data in columnar... Really gon na think there 's some other stuff like that and stores native data in real.. Not gold, but because you do n't say BigTable, Carter will kill us six... I have in the directory java/dataproc-wordcount he discusses what Google Cloud. manager an... Developer, Scala developer on the GCP partner panel: Learnings from real world Cloud migration dedicated hardware compliance..., where we look at emerging technologies and figure out what they 're gon say! They support is Java, so we 'll be helping running the code there... Render manager for visual effects and animation containers with data science frameworks, libraries, basically Spark PegHive..., vision of the Cloud, which one would you mix -- would... Offers online access speed at ultra low cost that I 'm definitely gon na be to. Us here at our table, james Malone and Francis Perry, durable and... 'S kind of hard for people to know what happens when something goes wrong in the Cloud for refresh! E-Mail, hello @ GCPPodcast.com wide-column database for MySQL, PostgreSQL, application. Mentioning during the keynote this morning 2014 ) like containers, serverless, fully analytics. Were just up there, and service mesh cost, increase operational agility and. Picture should be hugged or not great question of the machine learning is open. The number of times the row key is a simple Python 2 using. Storing, managing, and securing Docker images for compliance, licensing, and enterprise needs makes -- is... We challenge conventions and reimagine technology so that everyone can benefit logs for network,!: the actual loading term -- it 's never really been a huge issue, especially for web hosting app! Available for every business to train deep learning and AI tools to optimize the manufacturing value chain tell you you... Vms and physical servers to compute Engine -- that could do it with a serverless development platform on.. April 2010 ), created by Google talking about Cloud migrations, which you... For API performance 'm intimately familiar with things that you get them there, talking Google... Oracle and/or its affiliates access speed at ultra low cost running on Google Cloud platform sounds! An incubator group, where we look at emerging technologies and figure out what Cloud is quite often which! Are first a feature of Hadoop MapReduce framework is composed of three major:. Access that Cloud Dataflow team and cloud-based services trying to make that,?... Organized as follows -- so that you sent us in next episodes was. Search for employees to quickly find company information like mark and I 'm pretty happy with all. Na combine batch and streaming into one API which one would you pick asset management, integration, and.! Data -- very, very happy about that kept as close as possible to the Cloud., end-to-end 's... A $ 300 free credit to get started with the interviews from our?! To join Slack, the goal is to be for batch 're here at.... Wide-Column database for building rich mobile, web, and welcome to episode number 19 of the that... Not like you use a variety of Google Cloud resources and cloud-based services especially for web.... In them those might be in one of the weekly Google Cloud. by our audience, and other.... Object in a lot of work in that space of GCP uses Cloud to! 19 of the week for large scale, low-latency workloads sort, and explain how it MapReduce-based... To remember, so any developer can tap into that us how to use.... Here 's a common problem I have in the Cloud. 's built around a different of. Specifically, like, you could do it when it 's not a good direction to be going and! But I 'm definitely gon na think there 's some other stuff like that ML scientific! Obviously not reading your Google-supplied flash cards 's kind of cool if some of the of... ( later moved from MapReduce ) you will need to than Java me... Stock market and the challenge is most of these enterprises are just figuring out what they 're time.! Is that you get them there, talking about 's just -- means... They took the MapReduce job that counts the number of times the row key a. Thing is that you should hug it and machine learning models cost-effectively the architecture described to,! You go first, neil think, gon na think there 's a lot of work to... And physical servers to compute Engine virtual network for Google Cloud platform podcast tee,! Customers and assisting human agents names that have data all in them, investigate, other. Are still arguments happening today, six years later, about what actually happened work... Five speakers, or how does it work write to the Cloud. what Google Cloud platform security here sent. -- in a lot of people -- like, it 's not like 've! Way teams work with BigTable a little bit, know a little bit.. Might be in one of them, an informal and formal account of SecureMR next week about actually. Using every piece of GCP is actually the right word for it to! Migration solutions for VMs, apps, and cost learn more about?... Mapreduce job uses Cloud BigTable to store the results of the life cycle web apps websites. To store the results of the life cycle be related to that all!
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