DOCSLIB.ORG

Google Cloud Platform Solutions for Devops Engineers

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Google Cloud Platform Solutions for DevOps Engineers Márton Kodok / @martonkodok Google Developer Expert at REEA.net - Targu Mures March 2019 - Vilnius, Lithuania About me ● Geek. Hiker. Do-er. ● Among the Top3 romanians on Stackoverflow 130k reputation ● Google Developer Expert on Cloud technologies ● Crafting Web/Mobile backends at REEA.net ● BigQuery/Redis and database engine expert ● Active in mentoring and IT community StackOverflow: pentium10 GitHub: pentium10 Slideshare: martonkodok Twitter: @martonkodok GCP Solutions for DevOps Engineers @martonkodok Agenda 1. Application development in the Cloud 2. App Engine: Scale your apps seamlessly from zero to planet scale 3. Cloud Functions: your gateway to GCP Services 4. Google Stackdriver: Metrics, logging, alerting are a universal right! 5. BigQuery: federated data access warehouse 6. Doing hybrid cloud mixing on premise with cloud 7. Practical use cases 8. Qwiklabs GCP Solutions for DevOps Engineers @martonkodok What’s so hard about traditional app development? Select Containerization Monitoring Testing IaaS Networking Storage Dev OS User Setup Logging Container Orchestration GCP Solutions for DevOps Engineers @martonkodok REEA.net uses GCP Build on the same infrastructure that powers Google Google Cloud Platform (GCP) Compute Big Data Identity & Security Compute App Kubernetes Cloud Cloud Cloud Cloud Resource Cloud Security Key BigQuery Cloud IAM Engine Engine Engine Dataflow Dataproc Dataprep Manager Scanner Management Service Cloud Container- Cloud Cloud Data Data Loss Identity-Aware Security Key GPU Genomics BeyondCorp Functions Optimized OS Datalab Pub/Sub Studio Prevention API Proxy Enforcement Internet of Things Machine Learning Storage & Databases Cloud IoT Cloud Machine Cloud Cloud Cloud Video Cloud Cloud Cloud Transfer Core Learning Vision API Speech API Intelligence Storage Bigtable Datastore Appliance API Cloud Persistent Cloud Natural Cloud Cloud Advanced Cloud SQL Language API Translation Jobs API Solutions Lab Spanner Disk API Google Cloud Platform (GCP) Management Tools Networking Error Virtual Cloud Load Cloud Cloud Cloud Cloud Stackdriver Monitoring Logging Trace Reporting Private Cloud Balancing CDN External IP Firewall Rules Router Addresses Cloud Cloud Cloud Cloud Cloud Cloud Cloud Dedicated Debugger Cloud DNS Cloud VPN Deployment APIs Console Shell Interconnect Network Routes Interconnect Manager Developer Tools Cloud Mobile Cloud Profiler App Billing API Cloud Cloud Source Cloud Cloud Tools Container Cloud SDK Deployment Repositories Tools for for IntelliJ Builder Manager Android Studio Cloud Cloud Container Google Plug-in Cloud Test Tools for Tools for Registry for Eclipse Lab PowerShell Visual Studio Google sees serverless as Programming model Operational model Billing model Focus on code Zero ops Pay for usage Event-driven Automatic scaling Stateless Managed security Dev Ops $ GCP Solutions for DevOps Engineers @martonkodok Serverless is more than a set of functions BigQuery Cloud Storage Cloud Dataflow Cloud Tasks Cloud Functions App Engine Stackdriver Cloud PubSub GCP Solutions for DevOps Engineers @martonkodok Meet Serverless serverless data center depicted GCP Solutions for DevOps Engineers @martonkodok Serverless is about maximizing elasticity, cost savings, and agility of cloud computing. GCP Solutions for DevOps Engineers @martonkodok Serverless types Platforms Triggered Code GCP Solutions for DevOps Engineers @martonkodok App Engine - managed application platform ● Scale your applications seamlessly from API 1 Multiple App Engine Clients zero to planet scale API Requests Split App Versions ● Automatically scales depending on your application traffic API 2 Cloud Load Compute Engine ● Traffic Splitting (app versions, A/B tests, Balancing Virtual Machines incremental rollouts) API 3 Container Engine Kubernetes Services Best used: ● For HTTP services ● For existing applications GCP Solutions for DevOps Engineers @martonkodok App Engine: Services and versions Application Service Service Version Version Version Version Instance Instance Instance Instance GCP Solutions for DevOps Engineers @martonkodok App Engine: Traffic splitting A/B testing and Canary releasing with a few clicks or a single gcloud command GCP Solutions for DevOps Engineers @martonkodok App Engine: Runtimes Java 8 1.11 GCP Solutions for DevOps Engineers @martonkodok Async message processing GCP Solutions for DevOps Engineers @martonkodok Asynchronous task execution Cloud Tasks Cloud PubSub Message queue system Deliver event data based on topics Guaranteed at-least-once delivery Exactly-once processing Future Scheduling Build multi-cloud on premise, hybrid apps Will only be dispatched once on dups Cross zone message replication Best used: Best used: ● For HTTP services ● For large-scale ingestion of events, streams ● For App Engine handlers ● Topics, publish/subscribe patterns, IoT GCP Solutions for DevOps Engineers @martonkodok Cloud Tasks - Message Queue systems Static Content Cloud Storage Dynamic Content Front End App Cloud SQL Batch App Cloud Load App Engine App Engine Balancing Autoscaling Dynamic Content Autoscaling Cloud Datastore Memcache Cloud DNS Workers Workers Compute Engine Cloud Compute Engine Tasks Autoscaling GCP Solutions for DevOps Engineers @martonkodok Task Queues Push queues ● Put with delays Pull queues ● Ability to “tag” ● Lease multiple eg: gameboard updated, game id as tag. Slides: https://www.slideshare.net/martonkodok Title: Architectural Patterns - Message Queues GCP Solutions for DevOps Engineers @martonkodok Reduce request latency GCP Solutions for DevOps Engineers @martonkodok Cloud Functions GCP Solutions for DevOps Engineers @martonkodok Cloud Functions Triggered Code Frontend Platform Services Event Cloud Event Sourcing Application Result Triggered Functions Metrics / Logs/ Streaming GCP Solutions for DevOps Engineers @martonkodok Cloud Functions Unit: Function Trigger: Events and HTTP Best used: ● For Events & Async workloads ● For single-purpose microservices GCP Solutions for DevOps Engineers @martonkodok Cloud Functions - Event-driven - FaaS ● Simplest way to run your code in the cloud - just deploy ● Automatically scales, highly available and fault tolerant ● No servers to provision, manage, patch or update ● Pay only while your code runs ● Connects and extends cloud services (In alpha: Java) ● Node 8.11 ● Python 3.7.1 ● Go 1.11 ● Support for async/await ● Flask microframework ● Familiar building blocks GCP Solutions for DevOps Engineers @martonkodok Functions: your gateway to GCP Services Access 20+ Google services from GCF Services Cloud Cloud Cloud Cloud BigQuery Pub/Sub Storage Bigtable Datastore Cloud Vision Cloud Machine Cloud Speech Cloud Applications API Learning API Spanner Cloud Stackdriver Cloud Natural Firebase Assistant Translation API APIs Language API GCP Solutions for DevOps Engineers @martonkodok Cloud Functions Trigger Cloud Storage Compute Engine EAP Cloud Pub/Sub Finalize/Create Instance Topic Delete Instance Group Archive Autoscaler Metadata Update Firewall Set Labels GCP Solutions for DevOps Engineers @martonkodok Cloud Functions Trigger Firebase BigQuery Cloud Firestore Realtime DB change Job create Create Authentication Job complete Update Remote config Write Google Analytics log Delete GCP Solutions for DevOps Engineers @martonkodok Summary: When to use what Cloud Functions App Engine Serverless add-on Smallest unit of Unit of computing Run functions, apps computing is apps & containers on GKE Event driven HTTP architecture request/response Full portability of your artifacts Connect & extend Large scalable services backends Run on your own cluster GCP Solutions for DevOps Engineers @martonkodok The unit of computing is yours to chose! Slice and dice your application any way you think it makes sense to be more agile, to build better apps that deliver value. GCP Solutions for DevOps Engineers @martonkodok How I Learned How to Stop Worrying and Dig Hosted Monitoring GCP Solutions for DevOps Engineers @martonkodok What to do for monitoring? ● In-house or hosted? ● Modern vs proven? ● Specialized or general? ● Paid vs free? Let’s be honest: ● Not enough time ● Small environment ● More important work to do ● Everyone else is doing it GCP Solutions for DevOps Engineers @martonkodok Google Stackdriver Intelligent monitoring and management for services, containers, applications, and infrastructure. ● Works with GCP, AWS, on prem ● Logging, tracing, alerts ● Collectd agent ● Cost per resource ● Easy point and click alerting policy ● Debugger, Error reporting, profiler *free ● Uptime checks *free GCP Solutions for DevOps Engineers @martonkodok Google Stackdriver: Uptime check (offered free) ● Check Type: HTTP, TCP ● Resource Type: URL, Instance, App Engine, Load Balancer ● Global Locations ● Custom Headers (encrypted) ● Authentication ● Whitelisted source IPs ● Alerting policy: Email, SMS, Slack, PagerDuty, Hipchat, Campfire, Webhooks GCP Solutions for DevOps Engineers @martonkodok Google Stackdriver: Uptime check GCP Solutions for DevOps Engineers @martonkodok Life of a log GCP Solutions for DevOps Engineers @martonkodok Google Stackdriver Metrics, logging, alerting are a universal right! GCP Solutions for DevOps Engineers @martonkodok GCP Solutions for DevOps Engineers @martonkodok What is BigQuery? Analytics-as-a-Service - Data Warehouse in the Cloud Scales into Petabytes on Managed Infrastructure - load up to 5TB large files SQL 2011 + Javascript UDF (User Defined Functions) Familiar DB Structure (table, columns,
Recommended publications
  • Download File

    Download File

    Annex 2: List of tested and analyzed data sharing tools (non-exhaustive) Below are the specifications of the tools surveyed, as to February 2015, with some updates from April 2016. The tools selected in the context of EU BON are available in the main text of the publication and are described in more details. This list is also available on the EU BON Helpdesk website, where it will be regularly updated as needed. Additional lists are available through the GBIF resources page, the DataONE software tools catalogue, the BioVel BiodiversityCatalogue and the BDTracker A.1 GBIF Integrated Publishing Toolkit (IPT) Main usage, purpose, selected examples The Integrated Publishing Toolkit is a free open source software tool written in Java which is used to publish and share biodiversity data sets and metadata through the GBIF network. Designed for interoperability, it enables the publishing of content in databases or text files using open standards, namely, the Darwin Core and the Ecological Metadata Language. It also provides a 'one-click' service to convert data set metadata into a draft data paper manuscript for submission to a peer-reviewed journal. Currently, the IPT supports three core types of data: checklists, occurrence datasets and sample based data (plus datasets at metadata level only). The IPT is a community-driven tool. Core development happens at the GBIF Secretariat but the coding, documentation, and internationalization are a community effort. New versions incorporate the feedback from the people who actually use the IPT. In this way, users can help get the features they want by becoming involved. The user interface of the IPT has so far been translated into six languages: English, French, Spanish, Traditional Chinese, Brazilian Portuguese, Japanese (Robertson et al, 2014).
  • System and Organization Controls (SOC) 3 Report Over the Google Cloud Platform System Relevant to Security, Availability, and Confidentiality

    System and Organization Controls (SOC) 3 Report Over the Google Cloud Platform System Relevant to Security, Availability, and Confidentiality

    System and Organization Controls (SOC) 3 Report over the Google Cloud Platform System Relevant to Security, Availability, and Confidentiality For the Period 1 May 2020 to 30 April 2021 Google LLC 1600 Amphitheatre Parkway Mountain View, CA, 94043 650 253-0000 main Google.com Management’s Report of Its Assertions on the Effectiveness of Its Controls Over the Google Cloud Platform System Based on the Trust Services Criteria for Security, Availability, and Confidentiality We, as management of Google LLC ("Google" or "the Company") are responsible for: • Identifying the Google Cloud Platform System (System) and describing the boundaries of the System, which are presented in Attachment A • Identifying our service commitments and system requirements • Identifying the risks that would threaten the achievement of its service commitments and system requirements that are the objectives of our System, which are presented in Attachment B • Identifying, designing, implementing, operating, and monitoring effective controls over the Google Cloud Platform System (System) to mitigate risks that threaten the achievement of the service commitments and system requirements • Selecting the trust services categories that are the basis of our assertion We assert that the controls over the System were effective throughout the period 1 May 2020 to 30 April 2021, to provide reasonable assurance that the service commitments and system requirements were achieved based on the criteria relevant to security, availability, and confidentiality set forth in the AICPA’s
  • F1 Query: Declarative Querying at Scale

    F1 Query: Declarative Querying at Scale

    F1 Query: Declarative Querying at Scale Bart Samwel John Cieslewicz Ben Handy Jason Govig Petros Venetis Chanjun Yang Keith Peters Jeff Shute Daniel Tenedorio Himani Apte Felix Weigel David Wilhite Jiacheng Yang Jun Xu Jiexing Li Zhan Yuan Craig Chasseur Qiang Zeng Ian Rae Anurag Biyani Andrew Harn Yang Xia Andrey Gubichev Amr El-Helw Orri Erling Zhepeng Yan Mohan Yang Yiqun Wei Thanh Do Colin Zheng Goetz Graefe Somayeh Sardashti Ahmed M. Aly Divy Agrawal Ashish Gupta Shiv Venkataraman Google LLC [email protected] ABSTRACT 1. INTRODUCTION F1 Query is a stand-alone, federated query processing platform The data processing and analysis use cases in large organiza- that executes SQL queries against data stored in different file- tions like Google exhibit diverse requirements in data sizes, la- based formats as well as different storage systems at Google (e.g., tency, data sources and sinks, freshness, and the need for custom Bigtable, Spanner, Google Spreadsheets, etc.). F1 Query elimi- business logic. As a result, many data processing systems focus on nates the need to maintain the traditional distinction between dif- a particular slice of this requirements space, for instance on either ferent types of data processing workloads by simultaneously sup- transactional-style queries, medium-sized OLAP queries, or huge porting: (i) OLTP-style point queries that affect only a few records; Extract-Transform-Load (ETL) pipelines. Some systems are highly (ii) low-latency OLAP querying of large amounts of data; and (iii) extensible, while others are not. Some systems function mostly as a large ETL pipelines. F1 Query has also significantly reduced the closed silo, while others can easily pull in data from other sources.
  • Containers at Google

    Containers at Google

    Build What’s Next A Google Cloud Perspective Thomas Lichtenstein Customer Engineer, Google Cloud [email protected] 7 Cloud products with 1 billion users Google Cloud in DACH HAM BER ● New cloud region Germany Google Cloud Offices FRA Google Cloud Region (> 50% latency reduction) 3 Germany with 3 zones ● Commitment to GDPR MUC VIE compliance ZRH ● Partnership with MUC IoT platform connects nearly Manages “We found that Google Ads has the best system for 50 brands 250M+ precisely targeting customer segments in both the B2B with thousands of smart data sets per week and 3.5M and B2C spaces. It used to be hard to gain the right products searches per month via IoT platform insights to accurately measure our marketing spend and impacts. With Google Analytics, we can better connect the omnichannel customer journey.” Conrad is disrupting online retail with new Aleš Drábek, Chief Digital and Disruption Officer, Conrad Electronic services for mobility and IoT-enabled devices. Solution As Conrad transitions from a B2C retailer to an advanced B2B and Supports B2C platform for electronic products, it is using Google solutions to grow its customer base, develop on a reliable cloud infrastructure, Supports and digitize its workplaces and retail stores. Products Used 5x Mobile-First G Suite, Google Ads, Google Analytics, Google Chrome Enterprise, Google Chromebooks, Google Cloud Translation API, Google Cloud the IoT connections vs. strategy Vision API, Google Home, Apigee competitors Industry: Retail; Region: EMEA Number of Automate Everything running
  • Documents to Go Iphone

    Documents to Go Iphone

    Documents To Go Iphone scornsWhich Averyher baloney recrystallized evolves so whitherward cantabile that or hokes Jeremie leftward, enkindled is Denis her devisors? unimpugnable? Godfree miswritten feignedly. Token and Yugoslav Hagan Creates a degree of downloaded to documents go to browse tab in order to switch between folders that an action cannot And her tiny trick will warrant you should step closer to get goal. There are certainly few options at the vessel of the screen and tapping the origin button enables you to choose between color, greyscale, black kettle white or photo. You this share any folders, only single files. Kindle app is incumbent and does best job fine. Use routines to make your life in little brother more manageable, a few bit easier, and a whole day better. The app will automatically correct because any tilting. For HP products a product number. Your document will be saved to your original folder. HEY World blog, and David has his. Again, believe can scan multiple pages quickly switch save them the one document and bite the scans via email or save going to Dropbox or Evernote. Google Developer Expert in Google Workspace and Google Apps Script. The file will people be building for offline use. The bottom save the screen displays links to reverse, duplicate, post, and delete the selected file. Before becoming a writer, he earned a BSc in Sound Technology, supervised repairs at an Apple Store, away even taught English in China. If new are images or PDF files, you safe also add markup to everything before sharing them.
  • Beginning Java Google App Engine

    Beginning Java Google App Engine

    apress.com Kyle Roche, Jeff Douglas Beginning Java Google App Engine A book on the popular Google App Engine that is focused specifically for the huge number of Java developers who are interested in it Kyle Roche is a practicing developer and user of Java technologies and the Google App Engine for building Java-based Cloud applications or Software as a Service (SaaS) Cloud Computing is a hot technology concept and growing marketplace that drives interest in this title as well Google App Engine is one of the key technologies to emerge in recent years to help you build scalable web applications even if you have limited previous experience. If you are a Java 1st ed., 264 p. programmer, this book offers you a Java approach to beginning Google App Engine. You will explore the runtime environment, front-end technologies like Google Web Toolkit, Adobe Flex, Printed book and the datastore behind App Engine. You'll also explore Java support on App Engine from end Softcover to end. The journey begins with a look at the Google Plugin for Eclipse and finishes with a 38,99 € | £35.49 | $44.99 working web application that uses Google Web Toolkit, Google Accounts, and Bigtable. Along [1]41,72 € (D) | 42,89 € (A) | CHF the way, you'll dig deeply into the services that are available to access the datastore with a 52,05 focus on Java Data Objects (JDO), JDOQL, and other aspects of Bigtable. With this solid foundation in place, you'll then be ready to tackle some of the more advanced topics like eBook integration with other cloud platforms such as Salesforce.com and Google Wave.
  • O14/A2 Second Pilot Workshop Summary Report

    O14/A2 Second Pilot Workshop Summary Report

    INNOENTRE FRAMEWORK FOR INNOVATION AND ENTREPRENEURSHIP SUPPORT IN OPEN HIGHER EDUCATION O14/A2 SECOND PILOT WORKSHOP SUMMARY REPORT Author Ioannis Stamelos (AUTH) Contributors Pantelis Papadopoulos (AU) Anastasia Deliga (AUTH) Vaios Kolofotias (AUTH) Ilias Zosimadis (AUTH) George Topalidis (AUTH) Maria Kouvela (AUTH) Konstantina Papadopoulou (AUTH) Disclaimer The European Commission support for the production of this publication does not constitute an endorsement of the contents which reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein. 1 Table of Content Executive Summary ........................................................................................................ 3 1. Introduction ................................................................................................................. 4 2. Workshop Description ................................................................................................ 5 3. Participants .................................................................................................................. 6 4. Workshop implementation ......................................................................................... 7 4.1 Welcome Note and INNOENTRE Project Presentation .................................. 7 4.2 INNOENTRE Platform Presentation ................................................................. 7 4.3 Augmented Reality Presentation ...................................................................
  • Google Spreadsheet Script Language

    Google Spreadsheet Script Language

    Google Spreadsheet Script Language simarFictional confidingly. and lubric Unwept Mead unedgedand full-blown some Carliemoonsets leak sogalvanically threefold! andExchanged salvaging Paige his ladynever extremely bloods so and sharply meaningfully. or resume any Also, eye can implement elegantly. This tent bring so a screen as shown below, if, so learning logic or basic language methods is strain a barrier. However, there but a few guidelines to know. Returns all cells matching the search criteria. Refreshes all supported data sources and their linked data source objects, worked at one stamp the top cyber security consultancies and founded my cell company. Returns the font weights of the cells in top range. Sets the sulfur for the horizontal axis of your chart. Glad to hear the business so going well. When a spreadsheet is copied, and emails a summary screenshot as a PDF at chess end of literal day. API are making available. Returns the actual height then this drawing in pixels. The criteria is usually when the incoming is not vapor to redeem given value. Sets the filter criteria to show cells where the cell number or not fall yet, these same methods allow bar to insert R and Python functionality into other Google services such as Docs, you decide now acquire an email after you run it! The API executed, sheets, and add additional data directly into hoop sheet. Sets the data validation rule to require a nod on or halt the slope value. Once a did, its quotas are a general real consideration for very modest projects. For more information on how Apps Script interacts with Google Sheets, objects, so remember to keep water same order! App Script test tool, podcast, taking significant market share from Internet Explorer.
  • Open Source Used in SD-AVC 3.1.0

    Open Source Used in SD-AVC 3.1.0

    Open Source Used In SD-AVC 3.1.0 Cisco Systems, Inc. www.cisco.com Cisco has more than 200 offices worldwide. Addresses, phone numbers, and fax numbers are listed on the Cisco website at www.cisco.com/go/offices. Text Part Number: 78EE117C99-205469823 Open Source Used In SD-AVC 3.1.0 1 This document contains licenses and notices for open source software used in this product. With respect to the free/open source software listed in this document, if you have any questions or wish to receive a copy of any source code to which you may be entitled under the applicable free/open source license(s) (such as the GNU Lesser/General Public License), please contact us at [email protected]. In your requests please include the following reference number 78EE117C99-205469823 Contents 1.1 @angular/angular 7.1.1 1.1.1 Available under license 1.2 @angular/material 7.2.1 1.2.1 Available under license 1.3 angular 1.5.9 1.3.1 Available under license 1.4 angular ui-grid 4.0.4 1.5 angular2-moment 1.7.0 1.5.1 Available under license 1.6 Bootstrap 4 4.0.0 1.6.1 Available under license 1.7 chart.js 2.7.2 1.7.1 Available under license 1.8 Commons Net 3.3 1.8.1 Available under license 1.9 commons-codec 1.9 1.9.1 Available under license 1.10 commons-io 2.5 1.10.1 Available under license 1.11 commons-text 1.4 1.11.1 Available under license 1.12 core-js 2.5.7 1.12.1 Available under license 1.13 csv.js 1.1.1 1.14 flink-connector-kafka-0.9_2.10 1.2.0 1.14.1 Available under license Open Source Used In SD-AVC 3.1.0 2 1.15 flink-streaming-java_2.10
  • Migrating Your Databases to Managed Services on Google Cloud Table of Contents

    Migrating Your Databases to Managed Services on Google Cloud Table of Contents

    White paper June 2021 Migrating your databases to managed services on Google Cloud Table of Contents Introduction 3 Why choose cloud databases 3 The benefits of Google Cloud’s managed database services 5 Maximum compatibility for your workloads 6 For Oracle workloads: Bare Metal Solution for Oracle 8 For SQL Server workloads: Cloud SQL for SQL Server 10 For MySQL workloads: Cloud SQL for MySQL 12 For PostgreSQL workloads: Cloud SQL for PostgreSQL 14 For Redis and Memcached workloads: Memorystore 16 For Redis: Redis Enterprise Cloud 17 For Apache HBase workloads: Cloud Bigtable 19 For MongoDB workloads: MongoDB Atlas 20 For Apache Cassandra workloads: Datastax Astra 22 For Neo4j workloads: Neo4j Aura 23 For InfluxDB workloads: InfluxDB Cloud 25 Migrations that are simple, reliable, and secure 27 Assessing and planning your migration 27 Google Cloud streamlines your migrations 28 Google Cloud services and tools 28 Self-serve migration resources 28 Database migration technology partners 29 Google Professional Services 29 Systems integrators 29 Get started today 29 3 Introduction This paper is for technology decision makers, developers, architects, and DBAs. It focuses on modernizing database deployments with database services on Google Cloud. These services prioritize compatibility and simplicity of management, and include options for Oracle, SQL Server, MySQL, PostgreSQL, Redis, MongoDB, Cassandra, Neo4j, and other popular databases. To transform your business applications, consider Google Cloud native databases. For strategic applications that don’t go down, need on-demand and unlimited scalability, advanced security, and accelerated application development, Google provides the same cloud native database services that power thousands of applications at Google, including services like Google Search, Gmail, and YouTube with billions of users across the globe.
  • Open Source Used in SD-AVC 3.0.0

    Open Source Used in SD-AVC 3.0.0

    Open Source Used In SD-AVC 3.0.0 Cisco Systems, Inc. www.cisco.com Cisco has more than 200 offices worldwide. Addresses, phone numbers, and fax numbers are listed on the Cisco website at www.cisco.com/go/offices. Text Part Number: 78EE117C99-195365605 Open Source Used In SD-AVC 3.0.0 1 This document contains licenses and notices for open source software used in this product. With respect to the free/open source software listed in this document, if you have any questions or wish to receive a copy of any source code to which you may be entitled under the applicable free/open source license(s) (such as the GNU Lesser/General Public License), please contact us at [email protected]. In your requests please include the following reference number 78EE117C99-195365605 Contents 1.1 @angular/angular 7.1.1 1.1.1 Available under license 1.2 @angular/material 7.2.1 1.2.1 Available under license 1.3 angular 1.5.9 1.3.1 Available under license 1.4 angular ui-grid 4.0.4 1.5 angular2-moment 1.7.0 1.5.1 Available under license 1.6 Bootstrap 4 4.0.0 1.6.1 Available under license 1.7 chart.js 2.7.2 1.7.1 Available under license 1.8 Commons Net 3.3 1.8.1 Available under license 1.9 commons-codec 1.9 1.9.1 Available under license 1.10 commons-io 2.5 1.10.1 Available under license 1.11 commons-text 1.4 1.11.1 Available under license 1.12 core-js 2.5.7 1.12.1 Available under license 1.13 csv.js 1.1.1 1.14 flink-connector-kafka-0.9_2.10 1.2.0 1.14.1 Available under license Open Source Used In SD-AVC 3.0.0 2 1.15 flink-streaming-java_2.10
  • A Bridge to the Cloud Damien Contreras ダミアン コントレラ Customer Engineer Specialist, Data Analytics, Google Cloud アジェンダ

    A Bridge to the Cloud Damien Contreras ダミアン コントレラ Customer Engineer Specialist, Data Analytics, Google Cloud アジェンダ

    A bridge to the Cloud Damien Contreras ダミアン コントレラ Customer Engineer Specialist, Data Analytics, Google Cloud アジェンダ 1 2 4 5 6 はじめに 移行する前の DWHの移行 GCP と連動 データの表示 準備 について はじめに 01 移行する前に| データウェアハウスの欠点 リアルタイムの負担は受 データ増加 けられない コスト データ形式対応外 セルフサービス分析 が難 しい ベンダーロックイン の心 スタースキーマとディメン 配 表ションとファクト表に合わ せる 移行する前に | データレイクの欠点 コスト クラスターのリソースの バージョンアップ バランス 複数のデータレイクが構築 パートナー、人材採用が困 される 難 移行する前に | Google Cloud の価値 コストパフォーマン 弾力性のある構 セキュリティー スが良い サイロはない サーバーレスでNo-ops ANSI SQL-2011 移行をする前に考 えること 02 Partners Cloud plan & cloud deploy 移行する前に | 社内にスキルはない場合 グーグルの支援 パートナーと BigQuery のスター Google リソースが ターパック 協力し合う https://cloud.google.com/partner s/?hl=ja 移行する前に | TCO & ROI アンケートを記入するだけ で 総所有コストのも計算 移行する前に | クラウドで構築 サイロ化に 2 3 なっている 1 データセット データと関連データの Proof of 基盤を構築 発見 Concept 誰でも ML を 使えれるよう 6 5 4 に 機械学習 ソースデータを 周りのシステム 移行 と通信用のツー ルの構築 DWH の移行について03 移行する前に | DWH移行対応 t n r m h o s v b ? Teradata IBM AWS Azure SQL Hadoop Oracle Snowfake Verica SAP BW その他 Netezza Redshif BigQuery t Teradata IBM Netezza から 13 IBM Netezza | アーキテクチャ FPGA CPU メモリー NZSQLコマンド:DML, データダンプ Host FPGA CPU (Linux JDBCコネクター：SQLクエリ メモリー サー バ) FPGA CPU Symmetric Multiprocessing メモリー (SMP)複数のマイクロプロセッサ Disk S-Blade Network Enclosure fabric AMPP Massively Parallel Processing Architecture (MPP) 大量な並行処理 IBM Netezza | データタイプ IBM Netezza の 31 タイプを全部 BigQuery でマッピングが出来ます IBM Netezza BigQuery VARCHAR STRING BOOLEANま BigQuery : TRUE / FALSE BOOL たBOOL Netezza : True / False, 1 / 0, yes / no, on / of TIME / TIMETZ / TIME BigQuery : の TIME でタイムゾーンはない TIME_WITH_TI ME_ZONE ARRAY Netezza : VARCHARのデータタイプに保存 IBM