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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 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 -
Google Cloud Platform Integration
Solidatus FACTSHEET Google Cloud Platform Integration The Solidatus Google Cloud Platform (GCP) integration suite helps to discover data structures and lineage in GCP and automatically create and maintain Solidatus models describing these assets when they are added to GCP and when they are changed. As of January 2019, the GCP integration supports the following scenarios: • Through the Solidatus UI: – Load BigQuery dataset schemas as Solidatus objects on-demand. • Automatically using a Solidatus Agent: – Detect new BigQuery schemas and add to a Solidatus model. – Detect changes to BigQuery schemas and update a Solidatus model. – Detect new files in Google Cloud Storage (GCS) and add to a Solidatus model. – Automatically detect changes to files in GCS and update a Solidatus model. • Automatically at build time: – Extract structure and lineage from a Google Cloud Dataflow and create or update a Solidatus model. FEATURES BigQuery Loader Apache Beam (GCP Dataflow) Lineage A user can import a BigQuery table definition, directly Mapper from Google, as an object into a Solidatus model. A developer can visualise their Apache Beam job’s The import supports both nested and flat structures, pipeline in a Solidatus model. The model helps both and also includes meta data about the table and developers and analysts to see that data from sources dataset. Objects created via the BigQuery Loader is correctly mapped through transforms to their sinks, can be easily updated by a right-clicking on an providing a data lineage model of the pipeline. object in Solidatus. Updating models using this Generating the models can be ad-hoc (on-demand by feature provides the ability to visualise differences in the developer) or built into a CI/CD process. -
Google's Mission
& Big Data & Rocket Fuel Dr Raj Subramani, HSBC Reza Rokni, Google Cloud, Solutions Architect Adrian Poole, Google Cloud, Google’s Mission Organize the world’s information and make it universally accessible and useful Eight cloud products with ONE BILLION Users Increasing Marginal Cost of Change $ Traditional Architectures Prohibitively Expensive change Marginal cost of 18 years of Google R&D / Investment Google Cloud Native Architectures (GCP) Increasing complexity of systems and processes Containers at Google Number of running jobs Enabled Google to grow our fleet over 10x faster than we grew our ops team Core Ops Team 2004 2016 4 Google’s innovation in data Millwheel F1 Spanner TensorFlow MapReduce Dremel Flume GFS Bigtable Colossus Megastore Pub/Sub Dataflow 2002 2004 2006 2008 2010 2012 2013 2016 Proprietary + Confidential5 Google’s innovation in data Dataflow Spanner NoSQL Spanner Cloud ML Dataproc BigQuery Dataflow GCS Bigtable GCS Datastore Pub/Sub Dataflow 2002 2004 2006 2008 2010 2012 2013 2016 Proprietary + Confidential6 Now available on Google Cloud Platform Compute Storage & Databases App Engine Container Compute Storage Bigtable Spanner Cloud SQL Datastore Engine Engine Big Data Machine Learning BigQuery Pub/Sub Dataflow Dataproc Datalab Vision API Machine Speech API Translate API Learning Lesson of the last 10 years... ● Democratise ML ● Big datasets beat fancy algorithms ● Good Models ● Lots of compute Google BigQuery BigQuery is Google's fully managed, petabyte scale, low cost enterprise data warehouse for analytics. BigQuery is serverless. There is no infrastructure to manage and you don't need a database administrator, so you can focus on analyzing data to find meaningful insights using familiar SQL. -
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. -
Are3na Crabbé Et Al
ARe3NA Crabbé et al. (2014) AAA for Data and Services (D1.1.2 & D1.2.2): Analysing Standards &Technologies for AAA ISA Action 1.17: A Reusable INSPIRE Reference Platform (ARE3NA) Authentication, Authorization & Accounting for Data and Services in EU Public Administrations D1.1.2 & D1.2.2– Analysing standards and technologies for AAA Ann Crabbé Danny Vandenbroucke Andreas Matheus Dirk Frigne Frank Maes Reijer Copier 0 ARe3NA Crabbé et al. (2014) AAA for Data and Services (D1.1.2 & D1.2.2): Analysing Standards &Technologies for AAA This publication is a Deliverable of Action 1.17 of the Interoperability Solutions for European Public Admin- istrations (ISA) Programme of the European Union, A Reusable INSPIRE Reference Platform (ARE3NA), managed by the Joint Research Centre, the European Commission’s in-house science service. Disclaimer The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication. Copyright notice © European Union, 2014. Reuse is authorised, provided the source is acknowledged. The reuse policy of the European Commission is implemented by the Decision on the reuse of Commission documents of 12 December 2011. Bibliographic Information: Ann Crabbé, Danny Vandenbroucke, Andreas Matheus, Dirk Frigne, Frank Maes and Reijer Copier Authenti- cation, Authorization and Accounting for Data and Services in EU Public Administrations: D1.1.2 & D1.2.2 – Analysing standards and technologies for AAA. European Commission; 2014. JRC92555 1 ARe3NA Crabbé et al. (2014) AAA for Data and Services (D1.1.2 & D1.2.2): Analysing Standards &Technologies for AAA Contents 1. -
Platform As a Service (Paas) Scope
Platform as a Service (PaaS) Scope: 1. Platform as a Service (PaaS) 2. What is Google App Engine. • Overview • Programming languages support • Data storage • App Engine services • Security 3. When to use Google App Engine. 4. How to use Google App Engine. 1. Platform as a Service (PaaS) • Cloud computing service which provides a computing platform and a solution stack as a service. • Consumer creates the software using tools and/or libraries from the provider. • Provider provides the networks, servers, storage, etc. 2. What is Google App Engine. • Overview Google App Engine (GAE) is a Platform as a Service (PaaS) cloud computing platform for developing and hosting web applications in Google-managed data centers. Google App Engine lets you run web applications on Google's infrastructure. Easy to build. Easy to maintain. Easy to scale as the traffic and storage needs grow. Free Yes, free for upto 1 GB of storage and enough CPU and bandwidth to support 5 ??? million page views a month. 10 Applications per Google account. 2. What is Google App Engine. • Programming languages support Java: • App Engine runs JAVA apps on a JAVA 7 virtual machine (currently supports JAVA 6 as well). • Uses JAVA Servlet standard for web applications: •WAR (Web Applications ARchive) directory structure. • Servlet classes • Java Server Pages (JSP) • Static and data files • Deployment descriptor (web.xml) • Other configuration files • Getting started : https://developers.google.com/appengine/docs/java /gettingstarted/ 2. What is Google App Engine. • Programming languages support Python: • Uses WSGI (Web Server Gateway Interface) standard. • Python applications can be written using: • Webapp2 framework • Django framework • Any python code that uses the CGI (Common Gateway Interface) standard. -
Google-Cloud Documentation Release 0.20.0
google-cloud Documentation Release 0.20.0 Google Cloud Platform October 06, 2016 google-cloud 1 Base Client 1 2 Credentials Helpers 5 3 Base Connections 9 4 Exceptions 13 5 Environment Variables 17 6 Configuration 19 6.1 Overview................................................. 19 6.2 Authentication.............................................. 19 7 Authentication 21 7.1 Overview................................................. 21 7.2 Client-Provided Authentication..................................... 21 7.3 Explicit Credentials........................................... 22 7.4 Troubleshooting............................................. 23 7.5 Advanced Customization......................................... 24 8 Long-Running Operations 27 9 Datastore Client 29 9.1 Connection................................................ 32 10 Entities 37 11 Keys 39 12 Queries 43 13 Transactions 47 14 Batches 51 15 Helpers 55 16 Storage Client 57 16.1 Connection................................................ 59 i 17 Blobs / Objects 61 18 Buckets 69 19 ACL 77 20 Batches 81 21 Using the API 83 21.1 Authentication / Configuration...................................... 83 21.2 Manage topics for a project....................................... 83 21.3 Publish messages to a topic....................................... 84 21.4 Manage subscriptions to topics..................................... 84 21.5 Pull messages from a subscription.................................... 86 22 Pub/Sub Client 87 22.1 Connection................................................ 88 -
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 -
Google Certified Professional - Cloud Architect.Exam.57Q
Google Certified Professional - Cloud Architect.exam.57q Number : GoogleCloudArchitect Passing Score : 800 Time Limit : 120 min https://www.gratisexam.com/ Google Certified Professional – Cloud Architect (English) https://www.gratisexam.com/ Testlet 1 Company Overview Mountkirk Games makes online, session-based, multiplayer games for the most popular mobile platforms. Company Background Mountkirk Games builds all of their games with some server-side integration, and has historically used cloud providers to lease physical servers. A few of their games were more popular than expected, and they had problems scaling their application servers, MySQL databases, and analytics tools. Mountkirk’s current model is to write game statistics to files and send them through an ETL tool that loads them into a centralized MySQL database for reporting. Solution Concept Mountkirk Gamesis building a new game, which they expect to be very popular. They plan to deploy the game’s backend on Google Compute Engine so they can capture streaming metrics, run intensive analytics, and take advantage of its autoscaling server environment and integrate with a managed NoSQL database. Technical Requirements Requirements for Game Backend Platform 1. Dynamically scale up or down based on game activity 2. Connect to a managed NoSQL database service 3. Run customize Linux distro Requirements for Game Analytics Platform 1. Dynamically scale up or down based on game activity 2. Process incoming data on the fly directly from the game servers 3. Process data that arrives late because of slow mobile networks 4. Allow SQL queries to access at least 10 TB of historical data 5. Process files that are regularly uploaded by users’ mobile devices 6. -
Accelerating App Delivery
Accelerating App Delivery How aPaaS Enables Fast Delivery & Continuous Innovation Issue 1 2 Welcome 2 Resources 3 From the Gartner Files: Magic Quadrant for Enterprise Application Platform as a Service, Worldwide 32 About Mendix Featuring research from 2 Welcome Innovate or perish. That’s the reality facing every business, regardless of industry. The need to deliver modern, multi-channel applications that engage customers and empower employees has never been more urgent. Yet, fast-growing project backlogs and unhappy business sponsors are clear indications that traditional development approaches aren’t cutting it. Enterprise application Platform-as-a-Service (aPaaS) offers a much-needed way forward, promising to accelerate your application delivery cadence and capacity. But the market is crowded, and not all aPaaS offerings are created equal. In Gartner’s 2015 Magic Quadrant for Enterprise Application Platform as Service (aPaaS), Mendix was positioned as a “Visionary” due to its completeness of vision and ability to execute. Use this complimentary Gartner report to better understand and navigate the aPaaS landscape and ultimately select the platform best suited to your organization’s priorities. Resources In addition to Gartner’s perspective, we have [Video] aPaaS Success Stories included four resources to illustrate how Mendix See how Mendix customers, such as Dun & supports customers through their digital journeys, Bradstreet, LV= Insurance, The Boston Globe empowering them to deliver the right apps with and Kao, are rapidly delivering custom apps that unprecedented speed. differentiate their business. Watch video → Successful App Delivery for the Digital Age Find out how to keep your IT team on track and [Video] The Mendix App Platform Tour quickly deliver the multi-channel, multi-device Take a two-minute tour of the Mendix App apps needed to digitize your business. -
Google Managed Ssl Certificate Pricing
Google Managed Ssl Certificate Pricing Mucous Montague never carcases so radiantly or te-heeing any news southward. Alary Philip transhipping patrilineally while Fletcher always cobwebbed his wreckfish seres bifariously, he enswathes so baggily. Quent attitudinised his truce threw connubial, but tachistoscopic Clarence never wived so reversedly. Why they originated from google managed ssl certificate is Try 90-day Trial SSL Certificate before having real capital to test cert's functionality. ZeroSSL Free SSL Certificates and SSL Tools. A user is far behind likely to buy would you school your affect is secure. You require purchase that single site certificate a multiple-domains certificate SAN Looking for. GlobalSign's Managed PKI platform significantly lowers the sale Cost of Ownership for SSL by reducing the man hours needed to manage certificates and. If you must verify that a nice to edit an ai format is most disliked by the site that point to procure, for cost of managed ssl policies do not working. July 201 Google Chrome made it official If their site doesn't have a security certificate. Best Websites to Buy SSL Certificates 7year & up. Step 1 Purchase your SSL certificate from a reputable vendor into your. Data is slightly different prices are authenticated as a different scenarios where i have verified that does, thank you have been confirmed. But when using its pricing should be misleading because i set. Introducing managed SSL for Google App Engine googblogs. Installing an SSL certificate on Google App Engine Hosting. Low pricing a private global network improved performance and features. Analytics tech notes Adobe Analytics for Google Analytics users.