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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 -
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. -
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. -
Spanner: Becoming a SQL System
Spanner: Becoming a SQL System David F. Bacon Nathan Bales Nico Bruno Brian F. Cooper Adam Dickinson Andrew Fikes Campbell Fraser Andrey Gubarev Milind Joshi Eugene Kogan Alexander Lloyd Sergey Melnik Rajesh Rao David Shue Christopher Taylor Marcel van der Holst Dale Woodford Google, Inc. ABSTRACT this paper, we focus on the “database system” aspects of Spanner, Spanner is a globally-distributed data management system that in particular how query execution has evolved and forced the rest backs hundreds of mission-critical services at Google. Spanner of Spanner to evolve. Most of these changes have occurred since is built on ideas from both the systems and database communi- [5] was written, and in many ways today’s Spanner is very different ties. The first Spanner paper published at OSDI’12 focused on the from what was described there. systems aspects such as scalability, automatic sharding, fault tol- A prime motivation for this evolution towards a more “database- erance, consistent replication, external consistency, and wide-area like” system was driven by the experiences of Google developers distribution. This paper highlights the database DNA of Spanner. trying to build on previous “key-value” storage systems. The pro- We describe distributed query execution in the presence of reshard- totypical example of such a key-value system is Bigtable [4], which ing, query restarts upon transient failures, range extraction that continues to see massive usage at Google for a variety of applica- drives query routing and index seeks, and the improved blockwise- tions. However, developers of many OLTP applications found it columnar storage format. -
Economic and Social Impacts of Google Cloud September 2018 Economic and Social Impacts of Google Cloud |
Economic and social impacts of Google Cloud September 2018 Economic and social impacts of Google Cloud | Contents Executive Summary 03 Introduction 10 Productivity impacts 15 Social and other impacts 29 Barriers to Cloud adoption and use 38 Policy actions to support Cloud adoption 42 Appendix 1. Country Sections 48 Appendix 2. Methodology 105 This final report (the “Final Report”) has been prepared by Deloitte Financial Advisory, S.L.U. (“Deloitte”) for Google in accordance with the contract with them dated 23rd February 2018 (“the Contract”) and on the basis of the scope and limitations set out below. The Final Report has been prepared solely for the purposes of assessment of the economic and social impacts of Google Cloud as set out in the Contract. It should not be used for any other purposes or in any other context, and Deloitte accepts no responsibility for its use in either regard. The Final Report is provided exclusively for Google’s use under the terms of the Contract. No party other than Google is entitled to rely on the Final Report for any purpose whatsoever and Deloitte accepts no responsibility or liability or duty of care to any party other than Google in respect of the Final Report and any of its contents. As set out in the Contract, the scope of our work has been limited by the time, information and explanations made available to us. The information contained in the Final Report has been obtained from Google and third party sources that are clearly referenced in the appropriate sections of the Final Report. -
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. -
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 -
Nosql Database Comparison: Bigtable, Cassandra and Mongodb
Running Head: NOSQL DATABASE COMPARISON: BIGTABLE, CASSANDRA AND MONGODB NoSQL Database Comparison: Bigtable, Cassandra and MongoDB CJ Campbell Brigham Young University October 16, 2015 1 NOSQL DATABASE COMPARISON: BIGTABLE, CASSANDRA AND MONGODB INTRODUCTION THE SYSTEMS Google Bigtable History Data model & operations Physical Storage ACID properties Scalability Apache Cassandra History Data model & operations Physical Storage ACID properties Scalability MongoDB History Data model & operations Physical Storage ACID properties Scalability Differences Conclusion References 2 NOSQL DATABASE COMPARISON: BIGTABLE, CASSANDRA AND MONGODB Introduction As distributed systems are adopted and grown to scale, the need for scalable database solutions which meet the application’s exact need has become increasingly important. In the early days of computing, databases were almost entirely relational. Today, new breeds of database have emerged, called NoSQL databases. They are a common element in the grand design for most distributed software platforms. Each database is suited to a slightly different purpose from its peers. This paper discusses the features, similarities, and differences of three NoSQL databases: Google Bigtable, Apache Cassandra, and MongoDB. The Systems In this section, each of the three NoSQL databases are analyzed in-depth, starting with Google Bigtable, then Apache Cassandra, and finally MongoDB. Analysis includes their history, data model, accepted operations, physical storage schema, ACID properties, and scalability. Google Bigtable History. Bigtable was designed within Google to meet their internal data processing needs at scale. It began development in 2004 as part of their effort to handle large amounts of data across applications such as web indexing, Google Earth, Google Finance and more (Google, Inc., 2006). It first went into production use in April 2005. -
Google Cloud Security Whitepapers
1 Google Cloud Security Whitepapers March 2018 Google Cloud Encryption at Rest in Encryption in Transit in Application Layer Infrastructure Security Google Cloud Google Cloud Transport Security Design Overview in Google Cloud 2 Table of Contents Google Cloud Infrastructure Security Design Overview . 3 Encryption at Rest in Google Cloud . 23 Encryption in Transit in Google Cloud . 43 Application Layer Transport Security in Google Cloud . 75 3 A technical whitepaper from Google Cloud 4 Table of Contents Introduction . 7 Secure Low Level Infrastructure . 8 Security of Physical Premises Hardware Design and Provenance Secure Boot Stack and Machine Identity Secure Service Deployment . 9 Service Identity, Integrity, and Isolation Inter-Service Access Management Encryption of Inter-Service Communication Access Management of End User Data Secure Data Storage . 14 Encryption at Rest Deletion of Data Secure Internet Communication . 15 Google Front End Service Denial of Service (DoS) Protection User Authentication Operational Security . 17 Safe Software Development Keeping Employee Devices and Credentials Safe Reducing Insider Risk Intrusion Detection 5 Securing the Google Cloud Platform (GCP) . .. 19 Conclusion . 21 Additional Reading . 22 The content contained herein is correct as of January 2017, and represents the status quo as of the time it was written. Google’s security policies and systems may change going forward, as we continually improve protection for our customers. 6 CIO-level summary • Google has a global scale technical infrastructure designed to provide security through the entire information processing lifecycle at Google. This infrastructure provides secure deployment of services, secure storage of data with end user privacy safeguards, secure communications between services, secure and private communication with customers over the internet, and safe operation by administrators. -
Ray Cromwell
Building Applications with Google APIs Ray Cromwell Monday, June 1, 2009 “There’s an API for that” • code.google.com shows 60+ APIs • full spectrum (client, server, mobile, cloud) • application oriented (android, opensocial) • Does Google have a Platform? Monday, June 1, 2009 Application Ecosystem Client REST/JSON, GWT, Server ProtocolBuffers Earth PHP Java O3D App Services Media Docs Python Ruby Utility Blogger Spreadsheets Maps/Geo JPA/JDO/Other Translate Base Datastore GViz Social MySQL Search OpenSocial Auth FriendConnect $$$ ... GData Contacts AdSense Checkout Monday, June 1, 2009 Timefire • Store and Index large # of time series data • Scalable Charting Engine • Social Collaboration • Story Telling + Video/Audio sync • Like “Google Maps” but for “Time” Monday, June 1, 2009 Android Version 98% Shared Code with Web version Monday, June 1, 2009 Android • Full API stack • Tight integration with WebKit browser • Local database, 2D and 3D APIs • External XML UI/Layout system • Makes separating presentation from logic easier, benefits code sharing Monday, June 1, 2009 How was this done? • Google Web Toolkit is the Foundation • Target GWT JRE as LCD • Use Guice Dependency Injection for platform-specific APIs • Leverage GWT 1.6 event system Monday, June 1, 2009 Example App Code Device/Service JRE interfaces Guice Android Browser Impl Impl Android GWT Specific Specific Monday, June 1, 2009 Shared Widget Events interface HasClickHandler interface HasClickHandler addClickHandler(injectedHandler) addClickHandler(injectedHandler) Gin binds GwtHandlerImpl -
Before We Start…
Before we start… This is the Introduction to Databases Design and Implementation workshop • Download material: dartgo.org/db-design • Poll / Interactive questions: dartgo.org/poll • Optional software: https://dev.mysql.com/downloads/workbench/ • More info: rc.dartmouth.edu Introduction to Database Design and Implementation Christian Darabos, Ph.D. [email protected] Slides download: dartgo.org/db-design Overview • introduction to Databases and this workshop • development/production environments • tools (admin, browse, query, etc.) • DB design, UML and case study (http://www.datanamic.com/support/lt-dez005-introduction-db-model ing.html) • port model into MySQL Workbench Right-click > Open link in new window To keep open slides and poll dartgo.org/poll Research Computing Introduction • Research Computing service offering • Definition of a Relational Database • Overview of this workshop Right-click > Open link in new window To keep open slides and poll dartgo.org/poll Definition of a Relational Database (SQL) • a database type structured to recognize relations among stored items of information • designed to store text, dates/times, integers, floating-point number • implemented as a series of tables Mental Model • Think of a database as a set of spreadsheets • Each spreadsheet (or table) represents a type of entity (person, object, concept, etc.) • Better than Excel because it also models the relationship between the entities Why use a Relational Database • concurrent (simultaneous) read and write • powerful selecting, filtering and sorting cross-referencing tables • large quantity of structured storage and standardized distribution • minimize post-processing (simple analytics tools pre-implemented) • automate using any scripting and programming languages (R, Matlab, Python, C++, Java, PHP) • web-proof SQL vs.