The Services Included in Google Cloud Platform
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HW&Co. Landscape Industry Reader Template
TECHNOLOGY, MEDIA, & TELECOM QUARTERLY SOFTWARE SECTOR REVIEW │ 3Q 2016 www.harriswilliams.com Investment banking services are provided by Harris Williams LLC, a registered broker-dealer and member of FINRA and SIPC, and Harris Williams & Co. Ltd, which is authorised and regulated by the Financial Conduct Authority. Harris Williams & Co. is a trade name under which Harris Williams LLC and Harris Williams & Co. Ltd conduct business. TECHNOLOGY, MEDIA, & TELECOM QUARTERLY SOFTWARE SECTOR REVIEW │ 3Q 2016 HARRIS WILLIAMS & CO. OVERVIEW HARRIS WILLIAMS & CO. (HW&CO.) GLOBAL ADVISORY PLATFORM CONTENTS . DEAL SPOTLIGHT . M&A TRANSACTIONS – 2Q 2016 KEY FACTS . SOFTWARE M&A ACTIVITY . 25 year history with over 120 . SOFTWARE SECTOR OVERVIEWS closed transactions in the . SOFTWARE PRIVATE PLACEMENTS last 24 months OVERVIEW . SOFTWARE PUBLIC COMPARABLES . Approximately 250 OVERVIEW professionals across seven . TECHNOLOGY IPO OVERVIEW offices in the U.S. and . DEBT MARKET OVERVIEW Europe . APPENDIX: PUBLIC COMPARABLES DETAIL . Strategic relationships in India and China HW&Co. Office TMT CONTACTS Network Office UNITED STATES . 10 industry groups Jeff Bistrong Managing Director HW&CO. TECHNOLOGY, MEDIA & TELECOM (TMT) GROUP FOCUS AREAS [email protected] Sam Hendler SOFTWARE / SAAS INTERNET & DIGITAL MEDIA Managing Director [email protected] . Enterprise Software . IT and Tech-enabled . AdTech and Marketing . Digital Media and Content Services Solutions Mike Wilkins . Data and Analytics . eCommerce Managing Director . Infrastructure and . Data Center and . Consumer Internet . Mobile [email protected] Managed Services Security Software EUROPE Thierry Monjauze TMT VERTICAL FOCUS AREAS Managing Director [email protected] . Education . Fintech . Manufacturing . Public Sector and Non-Profit . Energy, Power, and . Healthcare IT . Professional Services . Supply Chain, Transportation, TO SUBSCRIBE PLEASE EMAIL: Infrastructure and Logistics *[email protected] SELECT RECENT HW&CO. -
Redis and Memcached
Redis and Memcached Speaker: Vladimir Zivkovic, Manager, IT June, 2019 Problem Scenario • Web Site users wanting to access data extremely quickly (< 200ms) • Data being shared between different layers of the stack • Cache a web page sessions • Research and test feasibility of using Redis as a solution for storing and retrieving data quickly • Load data into Redis to test ETL feasibility and Performance • Goal - get sub-second response for API calls for retrieving data !2 Why Redis • In-memory key-value store, with persistence • Open source • Written in C • It can handle up to 2^32 keys, and was tested in practice to handle at least 250 million of keys per instance.” - http://redis.io/topics/faq • Most popular key-value store - http://db-engines.com/en/ranking !3 History • REmote DIctionary Server • Released in 2009 • Built in order to scale a website: http://lloogg.com/ • The web application of lloogg was an ajax app to show the site traffic in real time. Needed a DB handling fast writes, and fast ”get latest N items” operation. !4 Redis Data types • Strings • Bitmaps • Lists • Hyperlogs • Sets • Geospatial Indexes • Sorted Sets • Hashes !5 Redis protocol • redis[“key”] = “value” • Values can be strings, lists or sets • Push and pop elements (atomic) • Fetch arbitrary set and array elements • Sorting • Data is written to disk asynchronously !6 Memory Footprint • An empty instance uses ~ 3MB of memory. • For 1 Million small Keys => String Value pairs use ~ 85MB of memory. • 1 Million Keys => Hash value, representing an object with 5 fields, -
Starburst Enterprise on Google Cloud
SOLUTION BRIEF Starburst Enterprise on Google Cloud The Starburst Enterprise Difference As organizations scale up, Starburst Enterprise on Google Cloud drives Available on the Google Cloud Marketplace, the better business outcomes, consistency, and reliability, delighting your data Starburst Enterprise platform is a fully supported, engineers and scientists. Teams look to Starburst Enterprise on Google Cloud production-tested, enterprise-grade distribution for expertise & constant fine-tuning that results in overall lower costs & faster of the open source Trino MPP SQL query engine. time-to-insights: Starburst integrates Google’s scalable cloud storage and computing services with a more Performance: stable, secure, efficient, and cost-effective way Includes the latest optimizations; Starburst Cached Views available for to query all your enterprise data, wherever it frequently accessed data; stable code that minimizes failed queries. resides. Leading organizations across multiple industries Connectivity rely on Starburst Enterprise and Google. 40+ supported enterprise connectors; high performance connectors for Oracle, Teradata, Snowflake, IBM DB2, Delta Lake, and many more. Analytics Anywhere Designed for the separation of storage and Security compute, Trino is ideal for querying data residing in multiple systems, from cloud data lakes to Role-based access control (via Apache Ranger); Kerberos, OKTA, LDAP legacy data warehouses. Deployed via Google integration; data encryption & masking; query auditing to see who is doing what. Kubernetes Engine (GKE), Starburst Enterprise on Google Cloud enables the user to run analytic Management queries across Google Cloud data sources and on-prem systems such as Teradata, Oracle, Enhanced tools for configuration, auto scaling, and Starburst Insights and others via Trino clusters. Within a single monitoring dashboards; easy deployment on Google platforms. -
Modeling and Analyzing Latency in the Memcached System
Modeling and Analyzing Latency in the Memcached system Wenxue Cheng1, Fengyuan Ren1, Wanchun Jiang2, Tong Zhang1 1Tsinghua National Laboratory for Information Science and Technology, Beijing, China 1Department of Computer Science and Technology, Tsinghua University, Beijing, China 2School of Information Science and Engineering, Central South University, Changsha, China March 27, 2017 abstract Memcached is a widely used in-memory caching solution in large-scale searching scenarios. The most pivotal performance metric in Memcached is latency, which is affected by various factors including the workload pattern, the service rate, the unbalanced load distribution and the cache miss ratio. To quantitate the impact of each factor on latency, we establish a theoretical model for the Memcached system. Specially, we formulate the unbalanced load distribution among Memcached servers by a set of probabilities, capture the burst and concurrent key arrivals at Memcached servers in form of batching blocks, and add a cache miss processing stage. Based on this model, algebraic derivations are conducted to estimate latency in Memcached. The latency estimation is validated by intensive experiments. Moreover, we obtain a quantitative understanding of how much improvement of latency performance can be achieved by optimizing each factor and provide several useful recommendations to optimal latency in Memcached. Keywords Memcached, Latency, Modeling, Quantitative Analysis 1 Introduction Memcached [1] has been adopted in many large-scale websites, including Facebook, LiveJournal, Wikipedia, Flickr, Twitter and Youtube. In Memcached, a web request will generate hundreds of Memcached keys that will be further processed in the memory of parallel Memcached servers. With this parallel in-memory processing method, Memcached can extensively speed up and scale up searching applications [2]. -
Google Cloud Issue Summary Multiple Products - 2020-08-19 All Dates/Times Relative to US/Pacific
Google Cloud Issue Summary Multiple Products - 2020-08-19 All dates/times relative to US/Pacific Starting on August 19, 2020, from 20:55 to 03:30, multiple G Suite and Google Cloud Platform products experienced errors, unavailability, and delivery delays. Most of these issues involved creating, uploading, copying, or delivering content. The total incident duration was 6 hours and 35 minutes, though the impact period differed between products, and impact was mitigated earlier for most users and services. We understand that this issue has impacted our valued customers and users, and we apologize to those who were affected. DETAILED DESCRIPTION OF IMPACT Starting on August 19, 2020, from 20:55 to 03:30, Google Cloud services exhibited the following issues: ● Gmail: The Gmail service was unavailable for some users, and email delivery was delayed. About 0.73% of Gmail users (both consumer and G Suite) active within the preceding seven days experienced 3 or more availability errors during the outage period. G Suite customers accounted for 27% of affected Gmail users. Additionally, some users experienced errors when adding attachments to messages. Impact on Gmail was mitigated by 03:30, and all messages delayed by this incident have been delivered. ● Drive: Some Google Drive users experienced errors and elevated latency. Approximately 1.5% of Drive users (both consumer and G Suite) active within the preceding 24 hours experienced 3 or more errors during the outage period. ● Docs and Editors: Some Google Docs users experienced issues with image creation actions (for example, uploading an image, copying a document with an image, or using a template with images). -
The Book of Apigee Edge Antipatterns V2.0
The Book of Apigee Edge Antipatterns Avoid common pitfalls, maximize the power of your APIs Version 2.0 Google Cloud Privileged and confidential. apigee 1 Contents Introduction to Antipatterns 3 What is this book about? 4 Why did we write it? 5 Antipattern Context 5 Target Audience 5 Authors 6 Acknowledgements 6 Edge Antipatterns 1. Policy Antipatterns 8 1.1. Use waitForComplete() in JavaScript code 8 1.2. Set Long Expiration time for OAuth Access and Refresh Token 13 1.3. Use Greedy Quantifiers in RegularExpressionProtection policy 16 1.4. Cache Error Responses 19 1.5. Store data greater than 512kb size in Cache 24 1.6. Log data to third party servers using JavaScript policy 27 1.7. Invoke the MessageLogging policy multiple times in an API proxy 29 1.8. Configure a Non Distributed Quota 36 1.9. Re-use a Quota policy 38 1.10. Use the RaiseFault policy under inappropriate conditions 44 1.11. Access multi-value HTTP Headers incorrectly in an API proxy 49 1.12. Use Service Callout policy to invoke a backend service in a No Target API proxy 54 Google Cloud Privileged and confidential. apigee 2 2. Performance Antipatterns 58 2.1. Leave unused NodeJS API Proxies deployed 58 3. Generic Antipatterns 60 3.1. Invoke Management API calls from an API proxy 60 3.2. Invoke a Proxy within Proxy using custom code or as a Target 65 3.3. Manage Edge Resources without using Source Control Management 69 3.4. Define multiple virtual hosts with same host alias and port number 73 3.5. -
Release 2.5.5 Ask Solem Contributors
Celery Documentation Release 2.5.5 Ask Solem Contributors February 04, 2014 Contents i ii Celery Documentation, Release 2.5.5 Contents: Contents 1 Celery Documentation, Release 2.5.5 2 Contents CHAPTER 1 Getting Started Release 2.5 Date February 04, 2014 1.1 Introduction Version 2.5.5 Web http://celeryproject.org/ Download http://pypi.python.org/pypi/celery/ Source http://github.com/celery/celery/ Keywords task queue, job queue, asynchronous, rabbitmq, amqp, redis, python, webhooks, queue, dis- tributed – • Synopsis • Overview • Example • Features • Documentation • Installation – Bundles – Downloading and installing from source – Using the development version 1.1.1 Synopsis Celery is an open source asynchronous task queue/job queue based on distributed message passing. Focused on real- time operation, but supports scheduling as well. The execution units, called tasks, are executed concurrently on one or more worker nodes using multiprocessing, Eventlet or gevent. Tasks can execute asynchronously (in the background) or synchronously (wait until ready). Celery is used in production systems to process millions of tasks every hour. 3 Celery Documentation, Release 2.5.5 Celery is written in Python, but the protocol can be implemented in any language. It can also operate with other languages using webhooks. There’s also RCelery for the Ruby programming language, and a PHP client. The recommended message broker is RabbitMQ, but support for Redis, MongoDB, Beanstalk, Amazon SQS, CouchDB and databases (using SQLAlchemy or the Django ORM) is also available. Celery is easy to integrate with web frameworks, some of which even have integration packages: Django django-celery Pyramid pyramid_celery Pylons celery-pylons Flask flask-celery web2py web2py-celery 1.1.2 Overview This is a high level overview of the architecture. -
Apigee X Migration Offering
Apigee X Migration Offering Overview Today, enterprises on their digital transformation journeys are striving for “Digital Excellence” to meet new digital demands. To achieve this, they are looking to accelerate their journeys to the cloud and revamp their API strategies. Businesses are looking to build APIs that can operate anywhere to provide new and seamless cus- tomer experiences quickly and securely. In February 2021, Google announced the launch of the new version of the cloud API management platform Apigee called Apigee X. It will provide enterprises with a high performing, reliable, and global digital transformation platform that drives success with digital excellence. Apigee X inte- grates deeply with Google Cloud Platform offerings to provide improved performance, scalability, controls and AI powered automation & security that clients need to provide un-parallel customer experiences. Partnerships Fresh Gravity is an official partner of Google Cloud and has deep experience in implementing GCP products like Apigee/Hybrid, Anthos, GKE, Cloud Run, Cloud CDN, Appsheet, BigQuery, Cloud Armor and others. Apigee X Value Proposition Apigee X provides several benefits to clients for them to consider migrating from their existing Apigee Edge platform, whether on-premise or on the cloud, to better manage their APIs. Enhanced customer experience through global reach, better performance, scalability and predictability • Global reach for multi-region setup, distributed caching, scaling, and peak traffic support • Managed autoscaling for runtime instance ingress as well as environments independently based on API traffic • AI-powered automation and ML capabilities help to autonomously identify anomalies, predict traffic for peak seasons, and ensure APIs adhere to compliance requirements. -
Google Cloud Identity Services
INTRODUCING Google Cloud Identity Services One account. All of Google Enter your email Next Enterprise identity made easy A robust and secure identity model is the foundation for enterprise success. Google Cloud’s identity services bring user lifecycle management, directory services, account security, single sign-on, mobile device management and more in a simple integrated solution. Introduction Millions of businesses and schools rely on Google Cloud’s identity services every day when they sign in to products like Google Drive and Google Cloud Platform (GCP). They offer core identity services that make it simple, secure and reliable for users to log in and for administrators to manage usage across the organization. These core features fall into six main areas, where we focus. • User Lifecyle Management • Single sign-on (SSO) • Directory • Reporting & Analytics • Account Security • Endpoint Management User Lifecyle Management Endpoint Directory Management Google Identity Account Security Reporting & Analytics SSO “Google provides business-critical solutions like serving as the central secure access point for cloud apps, while also providing infrastructure for these services like the identity directory.” -Justin Slaten, Manager, Enterprise Technology & Client Systems at Netflix User Lifecycle Management Directory Users are the core of any identity platform, and Google Cloud identity services make it easy the ability to manage access when they join, move to manage users and groups. Everything from within, or leave an organization is important to setting permissions to resetting passwords is administrators. Google Cloud identity services in one location so administrators can quickly make user lifecycle management easy with complete common tasks. Individual Google the unified Google Admin console and APIs. -
Manpreet Singh
MANPREET SINGH SUMMARY OF EXPERTISE ● 1 Year of Co-op experience at SAP as SLT/HANA Product support Engineer. ● 2+ years of full time experience in US IT firm named Cognizant as Java and ESB Developer. ● Broad understanding of Machine Learning, AI and hands on with latest developments in IoT. ● Experience in Penetration Testing, Intrusion Detection, Digital forensics and Risk Management. ● Sound Knowledge and Experience in Google API Management Platform named Apigee. ● Well acquainted with knowledge related to IT Infrastructure and SOA architecture. ● Good organizational, analytical, problem-solving skills and a great team player. ACADEMIC & PROFESSIONAL DEVELOPMENT Master of Engineering (Sep 2017 - Apr 2019) University of Victoria, Canada Electrical and Computer Engineering Bachelor of Technology (Aug 2011 - May 2015) LPU, Punjab, India Electronics and Communication Engineering TECHNICAL SKILLS Enterprise Tools SAP SLT, Apigee Edge, SAG webMethods, Soap UI, Splunk, SNow Penetration Testing Tools Nessus, Zenmap, Wireshark, Hydra, Burp-suite, Metasploit Programming C++, Java, Python Database MySQL Web Development Wordpress, HTML5, CSS3 Network TCP/IP, OSI Model, WLAN/LAN technologies Operating System Windows, Linux (Kali), Mac OS, iOS, Android Interpersonal Leadership, Teamwork, Time Management, Communication WORK EXPERIENCE Software Dev QA Engineer 1 (August 2019- Present) Fortinet Technologies, Burnaby, BC Canada. ● Work as Software developer for various security interfaces. ● Work as QA engineer for testing the code in production and development. Product Support Engineer (Sept 2018 – August 2019) SAP, Vancouver, Canada SAP Landscape Transformation Replication Server (SLT) Engineer ● Worked as a SLT product support engineer; handling Configurations, Troubleshooting and Incident Handling for top SAP clients. ● Handled (VH) priority issues for real business problems using live troubleshooting sessions for Max Attention Customers like Apple, Porsche, Coca-Cola. -
Data Warehouse Offload to Google Bigquery
DATA WAREHOUSE OFFLOAD TO GOOGLE BIGQUERY In a world where big data presents both a major opportunity and a considerable challenge, a rigid, highly governed traditional enterprise data warehouse isn’t KEY BENEFITS OF MOVING always the best choice for processing large workloads, or for applications like TO GOOGLE BIGQUERY analytics. Google BigQuery is a lightning-fast cloud-based analytics database that lets you keep up with the growing data volumes you need to derive meaningful • Reduces costs and business value, while controlling costs and optimizing performance. shifts your investment from CAPEX to OPEX Pythian’s Data Warehouse Offload to Google BigQuery service moves your workload from an existing legacy data warehouse to a Google BigQuery data • Scales easily and on demand warehouse using our proven methodology and Google experts–starting with a fixed-cost Proof of Concept stage that will quickly demonstrate success. • Enables self-service analytics and advanced analytics GETTING STARTED The Pythian Data Warehouse Offload to Google BigQuery service follows a proven methodology and delivers a Proof of Concept (POC) that demonstrates viability and value within three to four weeks. The POC phase will follow this workflow: 1. Assess existing data warehouse environment to identify tables and up to two reports that will be offloaded in this phase 2. Provision GCP infrastructure including Cloud storage, Bastion hosts, BigQuery, and Networking 3. Implement full repeatable extract/load process for selected tables 4. Implement selected reports on BigQuery 5. Produce report PYTHIAN DELIVERS By the end of the first stage of our engagement, you can expect to have: • Working prototype on BigQuery • Up to two reports • Demonstrated analysis capabilities using one fact with five associated dimensions www.pythian.com • Report that includes: an assessment of your current setup and support you need to plan and maintain your full (including a cost analysis for BigQuery), performance/ Google BigQuery data warehouse and enterprise analytics usability analysis of POC vs. -
Google Cloud Whitepaper
1 Table of contents Introduction 3 The compliance landscape for UK health and social care data 4 Legislation governing UK health data 4 Overview of NHS Digital in England 6 Overview of the Use of Public Cloud Guidance 6 Overview of the DSP Toolkit 7 Google Cloud Platform information governance overview 8 Google Cloud Platform’s approach to security and data protection 8 The Shared Responsibility Model 12 How Google Cloud Platform meets NHS Information Governance requirements 13 Data Security Standard 1 13 Data Security Standard 2 20 Data Security Standard 3 22 Data Security Standard 4 22 Data Security Standard 5 25 Data Security Standard 6 26 Data Security Standard 7 29 Data Security Standard 8 31 Data Security Standard 9 32 Data Security Standard 10 33 How Google Cloud Platform helps customers meet their DSP Toolkit requirements 34 Google Cloud Platform products to help with compliance 34 Google Cloud Platform Terms of Service and Conditions 37 Additional Resources to help Google Cloud Platform customers 37 Conclusion 38 2 Disclaimer This document was last updated in O ctober 2020 a nd is for informational purposes only. Google does not intend the information or recommendations in this document to constitute legal advice. Each customer must independently evaluate its own particular use of the services as appropriate to support its legal compliance obligations. Since Google is continually improving security and other features for our customers, some of the policies, procedures, and technologies mentioned in this document may have changed. Please visit cloud.google.com/security/compliance or contact your Google Cloud Account Representative to check for updated information.