SAP on Google Cloud with Bigquery
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Annual Report 2018
Pakistan Telecommunication Company Limited Company Telecommunication Pakistan PTCL PAKISTAN ANNUAL REPORT 2018 REPORT ANNUAL /ptcl.official /ptclofficial ANNUAL REPORT Pakistan Telecommunication /theptclcompany Company Limited www.ptcl.com.pk PTCL Headquarters, G-8/4, Islamabad, Pakistan Pakistan Telecommunication Company Limited ANNUAL REPORT 2018 Contents 01COMPANY REVIEW 03FINANCIAL STATEMENTS CONSOLIDATED Corporate Vision, Mission & Core Values 04 Auditors’ Report to the Members 129-135 Board of Directors 06-07 Consolidated Statement of Financial Position 136-137 Corporate Information 08 Consolidated Statement of Profit or Loss 138 The Management 10-11 Consolidated Statement of Comprehensive Income 139 Operating & Financial Highlights 12-16 Consolidated Statement of Cash Flows 140 Chairman’s Review 18-19 Consolidated Statement of Changes in Equity 141 Group CEO’s Message 20-23 Notes to and Forming Part of the Consolidated Financial Statements 142-213 Directors’ Report 26-45 47-46 ہ 2018 Composition of Board’s Sub-Committees 48 Attendance of PTCL Board Members 49 Statement of Compliance with CCG 50-52 Auditors’ Review Report to the Members 53-54 NIC Peshawar 55-58 02STATEMENTS FINANCIAL Auditors’ Report to the Members 61-67 Statement of Financial Position 68-69 04ANNEXES Statement of Profit or Loss 70 Pattern of Shareholding 217-222 Statement of Comprehensive Income 71 Notice of 24th Annual General Meeting 223-226 Statement of Cash Flows 72 Form of Proxy 227 Statement of Changes in Equity 73 229 Notes to and Forming Part of the Financial Statements 74-125 ANNUAL REPORT 2018 Vision Mission To be the leading and most To be the partner of choice for our admired Telecom and ICT provider customers, to develop our people in and for Pakistan. -
Amazon Connect Data Lake Best Practices AWS Whitepaper Amazon Connect Data Lake Best Practices AWS Whitepaper
Amazon Connect Data Lake Best Practices AWS Whitepaper Amazon Connect Data Lake Best Practices AWS Whitepaper Amazon Connect Data Lake Best Practices : AWS Whitepaper Copyright © Amazon Web Services, Inc. and/or its affiliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. Amazon Connect Data Lake Best Practices AWS Whitepaper Table of Contents Abstract and introduction .................................................................................................................... i Abstract .................................................................................................................................... 1 Are you Well-Architected? ........................................................................................................... 1 Introduction .............................................................................................................................. 1 Amazon Connect ................................................................................................................................ 4 Data lake design principles ................................................................................................................. -
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. -
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. -
Mapreduce: Simplified Data Processing On
MapReduce: Simplified Data Processing on Large Clusters Jeffrey Dean and Sanjay Ghemawat [email protected], [email protected] Google, Inc. Abstract given day, etc. Most such computations are conceptu- ally straightforward. However, the input data is usually MapReduce is a programming model and an associ- large and the computations have to be distributed across ated implementation for processing and generating large hundreds or thousands of machines in order to finish in data sets. Users specify a map function that processes a a reasonable amount of time. The issues of how to par- key/value pair to generate a set of intermediate key/value allelize the computation, distribute the data, and handle pairs, and a reduce function that merges all intermediate failures conspire to obscure the original simple compu- values associated with the same intermediate key. Many tation with large amounts of complex code to deal with real world tasks are expressible in this model, as shown these issues. in the paper. As a reaction to this complexity, we designed a new Programs written in this functional style are automati- abstraction that allows us to express the simple computa- cally parallelized and executed on a large cluster of com- tions we were trying to perform but hides the messy de- modity machines. The run-time system takes care of the tails of parallelization, fault-tolerance, data distribution details of partitioning the input data, scheduling the pro- and load balancing in a library. Our abstraction is in- gram's execution across a set of machines, handling ma- spired by the map and reduce primitives present in Lisp chine failures, and managing the required inter-machine and many other functional languages. -
Frequently Asked Questions for Google Bigquery Connector
Frequently Asked Questions for Google BigQuery Connector © Copyright Informatica LLC 2017, 2021. Informatica, the Informatica logo, and Informatica Cloud are trademarks or registered trademarks of Informatica LLC in the United States and many jurisdictions throughout the world. A current list of Informatica trademarks is available on the web at https:// www.informatica.com/trademarks.html. Abstract This article describes frequently asked questions about using Google BigQuery Connector to read data from and write data to Google BigQuery. Supported Versions • Cloud Data Integration Table of Contents General Questions............................................................ 2 Performance Tuning Questions................................................... 5 General Questions What is Google Cloud Platform? Google Cloud Platform is a set of public cloud computing services offered by Google. It provides a range of hosted services for compute, storage, and application development that run on Google hardware. Google Cloud Platform services can be accessed by software developers, cloud administrators, and other enterprise IT professionals over the public internet or through a dedicated network connection. Google Cloud Platform provides Google BigQuery to perform data analytics on large datasets. How can I access Google Cloud Platform? You must create a Google service account to access Google Cloud Platform. To create a Google service account, click the following URL: https://cloud.google.com/ What are the permissions required for the Google service -
What's New for Google in 2020?
Kevin A. McGrail [email protected] What’s new for Google in 2020? Introduction Kevin A. McGrail Director, Business Growth @ InfraShield.com Google G Suite TC, GDE & Ambassador https://www.linkedin.com/in/kmcgrail About the Speaker Kevin A. McGrail Director, Business Growth @ InfraShield.com Member of the Apache Software Foundation Release Manager for Apache SpamAssassin Google G Suite TC, GDE & Ambassador. https://www.linkedin.com/in/kmcgrail 1Q 2020 STORY TIME: Google Overlords, Pixelbook’s Secret Titan Key, & Googlesplain’ing CES Jan 2020 - No new new hardware was announced at CES! - Google Assistant & AI Hey Google, Read this Page Hey Google, turn on the lights at 6AM Hey Google, Leave a Note... CES Jan 2020 (continued) Google Assistant & AI Speed Dial Interpreter Mode (Transcript Mode) Hey Google, that wasn't for you Live Transcripts Hangouts Meet w/Captions Recorder App w/Transcriptions Live Transcribe Coming Next...: https://mashable.com/article/google-translate-transcription-audio/ EXPERT TIP: What is Clipping? And Whispering! Streaming Games - Google Stadia Android Tablets No more Android Tablets? AI AI AI AI AI Looker acquisition for 2.6B https://www.cloudbakers.com/blog/why-cloudbakers-loves-looker-for-business-intelligence-bi From Thomas Kurian, head of Google Cloud: “focusing on digital transformation solutions for retail, healthcare, financial services, media and entertainment, and industrial and manufacturing verticals. He highlighted Google's strengths in AI for each vertical, such as behavioral analytics for retail, -
Apache Hadoop Goes Realtime at Facebook
Apache Hadoop Goes Realtime at Facebook Dhruba Borthakur Joydeep Sen Sarma Jonathan Gray Kannan Muthukkaruppan Nicolas Spiegelberg Hairong Kuang Karthik Ranganathan Dmytro Molkov Aravind Menon Samuel Rash Rodrigo Schmidt Amitanand Aiyer Facebook {dhruba,jssarma,jgray,kannan, nicolas,hairong,kranganathan,dms, aravind.menon,rash,rodrigo, amitanand.s}@fb.com ABSTRACT 1. INTRODUCTION Facebook recently deployed Facebook Messages, its first ever Apache Hadoop [1] is a top-level Apache project that includes user-facing application built on the Apache Hadoop platform. open source implementations of a distributed file system [2] and Apache HBase is a database-like layer built on Hadoop designed MapReduce that were inspired by Googles GFS [5] and to support billions of messages per day. This paper describes the MapReduce [6] projects. The Hadoop ecosystem also includes reasons why Facebook chose Hadoop and HBase over other projects like Apache HBase [4] which is inspired by Googles systems such as Apache Cassandra and Voldemort and discusses BigTable, Apache Hive [3], a data warehouse built on top of the applications requirements for consistency, availability, Hadoop, and Apache ZooKeeper [8], a coordination service for partition tolerance, data model and scalability. We explore the distributed systems. enhancements made to Hadoop to make it a more effective realtime system, the tradeoffs we made while configuring the At Facebook, Hadoop has traditionally been used in conjunction system, and how this solution has significant advantages over the with Hive for storage and analysis of large data sets. Most of this sharded MySQL database scheme used in other applications at analysis occurs in offline batch jobs and the emphasis has been on Facebook and many other web-scale companies. -
New Zealand Reseller Update: June 2021 JUNE
New Zealand Reseller Update: June 2021 JUNE All the stock, all the updates, all you need. Always speak to your Synnex rep before quoting customer If you have colleagues not receiving this monthly Google deck but would like to, please have them sign up here Follow Chrome Enterprise on LinkedIn Channel news June update Questions about Switching to Chrome Promotions Marketing Case Studies Training Product Launches & Stock updates Channel news Chrome OS in Action: Chrome Enterprise has announced new solutions Chrome Demo Tool is live and open for partner sign ups! to accelerate businesses move to Chrome OS On October 20, Chrome OS announced new solutions to help businesses deploy Chromebooks Demo Tool Guide and Chrome OS devices faster, while keeping their employees focused on what matters most. Each solution solves a real-world challenge we know businesses are facing right now and will help them support their distributed workforce. The Chrome Demo Tool is a new tool for Google for Education ● Chrome OS Readiness Tool: Helps businesses segment their workforce and identify and Chrome Enterprise partners with numerous pre-configured which Windows devices are ready to adopt Chrome OS (available 2021). options to demo top Chrome features including single sign-on ● Chrome Enterprise Recommended: Program that identifies verified apps for the Chrome OS environment. (SSO), parallels, zero touch enrollment (ZTE), and many more ● Zero-touch enrollment: Allow businesses to order devices that are already corporate that are coming soon. enrolled so they can drop ship directly to employees. ● Parallels Desktop: Gives businesses access to full-featured Windows and legacy apps locally on Chrome OS. -
Google Search Techniques
Google Search Techniques Google Search Techniques Disclaimer: Using Google to search the Internet will locate resources that are available to the public. While these resources are good for some purposes, serious research and academic work often requires access to databases, articles and books that, if they are available online, are only accessible by subscription. Fortunately, the UMass Library subscribes to most of these services. To access these resources online, go to the UMass Library Web site (library.umass.edu). For the best possible help finding information on any topic, talk to a reference librarian in person. They can help you find the resources you need and can teach you some fantastic techniques for doing your own searches. For a complete guide to Google’s features go to http://www.google.com/help/ Simple Search Strategies Google keeps the specifics of its page-ranking techniques secret, but here are a few things we know about what makes pages appear at the top of your search: - your search terms appears in the title of the web page - your search terms appear in links that lead to that page - your search terms appear in the content of the page (especially in headers) When you choose the search terms you enter into Google, think about the titles you would expect to see on these pages or that you would see in links to these pages. The more well-known your search target, the more easy it will be to find. Obscure topics or topics that share terms with more common topics will take more work to find. -
Building Big Data Storage Solutions (Data Lakes) for Maximum Flexibility
Building Big Data Storage Solutions (Data Lakes) for Maximum Flexibility July 2017 © 2017, Amazon Web Services, Inc. or its affiliates. All rights reserved. Notices This document is provided for informational purposes only. It represents AWS’s current product offerings and practices as of the date of issue of this document, which are subject to change without notice. Customers are responsible for making their own independent assessment of the information in this document and any use of AWS’s products or services, each of which is provided “as is” without warranty of any kind, whether express or implied. This document does not create any warranties, representations, contractual commitments, conditions or assurances from AWS, its affiliates, suppliers or licensors. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers. Contents Introduction 1 Amazon S3 as the Data Lake Storage Platform 2 Data Ingestion Methods 3 Amazon Kinesis Firehose 4 AWS Snowball 5 AWS Storage Gateway 5 Data Cataloging 6 Comprehensive Data Catalog 6 HCatalog with AWS Glue 7 Securing, Protecting, and Managing Data 8 Access Policy Options and AWS IAM 9 Data Encryption with Amazon S3 and AWS KMS 10 Protecting Data with Amazon S3 11 Managing Data with Object Tagging 12 Monitoring and Optimizing the Data Lake Environment 13 Data Lake Monitoring 13 Data Lake Optimization 15 Transforming Data Assets 18 In-Place Querying 19 Amazon Athena 20 Amazon Redshift Spectrum 20 The Broader Analytics Portfolio 21 Amazon EMR 21 Amazon Machine Learning 22 Amazon QuickSight 22 Amazon Rekognition 23 Future Proofing the Data Lake 23 Contributors 24 Document Revisions 24 Abstract Organizations are collecting and analyzing increasing amounts of data making it difficult for traditional on-premises solutions for data storage, data management, and analytics to keep pace. -
Getting Started with Microsoft Azure Virtual Machines
G “ M A V M Ιντροδυχτιον Ψου χαν υσε α Μιχροσοφτ Αζυρε ςιρτυαλ Μαχηινε ωηεν ψου νεεδ α σχαλαβλε, χλουδ−βασεδ σερϖερ ρυννινγ α Wινδοωσ ορ Λινυξ οπερατινγ σψστεm ανδ ανψ αππλιχατιον οφ ψουρ χηοοσινγ. Βψ τακινγ αδϖανταγε οφ Μιχροσοφτ Αζυρε Ινφραστρυχτυρε ασ α Σερϖιχε (ΙααΣ), ψου χαν ρυν α ϖιρτυαλ mαχηινε ον αν ονγοινγ βασισ, ορ ψου χαν στοπ ανδ ρεσταρτ ιτ λατερ ωιτη νο λοσσ το ψουρ δατα ορ σερϖερ σεττινγσ. Ψου χαν θυιχκλψ προϖισιον α νεω ϖιρτυαλ mαχηινε φροm ονε οφ τηε ιmαγεσ αϖαιλαβλε φροm Μιχροσοφτ Αζυρε. Ον τηε οτηερ ηανδ, ιφ ψου αρε αλρεαδψ ρυννινγ αππλιχατιονσ ιν α ςΜωαρε ορ Ηψπερ−ς ϖιρτυαλιζεδ ενϖιρονmεντ, ψου χαν εασιλψ mιγρατε ψουρ ϖιρτυαλ mαχηινε το Μιχροσοφτ Αζυρε. Ονχε ψου ηαϖε χρεατεδ ψουρ ϖιρτυαλ mαχηινε ανδ αδδεδ ιτ το Μιχροσοφτ Αζυρε, ψου χαν ωορκ ωιτη ιτ mυχη λικε αν ον−πρεmισεσ σερϖερ βψ ατταχηινγ mορε δισκσ φορ δατα στοραγε ορ βψ ινσταλλινγ ανδ ρυννινγ αππλιχατιονσ ον ιτ. ςιρτυαλ mαχηινεσ ρελψ ον Μιχροσοφτ Αζυρε Στοραγε φορ ηιγη αϖαιλαβιλιτψ. Wηεν ψουρ ϖιρτυαλ mαχηινε ισ προϖισιονεδ, ιτ ισ ρεπλιχατεδ το τηρεε σεπαρατε λοχατιονσ ωιτηιν τηε δατα χεντερ το ωηιχη ψου ασσιγν ιτ. Ψου ηαϖε τηε οπτιον το εναβλε γεο−ρεπλιχατιον το ηαϖε χοπιεσ οφ ψουρ ϖιρτυαλ mαχηινε αϖαιλαβλε ιν α ρεmοτε δατα χεντερ ρεγιον. Χονσιδερινγ Σχεναριοσ φορ α ςιρτυαλ Μαχηινε Μιχροσοφτ Αζυρε γιϖεσ ψου νοτ ονλψ τηε φλεξιβιλιτψ το συππορτ mανψ αππλιχατιον πλατφορmσ, βυτ αλσο τηε φλεξιβιλιτψ το σχαλε υπ ανδ σχαλε δοων το συιτ ψουρ ρεθυιρεmεντσ. Φυρτηερmορε, ψου χαν θυιχκλψ προϖισιον α νεω ϖιρτυαλ mαχηινε ιν α φεω mινυτεσ. Α Μιχροσοφτ Αζυρε ϖιρτυαλ mαχηινε ισ σιmπλψ α φρεση mαχηινε πρελοαδεδ ωιτη αν οπερατινγ σψστεm οφ ψουρ χηοιχεψου χαν αδδ ανψ νεεδεδ αππλιχατιον εασιλψ.