Software Equity Group's 2012 M&A Survey
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Google Is a Strong Performer in Enterprise Public Cloud Platforms Excerpted from the Forrester Wave™: Enterprise Public Cloud Platforms, Q4 2014 by John R
FOR CIOS DECEMBER 29, 2014 Google Is A Strong Performer In Enterprise Public Cloud Platforms Excerpted From The Forrester Wave™: Enterprise Public Cloud Platforms, Q4 2014 by John R. Rymer and James Staten with Peter Burris, Christopher Mines, and Dominique Whittaker GOOGLE, NOW A FULL-SERVICE PLATFORM, IS RUNNING TO CATCH THE LEADERS Since our last analysis, Google has made significant improvements to its cloud platform — adding an IaaS service, innovated with new big data solutions (based on its homegrown dremel architecture), and added partners. Google is popular among web developers — we estimate that it has between 10,000 and 99,000 customers. But Google Cloud Platform lacks several key certifications, monitoring and security controls, and application services important to CIOs and provided by AWS and Microsoft.1 Google has also been slow to position its cloud platform as the home for applications that want to leverage the broad set of Google services such as Android, AdSense, Search, Maps, and so many other technologies. Look for that to be a key focus in 2015, and for a faster cadence of new features. Forrester Wave™: Enterprise Public Cloud Forrester Wave™: Enterprise Public Cloud Platforms For CIOs, Q4 ‘14 Platforms For Rapid Developers, Q4 ‘14 Risky Strong Risky Strong Bets Contenders Performers Leaders Bets Contenders Performers Leaders Strong Strong Amazon Web Services MIOsoft Microsoft Salesforce Cordys* Mendix MIOsoft Salesforce (Q2 2013) OutSystems OutSystems Google Mendix Acquia Current Rackspace* IBM Current offering (Q2 2013) offering Cordys* (Q2 2013) Engine Yard Acquia CenturyLink Google, with a Forrester score of 2.35, is a Strong Performer in this Dimension Data GoGrid Forrester Wave. -
The Python/C API Release 3.6.0
The Python/C API Release 3.6.0 Guido van Rossum and the Python development team March 05, 2017 Python Software Foundation Email: [email protected] CONTENTS 1 Introduction 3 1.1 Include Files...............................................3 1.2 Objects, Types and Reference Counts..................................4 1.3 Exceptions................................................7 1.4 Embedding Python............................................9 1.5 Debugging Builds............................................ 10 2 Stable Application Binary Interface 11 3 The Very High Level Layer 13 4 Reference Counting 19 5 Exception Handling 21 5.1 Printing and clearing........................................... 21 5.2 Raising exceptions............................................ 22 5.3 Issuing warnings............................................. 24 5.4 Querying the error indicator....................................... 25 5.5 Signal Handling............................................. 26 5.6 Exception Classes............................................ 27 5.7 Exception Objects............................................ 27 5.8 Unicode Exception Objects....................................... 28 5.9 Recursion Control............................................ 29 5.10 Standard Exceptions........................................... 29 6 Utilities 33 6.1 Operating System Utilities........................................ 33 6.2 System Functions............................................. 34 6.3 Process Control.............................................. 35 -
Professional Heroku® Programming
ffirs.indd i 1/10/2013 1:46:29 PM PROFESSIONAL HEROKU® PROGRAMMING INTRODUCTION . xxv PART I AN OVERVIEW OF HEROKU CHAPTER 1 How Heroku Works . 3 CHAPTER 2 Architectural Considerations . .17 CHAPTER 3 Porting Your Applications to Heroku . 45 PART II CREATING AND MANAGING HEROKU APPLICATIONS CHAPTER 4 Deploying Your Applications to Heroku . 73 CHAPTER 5 Managing Releases with Heroku . 89 CHAPTER 6 Working with Add-Ons . 115 CHAPTER 7 Managing, Monitoring, and Scaling Your Heroku Applications . 133 PART III USING A DATA STORE WITH YOUR HEROKU APPLICATIONS CHAPTER 8 Using Heroku Postgres as a Data Store for Your Heroku Applications . 157 CHAPTER 9 Using Database.com as a Data Store for Your Heroku Applications . .181 CHAPTER 10 Using Third-Party Data Stores for Your Heroku Applications . .207 PART IV PROGRAMMING ON HEROKU’S POLYGLOT PLATFORM CHAPTER 11 Developing with Ruby . .233 CHAPTER 12 Developing with Java . 267 CHAPTER 13 Developing with Other Supported Languages . 301 CHAPTER 14 Understanding Buildpacks . 351 PART V CREATING A NEW GENERATION OF HEROKU APPLICATIONS CHAPTER 15 Building Mobile Applications with Heroku . 383 CHAPTER 16 Building Social Applications with Heroku . 411 APPENDIX Additional Resources . 445 INDEX . 469 ffirs.indd i 1/10/2013 1:46:29 PM ffirs.indd ii 1/10/2013 1:46:30 PM PROFESSIONAL Heroku® Programming ffirs.indd iii 1/10/2013 1:46:30 PM ffirs.indd iv 1/10/2013 1:46:30 PM PROFESSIONAL Heroku® Programming Chris Kemp Brad Gyger John Wiley & Sons, Ltd. ffirs.indd v 1/10/2013 1:46:30 PM © 2013 John Wiley & Sons, Ltd. -
M&A Transaction Spotlight
2012 AWARD WINNER: Capstone Partners GLOBAL INVESTMENT BANKING Investment Banking Advisors BOUTIQUE FIRM OF THE YEAR SaaS & Cloud M&A and Valuation Update Q2 2013 BOSTON CHICAGO LONDON LOS ANGELES PHILADELPHIA SAN DIEGO SILICON VALLEY Capstone Partners 1 CapstoneInvestment BankingPartners Advisors Investment Banking Advisors WORLD CLASS WALL STREET EXPERTISE.1 BUILT FOR THE MIDDLE MARKET.TM Table of Contents Section Page Introduction Research Coverage: SaaS & Cloud 4 Key Takeaways 5-6 M&A Activity & Multiples M&A Dollar Volume 8 M&A Transaction Volume 9-11 LTM Revenue Multiples 12-13 Highest Revenue Multiple Transactions for LTM 14 Notable M&A Transactions 15 Most Active Buyers 16-17 Public Company Valuation & Operating Metrics SaaS & Cloud 95 Public Company Universe 19-20 Recent IPOs 21-27 Stock Price Performance 28 LTM Revenue, EBITDA & P/E Multiples 29-31 Revenue, EBITDA and EPS Growth 32-34 Margin Analysis 35-36 Best / Worst Performers 37-38 Notable Transaction Profiles 40-49 Public Company Trading & Operating Metrics 51-55 Technology & Telecom Team 57-58 Capstone Partners 2 CapstoneInvestment BankingPartners Advisors Investment Banking Advisors 2 Capstone Partners Investment Banking Advisors Observations and Introduction Recommendations Capstone Partners 3 CapstoneInvestment BankingPartners Advisors Investment Banking Advisors WORLD CLASS WALL STREET EXPERTISE.3 BUILT FOR THE MIDDLE MARKET.TM Research Coverage: SaaS & Cloud Capstone’s Technology & Telecom Group focuses its research efforts on the following market -
Cloud Computing: a Taxonomy of Platform and Infrastructure-Level Offerings David Hilley College of Computing Georgia Institute of Technology
Cloud Computing: A Taxonomy of Platform and Infrastructure-level Offerings David Hilley College of Computing Georgia Institute of Technology April 2009 Cloud Computing: A Taxonomy of Platform and Infrastructure-level Offerings David Hilley 1 Introduction Cloud computing is a buzzword and umbrella term applied to several nascent trends in the turbulent landscape of information technology. Computing in the “cloud” alludes to ubiquitous and inexhaustible on-demand IT resources accessible through the Internet. Practically every new Internet-based service from Gmail [1] to Amazon Web Services [2] to Microsoft Online Services [3] to even Facebook [4] have been labeled “cloud” offerings, either officially or externally. Although cloud computing has garnered significant interest, factors such as unclear terminology, non-existent product “paper launches”, and opportunistic marketing have led to a significant lack of clarity surrounding discussions of cloud computing technology and products. The need for clarity is well-recognized within the industry [5] and by industry observers [6]. Perhaps more importantly, due to the relative infancy of the industry, currently-available product offerings are not standardized. Neither providers nor potential consumers really know what a “good” cloud computing product offering should look like and what classes of products are appropriate. Consequently, products are not easily comparable. The scope of various product offerings differ and overlap in complicated ways – for example, Ama- zon’s EC2 service [7] and Google’s App Engine [8] partially overlap in scope and applicability. EC2 is more flexible but also lower-level, while App Engine subsumes some functionality in Amazon Web Services suite of offerings [2] external to EC2. -
Is Paas Becoming Just a Feature of Iaas?
Is PaaS becoming just a feature of IaaS? Analyst: Jay Lyman 6 Jan, 2014 We've seen consistent M&A involving PaaS vendors over the past few years, driven primarily by polyglot programming and the demand among not only developers, but also IT operations teams – meaning devops customers as well – for multiple languages, frameworks, databases and other pieces. The reason is that today's market demands faster software development and deployment cycles and processes while maintaining quality and uptime, and this means developers, system administrators, productivity teams and business units need the best tools for the job. The challenge comes in supporting all of these pieces, most of which fluctuate in popularity over time and also attract newcomers. Some vendors and customers have touted PaaS as a way to support polyglot programming and we also see significant devops uses on both public and private PaaS. However, as PaaS continues to evolve, the newer trend in consolidation in the space is for larger IaaS providers to be acquiring PaaS vendors. Analysis from ChangeWave, a service of 451 Research, also indicates that while IaaS and PaaS continue to grow in popularity and use, there is greater growth in SaaS. Given we're also seeing more consolidated offerings of IaaS and PaaS by the likes of Amazon, Google, Microsoft, Red Hat and VMware, it is clear that PaaS is facing pressure and potentially being supplanted from below in the IT software stack by IaaS and from above by SaaS. This is not to say PaaS as a set of capabilities and features or as a market will simply vanish, but it does highlight how the definition of PaaS is changing, particularly in the context of IaaS and SaaS. -
Dynamic Resource Allocation for Overload Avoidance and Green Cloud Computing
Dynamic Resource Allocation for Overload Avoidance and Green Cloud Computing {tag} {/tag} International Journal of Computer Applications Foundation of Computer Science (FCS), NY, USA Volume 126 - Number 12 Year of Publication: 2015 Authors: Saima Israil, Rajeev Pandey, Uday Chourasia 10.5120/ijca2015906234 {bibtex}2015906234.bib{/bibtex} Abstract Cloud Computing is a flourishing technology because of its scalability, flexibility, availability of resources and other features. Resource multiplexing is done through the virtualization technology in cloud computing. Virtualization technology acts as a backbone for provisioning requirements of the cloud based solutions. At present, load balancing is one of the challenging issues in cloud computing environment. This issue arises due to massive consumer demands variety of services as per their dynamically changing requirements. So it becomes liability of cloud service provide to facilitate all the demanded services to the cloud consumers. However, due to the availability of finite resources, it is very challenging for cloud service providers to facilitate all the demanded services efficiently. From the cloud service provider’s perspective, cloud resources must be allocated in a fair manner. This paper addresses the existing techniques for resource allocation in cloud computing and proposes the dynamic resource allocation technique to mitigate overloads. It also focuses on energy consumption issue of cloud data centres and devised technique for lower energy consumption in order to achieve green cloud computing. 1 / 3 Dynamic Resource Allocation for Overload Avoidance and Green Cloud Computing References 1. Rimal, B.P., Choi, E., Lumb, I., 2009, A Taxonomy and Survey of Cloud Computing Systems, Proceeding of the Fifth International Joint Conference on INC, IMS and IDC, pp. -
Cloud Computing Oct 7 2008
CS 683 Emerging Technologies Fall Semester, 2008 Doc 10 Cloud Computing Oct 7 2008 Copyright ©, All rights reserved. 2008 SDSU & Roger Whitney, 5500 Campanile Drive, San Diego, CA 92182-7700 USA. OpenContent (http:// www.opencontent.org/openpub/) license defines the copyright on this document. References Amazon Simple Storage Service Getting Started Guide, http://docs.amazonwebservices.com/ AmazonS3/2006-03-01/gsg/ Amazon Simple Storage Service, http://aws.amazon.com/s3/ Twenty-One Experts Define Cloud Computing, http://cloudcomputing.sys-con.com/node/612375/ print Cloud Computing Community Wiki, http://wiki.cloudcommunity.org/wiki/Main_Page Cloud computing, http://en.wikipedia.org/wiki/Cloud_computing Reading Cloud computing, http://en.wikipedia.org/wiki/Cloud_computing Amazon Simple Storage Service Getting Started Guide, http://docs.amazonwebservices.com/ AmazonS3/2006-03-01/gsg/ 2 Cloud Computing 3 Examples Google Apps iTunes Store Bittorent Skype Web mail Facebook Google Maps 4 In the Beginning "computation may someday be organized as a public utility" John McCarthy 1960 5 Wikipedia Definition IT-related capabilities are provided “as a service” Services accesses anywhere via network access IEEE It is a paradigm in which information is permanently stored in servers on the Internet and cached temporarily on clients that include desktops, entertainment centers, table computers, notebooks, wall computers, handhelds, etc. 6 Key Characteristics Capital expenditure minimized for users Device and location independence Performance Reliability -
Introduction to Cloud Computing
Introduction to Cloud Computing Business & Technology Timothy Chou © 2010 by Active Book Press 2nd Edition All rights reserved. ,6%1 Printed in the United States of America Contents ACKNOWLEDGEMENTS..........................................................................V INTRODUCTION.................................................................................... VIII SEVEN BUSINESS MODELS......................................................................1 MODEL ONE: TRADITIONAL.........................................................................3 MODEL TWO: OPEN SOURCE........................................................................5 MODEL THREE: OUTSOURCING....................................................................7 MODEL FOUR: HYBRID. ...............................................................................9 MODEL FIVE: HYBRID+ .............................................................................14 MODEL SIX: SOFTWARE AS A SERVICE ......................................................15 MODEL SEVEN: INTERNET .........................................................................17 COST OF SOFTWARE...................................................................................18 SUMMARY..................................................................................................22 APPLICATION CLOUD SERVICES .......................................................24 COLLABORATION .......................................................................................25 WEBEX STORY .............................................................................................26 -
Public Cloud – Solutions & Service Partners
Public Cloud – Solutions & A research report comparing provider Service Partners strengths, challenges Germany 2019 and competitive differentiators Quadrant Report November 2019 ISG Provider Lens™ Quadrant Report | November 2019 Section Name About this Report Information Services Group Inc. is solely responsible for the content of this report. ISG Provider Lens™ delivers leading-edge and actionable research studies, reports Unless otherwise cited, all content, including illustrations, research, conclusions, and consulting services focused on technology and service providers’ strengths and assertions and positions contained in this report were developed by, and are the sole weaknesses and how they are positioned relative to their peers in the market. These property of Information Services Group Inc. reports provide influential insights accessed by our large pool of advisors who are The research and analysis presented in this report includes research from the ISG actively advising outsourcing deals as well as large numbers of ISG enterprise clients Provider Lens™ program, ongoing ISG Research programs, interviews with ISG advisors, who are potential outsourcers. briefings with services providers and analysis of publicly available market information from multiple sources. The data collected for this report represents information that For more information about our studies, please email [email protected], ISG believes to be current as of August 2019 for providers who actively participated and call +49 (0) 561-50697537, or visit ISG Provider Lens™ under ISG Provider Lens™. for providers who did not. ISG recognizes that many mergers and acquisitions have taken place since that time, but those changes are not reflected in this report. All revenue references are in U.S. -
Scientific Computing with MATLAB
Scientific Computing with MATLAB Paul Gribble Fall, 2015 Tue 12th Jan, 2016, 06:25 ii About the author Paul Gribble is a Professor at the University of Western Ontario1 in London, On- tario Canada. He has a joint appointment in the Department of Psychology2 and the Department of Physiology & Pharmacology3 in the Schulich School of Medicine & Dentistry4. He is a core member of the Brain and Mind Institute5 and a core member of the Graduate Program in Neuroscience6. He received his PhD from McGill University7 in 1999. His lab webpage can be found here: http://www.gribblelab.org This document This document was typeset using LATEX version 3.14159265-2.6-1.40.16 (TeX Live 2015). These notes in their entirety, including the LATEX source, are avail- able on GitHub8 here: https://github.com/paulgribble/SciComp License This work is licensed under a Creative Commons Attribution 4.0 International License9. The full text of the license can be viewed here: http://creativecommons.org/licenses/by/4.0/legalcode iii LINKS Links 1http://www.uwo.ca 2http://psychology.uwo.ca 3http://www.schulich.uwo.ca/physpharm/ 4http://www.schulich.uwo.ca 5http://www.uwo.ca/bmi 6http://www.schulich.uwo.ca/neuroscience/ 7http://www.mcgill.ca 8https://github.com 9http://creativecommons.org/licenses/by/4.0/ LINKS iv Preface These are the course notes associated with my graduate-level course called Sci- entific Computing (Psychology 9040a), given inthe Department of Psychol- ogy10 at the University of Western Ontario11. The goal of the course is to provide you with skills in scientific computing: tools and techniques that you can use in your own scientific research. -
Who's Behind ICE: Tech and Data Companies Fueling Deportations
Who’s Behind ICE? The Tech Companies Fueling Deportations Tech is transforming immigration enforcement. As advocates have known for some time, the immigration and criminal justice systems have powerful allies in Silicon Valley and Congress, with technology companies playing an increasingly central role in facilitating the expansion and acceleration of arrests, detentions, and deportations. What is less known outside of Silicon Valley is the long history of the technology industry’s “revolving door” relationship with federal agencies, how the technology industry and its products and services are now actually circumventing city- and state-level protections for vulnerable communities, and what we can do to expose and hold these actors accountable. Mijente, the National Immigration Project, and the Immigrant Defense Project — immigration and Latinx-focused organizations working at the intersection of new technology, policing, and immigration — commissioned Empower LLC to undertake critical research about the multi-layered technology infrastructure behind the accelerated and expansive immigration enforcement we’re seeing today, and the companies that are behind it. The report opens a window into the Department of Homeland Security’s (DHS) plans for immigration policing through a scheme of tech and database policing, the mass scale and scope of the tech-based systems, the contracts that support it, and the connections between Washington, D.C., and Silicon Valley. It surveys and investigates the key contracts that technology companies have with DHS, particularly within Immigration and Customs Enforcement (ICE), and their success in signing new contracts through intensive and expensive lobbying. Targeting Immigrants is Big Business Immigrant communities and overpoliced communities now face unprecedented levels of surveillance, detention and deportation under President Trump, Attorney General Jeff Sessions, DHS, and its sub-agency ICE.