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Killexams : IBM SmartCloud test contents - BingNews https://killexams.com/pass4sure/exam-detail/000-N52 Search results Killexams : IBM SmartCloud test contents - BingNews https://killexams.com/pass4sure/exam-detail/000-N52 https://killexams.com/exam_list/IBM Killexams : Teamcenter PLM Meets Up With the Cloud

It's time to count another major PLM provider in the camp migrating their platforms to the cloud.

Joining rivals Dassault Systemes and Autodesk, as well as the lesser-known Aras Software and Arena Solutions, Siemens PLM Software will now offer a cloud-based delivery option for its Teamcenter PLM platform. The Teamcenter on the cloud offering will be delivered through what's called an infrastructure-as-a-service (IaaS) model through three of the leading cloud services: Microsoft Windows Azure, IBM SmartCloud Enterprise+, and Amazon Web Services.

Unlike the more familiar software-a-as-service (SaaS), where applications can be "rented" almost like a utility with a low, pay-as-you-go pricing model and the applications are administered and controlled by the software provider, IaaS is basically a cloud version of the old hosting model. With this approach, a company outsources the application to run on the storage, servers, and networks of a cloud provider, but it retains responsibility for care and feeding of the application.

The idea here is that Teamcenter shops can scale the infrastructure for running PLM up and down based on their project needs without having to make significant upfront investments in the additional computing resources. Teamcenter on the cloud also enables customers to focus their IT resources on higher value-added services like provider and partner on-boarding as opposed to the daily administration and management of the PLM platform, since, in theory, that effort would be centralized.

"The key benefit of Teamcenter on the cloud is the business flexibility it provides," said Eric Sterling, senior vice president, Lifecycle Collaboration Software, Siemens PLM Software, in a press release announcing the product. "In today's ever-changing global landscape, the flexibility to dynamically manage infrastructure on the cloud gives customers the ability to scale up computing resources with demand and more importantly, scale down costs if demand increases."

With this announcement, Siemens PLM Software is not trading the traditional Teamcenter model for a cloud-only delivery model. Rather, the move is designed to extend its "platform of choice" strategy, officials said, and fits in with one of its core tenets -- to deliver products based on a future-proof architecture.

Related posts:

Sun, 17 Jul 2022 11:59:00 -0500 en text/html https://www.designnews.com/design-hardware-software/teamcenter-plm-meets-cloud
Killexams : Cloud-based Information Governance Market Located Worldwide Trends and Application – EMC, HP Autonomy, IBM, Symantec

The MarketWatch News Department was not involved in the creation of this content.

Aug 05, 2022 (Market Insight Reports) -- Overview of the Global Cloud-based Information Governance Market:

The Cloud-based Information Governance Market Report 2022 report provides the latest industry data and future industry trends. The report lists leading competitors and manufacturers in the Cloud-based Information Governance industry and provides strategic industry insights and analysis of factors influencing the competitiveness of the market. The geographical scope of the Cloud-based Information Governance market is studied. The forecast market information, SWOT analysis, market scenario, and feasibility study are the vital aspects analyzed in this report.

Looking forward, Market Intelligence Data Group expects the market to grow at a CAGR of 12.6% during 2022-2028.

Request trial Copy of this Report:


Leading Players in the Cloud-based Information Governance Market- EMC, HP Autonomy, IBM, Symantec, AccessData, Amazon, BIA, Catalyst, Cicayda, Daegis, Deloitte, Ernst & Young, FTI, Gimmal, Google, Guidance Software, Index Engines, Iron Mountain, Konica Minolta, Kroll Ontrak, Microsoft, Mimecast, Mitratech, Proofpoint, R and other.

The leading players of the Cloud-based Information Governance industry, their market share, product portfolio, company profiles are covered in this report. The leading market players are analyzed based on production volume, gross margin, market value, and price structure. The competitive market scenario among Cloud-based Information Governance players will help the industry aspirants in planning their strategies. The statistics offered in this report will be a precise and useful guide to shape business growth.

Global Cloud-based Information Governance Market Segmentation:

Market Segmentation: By Application





IT And Telecom



Market Segmentation: By Type

Simple Storage And Retrieval

Basic Document Management

Complex Document Management

Functional Applications With Document Storage

Social Networking Applications With Document Storage

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Regional and Country-level Analysis:

The key regions covered in the Cloud-based Information Governance market report are North America,

Europe, Asia Pacific, Latin America, Middle East and Africa. It also covers

key regions (countries), viz, U.S., Canada, Germany, France, U.K., Italy,

Russia, China, Japan, South Korea, India, Australia, Taiwan, Indonesia,

Thailand, Malaysia, Philippines, Vietnam, Mexico, Brazil, Turkey, Saudi Arabia,

U.A.E, etc.

Key questions answered in the report include:

  1. What will the market size and the growth rate be in 2028?
  2. What are the key factors driving the Global Cloud-based Information Governance Market?
  3. What are the key market trends impacting the growth of the Global Cloud-based Information Governance Market?
  4. What are the challenges to market growth?
  5. Who are the key vendors in the Global Cloud-based Information Governance Market?
  6. What are the market opportunities and threats faced by the vendors in the Global Cloud-based Information Governance Market?
  7. Trending factors influencing the market shares of the Americas, APAC, Europe, and MEA.

Explore Full Report With Detailed TOC Here:


Crucial Elements from the Table of Contents of Global Cloud-based Information Governance Market:

– Cloud-based Information Governance Market Overview

– Global Cloud-based Information Governance Market Competition, Profiles/Analysis, Strategies

– Global Cloud-based Information Governance Capacity, Production, Revenue (Value) by Region (2016-2022)

– Global Cloud-based Information Governance Supply (Production), Consumption, Export, Import by Region (2016-2022)

– Global Cloud-based Information Governance Market Regional Highlights

– Industrial Chain, Sourcing Strategy, and Downstream Buyers

– Marketing Strategy Analysis, Distributors/Traders

– Market Effect Factors Analysis

– Market Decisions for the present scenario

– Global Cloud-based Information Governance Market Forecast (2022-2028)

– Case Studies

– Research Findings and Conclusion

Finally, the Cloud-based Information Governance Market report is the believable source for gaining the market research that will exponentially accelerate your business. The report gives the principle locale, economic situations with the item value, benefit, limit, generation, supply, request, and market development rate and figure, and so on. The Cloud-based Information Governance industry report additionally presents a new task SWOT examination, speculation attainability investigation, and venture return investigation.

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Fri, 05 Aug 2022 01:01:00 -0500 en-US text/html https://www.marketwatch.com/press-release/cloud-based-information-governance-market-located-worldwide-trends-and-application-emc-hp-autonomy-ibm-symantec-2022-08-05
Killexams : DevOps in AWS LiveLessons Review and Q&A with Author Paul Duvall

Devops in AWS LiveLessons, published by Addison-Wesley Professional, is a video course aimed at infrastructure developers and Sys Ops engineers who have a goal of creating a fully-automated continuous delivery system in Amazon Web Services (AWS). The 4+ hour course is especially focused on leveraging key features of AWS, such as programmable infrastructure, elasticity and ephemeral resources, while also presenting them in the framework of DevOps best practices and tools. InfoQ has spoken with course author Paul Duvall.

The course sets off by considering the importance of learning the motivation of stakeholders and putting in place the proper communication tools to get access to their assets. Once this is accomplished, according to Duvall, the next step is assessing the current state of the software delivery system. This entails documenting all steps of the current software delivery process as a pre-requisite to its final automation.

The rest of the course is devoted to a very thourough demonstration of all the steps that are required to setup a CI infrastructure in AWS, from the creation of network resources using AWS Virtual Private Cloud and the definition of subnets, route table, security groups and so on, to the set up of a proper CI pipeline across all of its stages. For each stage in the CI pipeline-commit, acceptance, capacity, exploratory, pre-production, and production-Duvall explains what its purpose is and what role it plays.

The final lessons address how to automate the process itself of setting up a new environment and deployment system; processes that are not specific to any given stage in the pipeline, such as monitoring and logging; and ongoing activities that can be helpful for the whole team.

InfoQ has interviewed Paul Duvall.

InfoQ: Hi Paul. Could you please shortly introduce yourself and describe your experience with Continuous Delivery?

I’m the co-founder of Stelligent and the primary author of the book on Continuous Integration. Our business objective at Stelligent is to implement Continuous Delivery solutions in Amazon Web Services for our customers so that they can release software whenever they choose to do so. I’ve been practicing Continuous Integration since the early 2000s on software development projects and began practicing what is now referred to as Continuous Delivery a few years after that. Like most things, it’s been a bit of an evolution toward more self-service, more automation and a better user experience for those who are developing and delivering software systems.

I’ve got a particular passion for automating repetitive and error-prone processes so that we humans can focus on higher-level activities. I find that the more I ask myself the question, “how can I systematize this process and make it self-service?”, the more I test the limits of what’s possible with automation. I believe we’re still at “Day 1” when it comes to automation around the software delivery process.

InfoQ: Can you briefly define Continuous Delivery and what benefits it can bring to an organization?

CD provides the benefits of having always-releasable software so decisions to release software are driven by business needs rather than operational constraints. Moreover, CD reduces the time between when a defect is introduced and when it’s discovered and fixed. CD embraces the DevOps mindset which, at its core, is about increasing collaboration and communication across an organization and reducing team silos that stifle this collaboration.

InfoQ: When should an organization adopt a continuous delivery model?

Organizations should implement a continuous delivery model when they have a need to regularly release software and/or they want to reduce the heroic efforts when they do release software. Releasing software less tends to make the release process more complex and prone to error, which calls for heroic efforts that can often otherwise be avoided. In other words, even if there isn’t a business need to release once a month, there’s often a compelling cost and quality of life motivation to move toward Continuous Delivery regardless of how often releases occur.

Organizations that haven’t incorporated Continuous Delivery and practices into their teams usually experience at least some of the following symptoms:

  • Team Silos - Teams segmented by functional area
  • Manual Instructions - Using manual instructions as the canonical source for infrastructure and other configuration
  • Tribal Knowledge - Knowledge is shared from one person to others and not formally institutionalized
  • Email - Managing release activities through emails
  • Different Tools Across Teams: Different teams across the lifecycle use different tools to deliver software
  • Issues/Tickets - Using issues/tickets as a means of communicating and assigning build, test, deployment and release-related tasks
  • Meetings - Meetings are used as a weak attempt to get different teams on the “same page”

These Symptoms Often Generate These Results:

  • Errors - When a full system isn’t integrated frequently, environments throughout the lifecycle are different, often leading to errors
  • Increased costs - Errors lead to increased costs
  • Delays - Weeks to get access to even just development and testing environments; Increased wait times as teams attempt to communicate across team silos
  • Release less frequently - Release during off-hours, weekends, calling for hero efforts

InfoQ: How would you describe the kind of mentality change that should go along with adopting a CD system?

On an individual level, people need to be moving away from the “it’s not my job” mentality and move toward a “systems thinking” mentality. People should be continually asking themselves “how will this change affect the rest of the system?” This kind of systems thinking should manifest into more holistic thinking for the benefit of the overall system.

When I refer to the whole team, I mean a cross-functional team that consists of people who are 100% dedicated to the project. This team’s external team dependencies should be minimal and they should have the ability to release the software whenever there’s a business decision to do so without going through a separate team.

When I refer to the whole process, I’m referring to a heuristic that we’ve found to work well when thinking about “systematizing” a process. This heuristic is: document, test, code, version and continuous. This translates to:

  1. Document - Document the process with the idea that you will automate away most of the steps you’re documenting. We refer to this as “document to automate”.
  2. Test - Write automated tests that will verify that the behavior you’re automating is working.
  3. Code - Codify the behavior based on the tests and/or documentation.
  4. Version - Commit the code and tests to the version-control repository.
  5. Continuous - Once the code and tests are versioned, ensure it can be run headless (e.g. a single command taking into account any necessary dependencies) and then configured to run as part of a single path to production through a continuous integration system.

When people think in terms of the whole system, they need to extend beyond just the application/service code, and include the configuration, infrastructure, data and all the supporting and dependent resources such as build, tests, deployments, binaries, etc. All of these components need to be documented, tested, codified, versioned and run continuously with each and every commit.

InfoQ: In your LiveLessons, you stress the importance of a number of initial steps, such as identifying all stakeholders, performing a discovery process, setting up communication management tools, moving to a cross-functional organization and so on. Could you elaborate on the importance of those steps?

As an engineer, it’d be easy for me to gloss over these types of activities for the more interesting hands-on coding exercises. While other lessons in the video do focus on the hands-on coding, I was seeking to show viewers all of the steps that we typically go through at Stelligent when implementing CD with a customer. For example, we find that when teams don’t take the time to determine the current state of their processes, they perform unnecessary sub-optimization on things that don’t provide the most benefit.

For instance, let’s say your process from code commit to production takes three months and there are multiple days just waiting for people downstream to just click or approve something, why would you first spend days or weeks optimizing the process time of the one of the steps in your delivery process? Instead, you might spend some time determining why there are multi-day bottlenecks.

This is why it’s essential to do things like value-stream mapping so that everyone gets on the same page about the current state (either on the software project under development or other projects in the organization if it’s a new development effort) so that you’re spending time on optimizing the most critical bottlenecks first.

InfoQ: Among all the steps you go through to set up a CD solution within an organization, what are the critical ones in order to get CD right?

  • Value-stream mapping - Create a left-to-right illustration of the current state containing all the steps in your process including the overall lead time, process times and wait times. Illustrate the anticipated future state.
  • Self Service - create fully-automated pull-based systems so that any authorized team member can get environments, tools or other resources without human intervention.
  • Cross-functional teams - for example: application developers, testing/QA, DBAs, operations, business analysts as part of the same small team
  • Feedback - get the right information to the right people at the right time. This may include real-time information radiators, emails and other communication mechanisms. Instill “stopping the line” into the team culture so that errors are fixed soon after they’re introduced.

InfoQ: You focus on AWS in your LiveLessons. What is the advantage of using it to implement an organization’s CD system?

At Stelligent, we’ve focused exclusively on building CI/CD solutions and have been working in the cloud since 2009. We’d worked with multiple cloud providers in the first year or so. For one customer, we used and analyzed something like 15 cloud providers, at the time. We determined that AWS was the most feature-rich, stable, cost-effective solution for their needs and we decided that it was the best solution for our needs.

InfoQ: How would AWS stack up in a hypothetical Continuous Delivery Contest against its main competitors, such as Microsoft Azure, IBM SmartCloud, Google Cloud Platform, and so on?

For what it’s worth, I’d include the Google Cloud Platform before I’d include the IBM SmartCloud. AWS is far ahead of any of the other infrastructure-as-a-service providers on the market. For example, there’s no real equivalent to AWS OpsWorks - particularly in the context of an integrated IaaS provider. Because AWS has a large suite of services, it provides and then exposes them through AWS CloudFormation, and the number of resources you can automatically provision is much more than any other provider.

Moreover, there are no genuinely comparable solutions to the new Continuous Delivery-focused services at AWS such as AWS CodeDeploy, AWS CodePipeline and AWS CodeCommit and several other enterprise-focused services.

Paul M. Duvall is the Chairman and CTO at Stelligent. Stelligent is an expert in implementing Continuous Delivery solutions in Amazon Web Services (AWS) and has been working with AWS since 2009. Paul is the principal author of Continuous Integration: Improving Software Quality and Reducing Risk (Addison-Wesley, 2007) and a 2008 Jolt Award Winner. Paul is an author of many other books and publications including DevOps in AWS (Addison-Wesley, 2014) and two IBM developerWorks series on Topics around automation, DevOps and Cloud Computing. He is passionate about software delivery and the cloud and actively blogs here.

Thu, 11 Nov 2021 09:22:00 -0600 en text/html https://www.infoq.com/articles/devops-aws-livelessons-review/
Killexams : Machine Learning In Utilities Market To Witness Widespread Expansion,Rising Business Opportunities With Prominent Investment,Forecast To 2030

Machine Learning in Utilities Market: A thorough analysis of statistics about the current as well as emerging trends offers clarity regarding the Machine Learning in Utilities Market dynamics. The report includes Porter’s Five Forces to analyze the prominence of various features such as the understanding of both the suppliers and customers, risks posed by various agents, the strength of competition, and promising emerging businesspersons to understand a valuable resource. Also, the report spans the Machine Learning in utility research data of various companies, benefits, gross margin, strategic decisions of the worldwide market, and more through tables, charts, and infographics.

The Machine Learning in Utilities Market report highlights an all-inclusive assessment of the revenue generated by the various segments across different regions for the forecast period, 2022 to 2030. To leverage business owners, and gain a thorough understanding of the current momentum, the research taps hard-to-find data on aspects including but not limited to demand and supply, distribution channels, and technology upgrades. Principally, the determination of strict government policies and regulations and government initiatives building the growth of Machine Learning in the utility market offers knowledge of what is in store for the business owners in the upcoming years.

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Promising Regions & Countries Mentioned In Machine Learning in Utilities Market Report:

‣ North America ( United States)
‣ Europe ( Germany, France, UK)
‣ Asia-Pacific ( China, Japan, India)
‣ Latin America ( Brazil)

The report studies Machine Learning in the utility market by evaluating the market chain, prevalent policies, and regulations as well as the manufacturers, their manufacturing chain, cost structures, and contribution to the industry. The regional markets in the report are examined by analyzing the pricing of products in the region compared to the profit generated. The production capacity, demand and supply, logistics, and the historical performance of the market in the given region are also evaluated in this market report.

Top Companies Covered In This Report:

Baidu, Hewlett Packard Enterprise Development LP, SAS Institute, Inc., IBM, Microsoft, Nvidia, Amazon Web Services, Oracle, SAP, BigML, Inc., Fair Isaac Corporation, Intel Corporation, Google LLC, H2o.AI, Alpiq, SmartCloud

By the product type, the market is primarily split into:

Hardware, Software, Service

By the application, this report covers the following segments:

Renewable Energy Management, Demand Forecast, Safety, and Security, Infrastructure

Analysis of the market:

Other important factors studied in this report include demand and supply dynamics, industry processes, import & export scenarios, R&D development activities, and cost structures. Besides, consumption demand and supply figures, cost of production, gross profit margins, and selling price of products are also estimated in this report.

The conclusion part of their report focuses on the existing competitive analysis of the market. We have added some useful insights for both industries and clients. All leading manufacturers included in this report take care of expanding operations in regions. Here, we express our acknowledgment for the support and assistance from the News Apps industry experts and publicizing engineers as well as the examination group’s survey and conventions. Market rate, volume, income, demand, and supply data are also examined.

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Detailed analysis of the Global Machine Learning in Utilities Market
✔ Fluctuating market dynamics of the industry
✔ Detailed market segmentation
✔ Historical, current, and projected market size in terms of volume and value
✔ recent industry trends and developments
✔ Competitive landscape of the Global Machine Learning in Utilities Market
✔ Strategies of key players and product offerings
✔ Potential and niche segments/regions exhibiting promising growth
✔ A neutral perspective toward Global Machine Learning in utility Market performance

𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐌𝐞𝐭𝐡𝐨𝐝𝐨𝐥𝐨𝐠𝐲

The report has its roots definitely set in thorough strategies provided by proficient data analysts. The research methodology involves the collection of information by analysts only to have them studied and filtered thoroughly in an attempt to provide significant predictions about the market over the review period. The research process further includes interviews with leading market influencers, which makes the primary research relevant and practical. The secondary method gives a direct peek into the demand and supply connection. The market methodologies adopted in the report offer precise data analysis and provide a tour of the entire market. Both primary and secondary approaches to data collection have been used. In addition to these, publicly available sources such as annual reports, and white papers have been used by data analysts for an insightful understanding of the market.

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Table of Contents:

1. Introduction
2. Key Takeaways
3. Research Methodology
4. Machine Learning in Utilities Landscape
5. Key Market Dynamics
6. Machine Learning in Utilities Market – Global Market Analysis
7. Revenue and Forecasts to 2030 – Segmentation
8. Geographical Analysis
9. Industry Landscape
10. Machine Learning in Utilities Market, Key Company Profiles
11. Appendix

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Tue, 19 Jul 2022 22:46:00 -0500 Coherent Market Insights en-US text/html https://www.digitaljournal.com/pr/machine-learning-in-utilities-market-to-witness-widespread-expansionrising-business-opportunities-with-prominent-investmentforecast-to-2030
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