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000-252 Web Sphere Application Server Network Deployment V6.0, Core Administration

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000-252 exam Dumps Source : Web Sphere Application Server Network Deployment V6.0, Core Administration

Test Code : 000-252
Test Name : Web Sphere Application Server Network Deployment V6.0, Core Administration
Vendor Name : IBM
: 109 Real Questions

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IBM Web Sphere Application Server

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February 4, 2019 Timothy Prickett Morgan

there are lots of issues that one could constructively criticize IBM about when it involves the energy programs platform working the IBM i operating device. however, in fresh years at least, one of those issues would now not be – and could now not be – that the company has now not achieved satisfactory to embrace the most important elements of the contemporary programming toolbox.

in reality, the company has accomplished and more and more respectable job of embracing and extending the compilers, interpreters, frameworks, and fashions of the programming languages which have long gone mainstream for the reason that Java first took the stage initially of the dot-com growth in 1996 as an try to bring a simplified variant of C++, dwelling in an idealized, simplified, and importantly transportable hardware abstraction known as a digital desktop. considering the fact that that point, big Blue and the IBM i community have worked collectively, often with the community out in entrance with the active involvement of the IBMers within the developerWorks company, to bring the Apache internet server and the Tomcat utility server the OS/400, i5/OS and IBM i platform together with programming languages reminiscent of Perl, personal home page, Python, and Node.js, and versioning tools equivalent to Git, and other programs application that are are simple tools for contemporary programmers.

The 2019 version of the IBM i market Survey, which became released in January and which is spearheaded through Tom Huntington, government vice chairman of technical options at HelpSystems, provided some insight into what is happening obtainable on the programming entrance. The survey was carried out closing October and had 700 respondents, with fifty seven p.c coming from the U.S. but best 2 p.c from Canada; another 19 percent came from Europe, 15 percent came from Latin the us, and the the rest changed into break up throughout Australia, Asia, and Africa. As they now have cited earlier than, here is no longer precisely consultant of the exact IBM i installed base, which is more closely distributed outdoor of the us; but the numbers are step by step moving in the appropriate path as HelpSystems interprets the survey into extra languages and agencies such as it Jungle encourage readers to take the survey.

The diversity of operating methods supported on the vigour methods platform and the potential to snap in equipment created for Unix-like working methods into the PASE AIX runtime embedded in OS/400 and its successors is a key aspect to the potential to get and use equipment that other systems have. (in this manner, IBM preserves its own merits such as RPG company language programming and an built-in relational database administration equipment and grafts on the benefits of alternative platforms, giving it a form of hybrid energy.) And IBM i retail outlets were embracing that operating system range on the platform and becoming much less based on windows Server machines operating on X86 iron elsewhere within the datacenter, as which you could see from this working device distribution from the survey:


simplest 22 percent of the organizations surveyed talked about that they had been best operating the IBM i platform, and admittedly, they locate it challenging to trust that this number is not zero percent given how pervasive windows file and print servers are. nonetheless it is feasible to move it alone with IBM i, possibly in factory or far off retail settings, and it's possible so they are able to take the survey respondents at face price. what is exciting in this chart is that the share of home windows Server is falling. again in the day, someplace north of ninety five percent of IBM midrange stores pronounced the use of windows Server alongside their OS/four hundred and i5/OS machines, and this has been trending down as Linux, both on power methods iron inside of PowerVM logical partitions and on outboard X86 techniques, is on the upward thrust. AIX has won some penetration at IBM i shops, too, each on distinct vigour techniques equipment and on PowerVM partitions on the same machines working IBM i.

As you could think about, RPG remains essentially the most universal programming language in use at IBM i shops, with over eighty four percent of respondents asserting they use RPG to create functions (or bought RPG applications from third birthday celebration utility carriers that do). The SQL database programming language ranks quantity two, at 72 %, adopted by using the CL scripting language native to IBM at 47 % and Java at forty one percent. right here’s the rundown of programming language adoption:

There are a few enjoyable issues occurring here. First, whereas sixty eight % of retailers record the usage of windows Server of their IBM i retail outlets, most effective 17 p.c say they're the usage of the .internet framework from Microsoft for programming, which supports visible simple and the Java-inspired (that’s the best manner to say that) C# languages. they would have concept that after two and a half many years, Java would have been up there with RPG and SQL as a favored language, however that has no longer took place. The share of COBOL is about the ancient style and tends to be focused in the fiscal services and insurance industries the place code become ported from mainframes so many many years in the past.

agencies had been definitely allowed to position down more than one programming language as a part of the survey, and if you add up all the percentages, you get 320 %, which implies an arithmetic imply of three.2 languages in use per web site on usual across the IBM i put in base. (smartly, at least this upper echelon – by which they suggest the lively portion, no longer the largest corporations or those with the greatest machines – a part of the IBM i base. They don’t know anything concerning the americans who don’t take surveys and they suppose that the records that changed into gathered for the 5 years of the IBM i marketplace Survey are consultant of these energetic shops. That skill people who keep their techniques and working methods tremendously latest and that are typically extra inclined to adopt newer applied sciences, starting from storage enviornment networks to high availability clustering to superior programming thoughts.) we're of two minds about this average. First, RPG, SQL, and CL dominate, and mixed they provide you with a typical of two languages. They think there are lots of websites that in particular use RPG and SQL, specially those who have third celebration application utility. it's vital to recognise that the survey question turned into no longer what languages had been used to create your purposes, but quite what languages are used for brand new utility building. So the base might have much more distinct and a stronger numerical distribution of compilers and interpreters than this facts implies. I don’t suppose this is the case, however it doubtless averages greater like five languages as a result of whether they comprehend it or now not, there’s likely some SQL, CL, or Java running somewhere in the utility stack.

The survey asked agencies about what open supply technologies they have deployed of their IBM midrange retail outlets, and accept as true with it or no longer the Apache web server, which IBM first commercialized because the kernel of WebSphere again in 1998, remains on the upward push and that's likely going on as fewer and fewer retail outlets use windows Server for software and internet serving and greater retail outlets go native. That the native Apache internet server has handiest 40 % penetration tells you what these home windows Server that stay are doing. it's a mix of print, file, web, and application serving plus a healthy dose of SQL Server for analytical database processing that corporations don’t want to deploy natively on their IBM i machines. Python and Node.js are on a gradual upward thrust, and the Git versioning tool is discovering its own degree, too.

What they can’t understand from the first five surveys and what they would like to recognize in the sixth next 12 months is how many of RPG is being coded in the up to date free kind trend that is greater like what Java does and what percentage is still the use of the older college, extra tightly constrained older RPG mannequin. My bet is that for brand spanking new code, it is doubtless as excessive as half and half, and that is mirrored in the indisputable fact that about have of the agencies surveyed pointed out that they'd purchased Rational Developer for i and were the usage of it for as a minimum some utility coding. About a 3rd of the groups which have RDi say they are the use of it for all of their application construction, about a 3rd say it is round half and half, and a third say it is under half. average that out and it's averaging round half of the functions at half of the groups surveyed are achieved the usage of RDi, which implies free form RPG.

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The IBM i Base Is ready to move On Up

funding And Integration indicators For IBM i

protection still Dominates IBM i dialogue, HelpSystems’ 2018 Survey displays

The IBM i Base now not As Jumpy because it Has Been

The Feeds And Speeds Of The IBM i Base

IBM i Priorities For 2017: Pivot To protection

IBM i trends, issues, And Observations

IBM i Survey gets greater As Numbers develop

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finding IBM i: A game Of 40 Questions

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IBM i marketplace Survey: The importance Of Being Earnest

What’s Up in the IBM i market?

IBM i market Survey Fills in the Blanks

IBM App connect XML statistics XML exterior Entity [CVE-2018-1801] | Real Questions and Pass4sure dumps

A vulnerability was found in IBM App connect, Integration Bus and WebSphere Message broker (software Server utility) and categorized as vital. This problem affects a part of the element XML records Handler. The manipulation with an unknown input ends up in a privilege escalation vulnerability (XXE). the use of CWE to declare the problem results in CWE-611. Impacted is confidentiality, integrity, and availability.

The weak point was released 02/04/2019. it is feasible to read the advisory at The identification of this vulnerability is CVE-2018-1801 since 12/13/2017. The attack could be initiated remotely. The technical particulars are unknown and an take advantage of is not publicly purchasable. The pricing for an exploit might be around USD $25k-$100k for the time being (estimation calculated on 02/05/2019).

There is no assistance about feasible countermeasures common. It may be advised to change the affected object with an option product.

CPE CVSSv3 VulDB Meta Base ranking: 6.3VulDB Meta Temp ranking: 6.3

VulDB Base ranking: ≈6.3VulDB Temp score: ≈6.3VulDB Vector: 🔒VulDB Reliability: 🔍

CVSSv2 VulDB Base score: 🔒VulDB Temp score: 🔒VulDB Reliability: 🔍Exploiting category: Privilege escalation / XXE (CWE-611)local: NoRemote: yes

Availability: 🔒

cost Prediction: 🔍current fee Estimation: 🔒

chance Intelligence risk: 🔍Adversaries: 🔍Geopolitics: 🔍financial system: 🔍Predictions: 🔍moves: 🔍 Countermeasures informed: no mitigation known0-Day Time: 🔒 Timeline 12/13/2017 CVE assigned02/04/2019 Advisory disclosed02/05/2019 VulDB entry created02/05/2019 VulDB remaining replace sourcesAdvisory: trade.xforce.ibmcloud.comConfirmation: 🔒

CVE: CVE-2018-1801 (🔒)

entryCreated: 02/05/2019Complete: 🔍

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what is "Liberty Profile" - IBM WebSphere utility Server V8.5 | Real Questions and Pass4sure dumps

"Liberty Profile" - IBM WebSphere utility Server V8.5

IBM WebSphere software Server V8.5 Liberty profile is a flexible and dynamic server profile of changed into which enables the became server to deploy handiest required customized facets as a substitute of deploying a large set of attainable JEE add-ons.

what is Liberty profile in IBM was?

Liberty Profile is part of IBM WebSphere utility Server V8.5.5.5. It is terribly lightweight profile of WebSphere application Server. Liberty profile is a versatile and dynamic profile of turned into which enables the was server to set up most effective required customized facets as a substitute of deploying a huge set of attainable JEE add-ons. builders can choose required points in line with business requirement and push it to app server. become Liberty profile is most useful desirable for developers working on mission critical enterprise purposes. It can be even used for creation deployment. present version of IBM changed into Liberty profile is Java EE 6 criticism and works smartly for functions the use of this Java EE 6 certified web profile.

Liberty profile is often known as gentle weight, down sized version of changed into ranging from eight.5. they can decide to use the identical for software development if we've confined and smartly described set of server add-ons.

turned into Liberty profile architecture

architecture component Description

  • Liberty Kernel:  it is the core server profile part.
  • Java EE 6+ :  ordinary Java EE6 API
  • facets: JSP, JSF, internet App protection, Servlet, JMS etc.
  • purposes: web applications, enterprise functions
  • OSGi Framework Runtime: In-constructed run time bundles
  • observe:

    "Liberty profile is part of IBM was Product and it is distributed as an in-constructed core function of the WebSphere software Server. Liberty profile isn't in any respect a separate product. it is a runtime environment for software server (turned into) with a wealthy feature set that varies by way of WebSphere software Server diverse versions.”

    How changed into Liberty Profile Works?

    If net-utility requires handiest a servlet engine, then instead of starting all different accessories liberty profile handiest begins the became kernel, the HTTP transport and the net container in order that developers can right away beginning and install the applications.

    If an application needs persistence function in their application and would like to use JPA provider component to entry relational records (RDBMS), developer just need to add JPA configuration in XML and Liberty profile will make it attainable persistence within the utility.

    The set of points which they can outline in <featureManager> tag describes the concrete profile for the configured server particular illustration and then those lists of facets are tailor-made for the application deployed to the software server. Internally, these features are a discrete set of JARs which is nothing but the OSGi bundles which can be initialized and began as quickly as they are brought to the server configuration file (e.g. server.xml ). <function> tag is use to outline app particular JEE facets.

    the freedom profile works on a dynamic runtime ambiance called OSGi runtime. OSGi services are used to manage JEE based mostly part lifecycles, and the injection of dependencies and their runtime configuration. After this step server procedure and contains a single JVM, general because the Liberty kernel, and any variety of optional features required by using the purposes. After that configured feature code and many of the kernel code both runs as impartial OSGi bundles or OSGi modules inside an OSGi framework (Open gadget Gateway).

    the freedom profile supports a subset of the entire WebSphere software Server programming model. It helps beneath kinds-

  • web applications
  • OSGi functions
  • business JavaBeans (EJB) purposes
  • OSGi Framework Lifecycle

    OSGi framework follows OSGi Lifecycle for their Bundles. beneath is the common lifecycle of OSGi.

    how to deploy become Liberty profile?

    There are two ways to download and set up the freedom profile runtime

  • From within your Eclipse IDE.
  • As a standalone JAR file that you deploy from the command line.
  • Please refer beneath URLs for download material/ibm%C2p.cAE-websphere%C2p.cAE-utility-server-v85-liberty-profile-developer-tools-eclipse-helios-indigo

    Why should i use changed into Liberty Profile?

    There are some key advantages of the use of Liberty profile runtime which is listed under:

    became Liberty profile standard configuration

    Liberty profile makes it truly handy to configure their server in a really basic and productive means the use of XML file. for example, default server.xml configuration file can also seem like under:

    <server description="look at various server"> <!--enable elements which you would want to use --> <featureManager> <feature>jsp-2.2</feature> </featureManager> <httpEndpoint identification="appHttpEndpoint" host="localhost" httpPort="9080" httpsPort="9443" /> </server>

    As per above default server.xml configuration truly allows the JSP 2.2 characteristic, which depends on the Servlet three.0 function; hence the Servlet function is immediately enabled. They needn't to name and define it explicitly in changed into server.xml configuration file.

    was Liberty profile configuration "Code Snippets"

    below are some code snippet to configure became Liberty Profile. they can use the equal as and when required in the application building.

    <server> <featureManager> <feature>servlet-3.0</characteristic> <feature>mylocalConnector-1.x</feature> </featureManager> </server>

    above code will allow servlet-three.0 API and myLocalConnector-1.x for the configured software within the IBM WebSphere Server.

    <server description= "Dev Server DV 33-04"> <featureManager> <feature>servlet-three.x</characteristic> </featureManager> <application identification="TestWebApp" region="WebApps/test.warfare" identify="TestWebApp" type="battle" </server>

    above code will allow servlet-3.0 API and a dependent warfare file named as check.warfare below net application TestWebApp.

    <server description="My examine server"> <featureManager> <!--allow jndi api for datasource lookups --> <characteristic>jndi-1.0</function> </featureManager> </server> above code will permit jndi-1.0 edition for utility. <client> <featureManager> <characteristic>javaeeClient-7.x</feature> <featureManager> <utility identity="CLIENT_APP_ID_VALUE" identify="CLIENT_APP_TYPE" type="ear" place="clientAppType.ear"/> </customer>

    above code will enable java customer api v7 and observe this to when install as an EAR file.

    datasource configuration snippet

    <?xml edition="1.0" encoding="UTF-eight"?> <server description="My examine DB server"> <!-- enable facets --> <featureManager> <characteristic>jdbc-four.x</characteristic> </featureManager> <datasource databaseName="$changed into.server.dir/CustomerDB" id="datasource_id" jndiName="data/jndi/lookup/financial institution/CustomerDB" </server>

    above code will permit jdbc-4.0 API and enable configured database name in line with jndi search for.

    regular JEE specification in became Liberty profile

    under Oracle JEE/J2EE/JSR requisites are available in strong IBM become Liberty profile. builders can configure any elements the use of above code snippets in line with software requirement.

  • CDI 1.2
  • JSP 2.3 and EL three.0
  • application customer 1.0
  • JASPIC 1.1
  • JACC 1.5
  • SIP Servlets 1.1 and tools
  • SPNEGO assist
  • OSGi App integration
  • JDBC 4.1
  • OSGi & web three.1 aspect configuration for OSGi bundles
  • JAX-RS 2.0 client wizard
  • help for far flung development
  • Auto-scaling and dynamic routing
  • true-Time Communications (WebRTC) and CouchDB
  • JAX-RS 2.0, Java Batch
  • JMS 2.0, JPA 2.1
  • Bean validation 1.1
  • JSON-P 1.0
  • EJB three.2 Lite
  • Concurrent-1.0
  • Servlet 3.1
  • OpenID join
  • Java 8 toleration
  • WebSockets
  • Challenges 

    1.  the freedom Profile is free to make use of which is decent however simplest in development environment now not in creation atmosphere. If they are looking to stream to creation with the liberty Profile they will anyways need to pay the standard IBM was licensing cost which doesn't sounds good.

    2.  There are different lightweight servers obtainable nowadays available in the market which is free even for production ambiance so picking Liberty profile over these options nonetheless should be evaluated.

    3.  the freedom Profile does not supply any UI like administrative console to operate server certain effective configuration movements like updating the server config or setting up/uninstalling purposes and so forth. so they should rely on Eclipse/RAD/NetBeans editor to update the server.xml file or they ought to manually adjust it which doesn't seem a feasible choice for developers.

    four.  software builders evaluate this server to Tomcat and Glassfish which have already been around for many years so it can be probably the most biggest challenges for moving to liberty profile.

    5.  In newest version liberty profile is coming up with lot of latest aspects so it could be unique to see how the freedom Profile handles the boost performance load with both footprint and dimension (approx. 60MB).

    6.  For lessen IBM WebSphere Server versions (5, 6, 7) it isn't suitable which generally is a challenge for developers and applications the usage of them.


    In a nutshell they will say that Liberty Profile is among the quickest changing and most exciting app servers to watch in the marketplace today. So they should still in reality focus on their upcoming releases. New Beta types are developing very immediately out there with lot of recent features which may use in their functions with just an easy configuration. IBM should definitely focus on building UI and a few Migration Apps for Liberty Profile builders so that they can impulsively undertake it as evaluate to different important rivals like Tomcat, Glassfish, Joss etc. It can be truly wonderful to peer how the latest types of Liberty Profile tackle the enhance performance with each footprint and measurement which is the predominant plus with IBM become Liberty Profile.

    References doctors/developing-applications-wdt-liberty-

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    The Big Data Trade-Off | real questions and Pass4sure dumps

    This chapter is from the book 

    Because of the incredible task of dealing with the data needs of the World Wide Web and its users, Internet companies and research organizations realized that a new approach to collecting and analyzing data was necessary. Since off-the-shelf, commodity computer hardware was getting cheaper every day, it made sense to think about distributing database software across many readily available servers built from commodity parts. Data processing and information retrieval could be farmed out to a collection of smaller computers linked together over a network. This type of computing model is generally referred to as distributed computing. In many cases, deploying a large number of small, cheap servers in a distributed computing system can be more economically feasible than buying a custom built, single machine with the same computation capabilities.

    While the hardware model for tackling massive scale data problems was being developed, database software started to evolve as well. The relational database model, for all of its benefits, runs into limitations that make it challenging to deploy in a distributed computing network. First of all, sharding a relational database across multiple machines can often be a nontrivial exercise. Because of the need to coordinate between various machines in a cluster, maintaining a state of data consistency at any given moment can become tricky. Furthermore, most relational databases are designed to guarantee data consistency; in a distributed network, this type of design can create a problem.

    Software designers began to make trade-offs to accommodate the advantages of using distributed networks to address the scale of the data coming from the Internet. Perhaps the overall rock-solid consistency of the relational database model was less important than making sure there was always a machine in the cluster available to process a small bit of data. The system could always provide coordination eventually. Does the data actually have to be indexed? Why use a fixed schema at all? Maybe databases could simply store individual records, each with a different schema, and possibly with redundant data.

    This rethinking of the database for an era of cheap commodity hardware and the rise of Internet-connected applications has resulted in an explosion of design philosophies for data processing software.

    If you are working on providing solutions to your organization’s data challenges, the current era is the Era of the Big Data Trade-Off. Developers building new data-driven applications are faced with all manner of design choices. Which database backend should be used: relational, key–value, or something else? Should my organization build it, or should they buy it? How much is this software solution worth to me? Once I collect all of this data, how will I analyze, share, and visualize it?

    In practice, a successful data pipeline makes use of a number of different technologies optimized for particular use cases. For example, the relational database model is excellent for data that monitors transactions and focuses on data consistency. This is not to say that it is impossible for a relational database to be used in a distributed environment, but once that threshold has been reached, it may be more efficient to use a database that is designed from the beginning to be used in distributed environments.

    The use cases in this book will help illustrate common examples in order to help the reader identify and choose the technologies that best fit a particular use case. The revolution in data accessibility is just beginning. Although this book doesn’t aim to cover every available piece of data technology, it does aim to capture the broad use cases and help guide users toward good data strategies.

    More importantly, this book attempts to create a framework for making good decisions when faced with data challenges. At the heart of this are several key principles to keep in mind. Let’s explore these Four Rules for Data Success.

    Build Solutions That Scale (Toward Infinity)

    I’ve lost count of the number of people I’ve met that have told me about how they’ve started looking at new technology for data processing because their relational database has reached the limits of scale. A common pattern for Web application developers is to start developing a project using a single machine installation of a relational database for collecting, serving, and querying data. This is often the quickest way to develop an application, but it can cause trouble when the application becomes very popular or becomes overwhelmed with data and traffic to the point at which it is no longer acceptably performant.

    There is nothing inherently wrong with attempting to scale up a relational database using a well-thought-out sharding strategy. Sometimes, choosing a particular technology is a matter of cost or personnel; if your engineers are experts at sharding a MySQL database across a huge number of machines, then it may be cheaper overall to stick with MySQL than to rebuild using a database designed for distributed networks. The point is to be aware of the limitations of your current solution, understand when a scaling limit has been reached, and have a plan to grow in case of bottlenecks.

    This lesson also applies to organizations that are faced with the challenge of having data managed by different types of software that can’t easily communicate or share with one another. These data silos can also hamper the ability of data solutions to scale. For example, it is practical for accountants to work with spreadsheets, the Web site development team to build their applications using relational databases, and financial to use a variety of statistics packages and visualization tools. In these situations, it can become difficult to ask questions about the data across the variety of software used throughout the company. For example, answering a question such as “how many of their online customers have found their product through their social media networks, and how much do they expect this number to increase if they improved their online advertising?” would require information from each of these silos.

    Indeed, whenever you move from one database paradigm to another, there is an inherent, and often unknown, cost. A simple example might be the process of moving from a relational database to a key–value database. Already managed data must be migrated, software must be installed, and new engineering skills must be developed. Making smart choices at the beginning of the design process may mitigate these problems. In Chapter 3, “Building a NoSQL-Based Web App to Collect Crowd-Sourced Data,” they will discuss the process of using a NoSQL database to build an application that expects a high level of volume from users.

    A common theme that you will find throughout this book is use cases that involve using a collection of technologies that deal with issues of scale. One technology may be useful for collecting, another for archiving, and yet another for high-speed analysis.

    Build Systems That Can Share Data (On the Internet)

    For public data to be useful, it must be accessible. The technological choices made during the design of systems to deliver this data depends completely on the intended audience. Consider the task of a government making public data more accessible to citizens. In order to make data as accessible as possible, data files should be hosted on a scalable system that can handle many users at once. Data formats should be chosen that are easily accessible by researchers and from which it is easy to generate reports. Perhaps an API should be created to enable developers to query data programmatically. And, of course, it is most advantageous to build a Web-based dashboard to enable asking questions about data without having to do any processing. In other words, making data truly accessible to a public audience takes more effort than simply uploading a collection of XML files to a privately run server. Unfortunately, this type of “solution” still happens more often than it should. Systems should be designed to share data with the intended audience.

    This concept extends to the private sphere as well. In order for organizations to take advantage of the data they have, employees must be able to ask questions themselves. In the past, many organizations chose a data warehouse solution in an attempt to merge everything into a single, manageable space. Now, the concept of becoming a data-driven organization might include simply keeping data in whatever silo is the best fit for the use case and building tools that can glue different systems together. In this case, the focus is more on keeping data where it works best and finding ways to share and process it when the need arises.

    Build Solutions, Not Infrastructure

    With apologies to true ethnographers everywhere, my observations of the natural world of the wild software developer have uncovered an amazing finding: Software developers usually hope to build cool software and don’t want to spend as much time installing hard drives or operating systems or worrying about that malfunctioning power supply in the server rack. Affordable technology for infrastructure as a service (inevitably named using every available spin on the concept of “clouds”) has enabled developers to worry less about hardware and instead focus on building Web-based applications on platforms that can scale to a large number of users on demand.

    As soon as your business requirements involve purchasing, installing, and administering physical hardware, I would recommend using this as a sign that you have hit a roadblock. Whatever business or project you are working on, my guess is that if you are interested in solving data challenges, your core competency is not necessarily in building hardware. There are a growing number of companies that specialize in providing infrastructure as a service—some by providing fully featured virtual servers run on hardware managed in huge data centers and accessed over the Internet.

    Despite new paradigms in the industry of infrastructure as a service, the mainframe business, such as that embodied by IBM, is still alive and well. Some companies provide sales or leases of in-house equipment and provide both administration via the Internet and physical maintenance when necessary.

    This is not to say that there are no caveats to using cloud-based services. Just like everything featured in this book, there are trade-offs to building on virtualized infrastructure, as well as critical privacy and compliance implications for users. However, it’s becoming clear that buying and building applications hosted “in the cloud” should be considered the rule, not the exception.

    Focus on Unlocking Value from Your Data

    When working with developers implementing a massive-scale data solution, I have noticed a common mistake: The solution architects will start with the technology first, then work their way backwards to the problem they are trying to solve. There is nothing wrong with exploring various types of technology, but in terms of making investments in a particular strategy, always keep in mind the business question that your data solution is meant to answer.

    This compulsion to focus on technology first is the driving motivation for people to completely disregard RDBMSs because of NoSQL database hype or to start worrying about collecting massive amounts of data even though the answer to a question can be found by statistical analysis of 10,000 data points.

    Time and time again, I’ve observed that the key to unlocking value from data is to clearly articulate the business questions that you are trying to answer. Sometimes, the answer to a perplexing data question can be found with a sample of a small amount of data, using common desktop business productivity tools. Other times, the problem is more political than technical; overcoming the inability of admins across different departments to break down data silos can be the true challenge.

    Collecting massive amounts of data in itself doesn’t provide any magic value to your organization. The real value in data comes from understanding pain points in your business, asking practical questions, and using the answers and insights gleaned to support decision making.

    One Billion Deals On Smart Contracts | real questions and Pass4sure dumps


    In this article I’m covering the use of smart contracts for automatic execution of deals, enabling all parties to agree to predefined transaction conditions in order for them to be executed later automatically. The sum of money being transferred is not the issue here — the data surrounding the deal, defining its outcome, is more important. They don’t often think about it, but even a simple purchase of goods is in fact a complex deal depending on many parameters. You can verify this statement simply by examining a shop receipt. Even lending a sum of money to your friend implies an unspoken agreement of a reasonable time frame to return the money, meaning that the process is once more accompanied by additional data. This is why cryptocurrencies without smart contracts fail to establish the necessary requirements for deal processing.

    Smart contracts are pieces of code specifically tailored to deal processing, although it’s often argued that current systems in place are more than adequate. In this article, I’d like to demonstrate some properties of blockchain solutions from a developer’s viewpoint. I will deliberately not discuss the topics of volatility or current market conditions, scam cases and other non-technical issues. I’m neither a trader nor an investor, and couldn’t care less about the price of BTC, so please refrain from discussing these and ICO-related topics in the comments section.

    I’ll also describe the shortcomings of smart contracts and DApps, some of which are quite prominent. This is exactly what makes smart contracts unsuitable for a wide range of tasks. We’ll discuss the costs of ownership and maintenance, security and development of systems of smart contracts and DApps for automation of deals, among other things. Let us try to answer the title question of the article — how close are they to a billion deals on smart contracts and is it a realistic number they can actually reach?

    I will speak of modern blockchains, such as Ethereum, EOS or Neo, their forks and smart contracts deployed on them, of the process of development of such contracts, their deployments and maintenance. Any of these blockchain networks allow a user to set up a small program (smart contract) which will process various events generated by the network users. Each of the events is, technically speaking, a transaction; generated and signed by a user. For any of these events, users pay a small fee to a block producer, who processes the transaction, executes the smart contract code, receives a result and adds it to the next block being formed. Every event can carry a certain amount of cryptocurrency, which can be sent to the balance of a smart contract, and can then be utilized according to its code. Every event triggers a subprogram (function) from the contract. You should already know the basics, otherwise you wouldn’t be reading the article, would you?

    It’s seemingly easy to achieve a billion deals, as any transaction on a decentralized network implies the transfer of rights of ownership of data from one address to another. In other words — a deal. To give another example, a crypto remittance looks this way — “Hey, miners. How about I pay one of you X coins for you to transfer my ownership of Y coins to Ivan on blockchain?” The code that checks certain conditions, and as a result transfers the ownership, is called a “contract”, and it does have a lot of similarities with a regular deal: “If verified by a valid signature, the transaction passes on the rights to X bitcoins from address 1 to address 2”. To re-iterate this — any crypto transaction using UTXO (as in the case with BTC), is the result of a typical deal (contract) in which the sender (presenting his digital signature) transfers his rights for the output of the transaction to another network user.

    Legally smart contracts are compared to regular agreements, and not without reason. From a non-technical perspective, we’re interested in seeing a billion deals not as technical concept, but as defined by lawyers — deals of consequence to the real world, which automatically excludes such transactions as, for instance, the ever-present airdrops. Smart contracts have a long way to go before replacing the offline tools they use today. I’ll describe the technical advantages of such solutions in detail in order to evaluate the cases where the use of such systems is justified, as well as cases where the developers’ promises must be taken with a pinch of salt.

    DApps, Smart Contracts and Automatization

    Deploying a smart contract on a blockchain network means outsourcing the processing of deals. If you show a smart contract on your site, its address will be the thing users see and interact with, as everything else they’ll do on their own in the browser. If need be, you can manage your accounts in a centralized way and redefine the contracts’ conditions, but it will happen without a dedicated admin dashboard, as authorization only requires your own public key. In its current state, the Ethereum and EOS infrastructure has matured enough to allow for the creation of fully-featured DApps, consisting of a core (a few code lines defining the logic of interaction with smart contracts) and a set of blockchain smart contracts.

    The limited functionality of such systems that can’t access external data sources or make sudden changes to the internal logic, makes it a perfect fit for deals where one or more parties agree to a transaction without mutual trust and have only pre-verified facts pertaining to the deal to record.

    Smart contract code can be compared to a stored procedures widely using in any modern SQL database, widely employed in FinTech. The code of a such stored procedure in question is transparent and open for inspection, with any modification recorded on blockchain. Every transaction requires a valid digital signature, allowing it to use a centralized or decentralized scheme of identity verification. As such, any of the following is made redundant:

  • Registered and verified (among other things — via SMS) accounts database
  • Code for integration with banking systems
  • Database and transaction log backups
  • DDoS protection
  • Some other specific and obsolete heaps of code
  • So, let’s take a look on some aspects of DApps, and try to conclude — are they ready to billions of automated deals or not?

    Cost of Ownership

    One of the key advantages of decentralized systems is the low cost of maintenance of an IT system. It’s a huge advantage from a business perspective. A company can maintain a set of smart contracts to provide the same services as conventional IT systems would allow, but at a lower cost and with a higher level of security. The concept of microservices independently serving a certain purpose, as opposed to one massive all-in-one application, is predominant in the modern IT sphere. One massive service with tons of code becomes difficult to manage and over time, loses flexibility and have very high cost of ownership. “Microservices” approach requires highly-coordinated management of numerous hubs and stimulates companies to adopt cloud services, since the cloud is the go-to solution for smaller services. This is as opposed to servers, mining farms and technical problems that may arise. Cloud services provide out-of-the-box UI allowing it to manage a large number of small servers, which helps to pinpoint errors, control every system and scale it easily.

    Decentralized networks and smart contracts make the outsourcing options described above even more viable. Block producers are the ones responsible for maintenance, as it is in their best interest to keep the network running without a hitch. Hacking or overloading such a network is possible only in the early stages of its development due to bugs and critical oversights in its design. However, the same applies for centralized systems — massive hacking attempts are observed in both cases. If they compare two software solutions and inherently consider their level of security equal, then the one deployed on blockchain will still provide an increased level of resistance to various network issues and a high level of reliability. Fail-safe systems are something that require a very costly infrastructure, like with centralized solutions. This is why, at the end of the day, a smart contract-based solution is financially viable for business owners.

    In addition, pricing mechanisms underlying transactions in process are more flexible than service plans of cloud providers. It’s not necessarily a more fair mechanism as block producers (miners) can, in theory, enter a mutual agreement (as can cloud service providers). In the first case though, the pricing is transparent and auditable, which incentivizes miners to avoid entering into cartel agreements, as such behaviour can have a detrimental effect on the whole blockchain network and a further detrimental effect on all of its participants. These facts allow us to conclude that pricing for computational resources and the processing outsourcing of deals on blockchain should be more favourable to businesses than that of centralized cloud service providers.

    Simplicity has a lot of benefits in terms of security, a great expenditure of centralized service providers. We’ll discuss security in detail further down, but let’s keep in mind that all the main safety mechanisms are already built into decentralized systems, being integral parts of it, so a business owner only pays for deployment of his code on blockchain and doesn’t have to bear any costs to protect his database, web server, infrastructure separately.

    Rightfully the costs of ownership of smart contracts go first among the advantages of decentralized systems. Smart contract regulated deals present a cheap means of transaction, fulfilling all the requirements — security, transparency, enforceability. In these terms, no other solution can compete against smart contracts and these advantages will be a great driver towards achieving the milestone of a billion deals.

    Security and reliability

    The next point to cover is related to security and the reliability of systems. At the core of any platform for automated deals, centralized or decentralized) resides the user authentication and authorization subsystem. This subsystem, developed with all safety standards in mind, still has an inherent flaw. It processes and stores private user data, leakage of which leads to a drastic decrease of security level. For instance, even though banks do not store CVC codes of plastic cards, access to other sensitive data opens possibilities of social engineering attacks or brute-force attempts. The leak of a database with password hashes, even if reinforced by encryption and single-use authorisation, compromises the overall system nonetheless, because any disclosure of critical data can lead to successful attack, it’s not only data in database but also its structure or statistical properties for stealed data. No centralized system can completely work around this potential problem, as from a technical point of view, such systems use a storage of “shared secrets”.

    We’ve already enumerated some bulks of code made redundant by smart contracts and if you review them again, you’ll see that all of these are critical for security. Therefore, security needs only to be provided exclusively on the client side. It is sufficient to protect the workplaces of key employees (which is common practice nowadays already) by providing a reasonable level of security and making the IT security job a lot easier not just in prevention, but in hacking cases investigations as well. Speaking of the so-called “51% attack” relating to blockchains, let’s not forget the fact that in centralized systems, devastating attacks can be performed by one person. So yes, you can even refer to it as the “1% attack” if you so wish.

    It’s necessary to admit that decentralized software is still in the beta version and has certain persisting security issues, but this can be said about any system in the early stages of development. At the end of the day the removal of weak points from which critical data can be stolen or MITM attack can be performed, ends up having an overall positive effect on security. Deals conducted via one-time smart contracts or a related system — problems with access to the contract, division of rights, transaction validation and irreversibility of executed lines of code — are completely solved on the side of blockchain and smart contracts. In circumstances when basic cryptocurrencies are used, any cold storage provides a better level of security than any centralized system possibly can, as it simply does not have as many vulnerabilities that the latter has.

    Speaking of the perspectives of adoption of even more secure systems, decentralization provides a whole lot of opportunities for development — namely, zero-knowledge protocols. Such protocols allow the outsourcing of confidential processes and calculations, essentially transforming decentralized networks into supercomputers, able to process great amounts of private data without compromising security. As of now, such systems are complex and inefficient, practically unusable for real use cases. The same could be said about the internet and cryptography in general. Centralized systems don’t have a lot of perspectives for development, apart from an increase in sheer processing power, resulting in higher CPU, RAM, storage and parallel computing capacity.

    The fact that from now on security can only get better in decentralized systems constitutes yet another argument in favour of smart contracts working towards the achievement of one billion deals.

    Ease of Porting and Compatibility

    Compatibility should in theory be a non-issue, as data exchange systems already moved towards this goal quite some time ago. A lot of unified standards exist, such as JSON, XML and YAML that facilitate the transfer of structured data between heterogeneous services. This fact, however, does not ensure compatibility, as different companies present different requirements to the data itself, which may lead to discrepancies in the way it’s transferred, validated or treated. Open source code and smart contracts logic help eliminate this problem. In decentralized systems code is executed in a strictly deterministic way on all particiapnts’ machines, not allowing for discrepancies and variations in data treatment. Strange as it is, it appears that, in general, smart contracts are far more “centralized” than one huge database with thousands of replicas and .

    Another important thing to remember about decentralized systems is the fact that their code is always open. There is no point in hiding the code which is openly used by any entity on a network, where every transaction can be replicated in detail. The Kerckhoffs principle of cryptography, which in a well-protected cryptographic system has the algorithm at its base, must not be kept secret, but be readily available to anyone. It allows any well-informed blockchain developer to perform thorough testing of any solution under development and ensure its compatibility. The programming languages must be verified, which is a simple task in such a deterministic environment as block processing and blockchain transactions.

    Deals between multiple parties are significantly better suited to smart contracts than to any centralized systems. This is because all the necessary underlying tools are openly accessible and can be used in any web-based system without the need to register or purchase expensive software solutions, define rules and terms of use — all of these things are now in the past. A lot of large companies changed to a SaaS (Software-as-a-Service) model and this business model works great with smart contracts, allowing them to implement even the most complex subscription models with maximum transparency to any user.

    There are, as one may suspect, problems that transpire in connection with real-life cases. The platforms under development keep transforming all the time and compatibility they present can’t be compared yet to well-established data-oriented companies. However, in the long term, blockchain can, and certainly will, provide unified standards for smart contracts, and back them up by high rates of adoption and block producing community.

    Development and Operation

    In relation to development and operation, everything is not as successful as in terms of security and compatibility. A publicly verifiable execution of smart-contract code, transparent and verifiable history of every line of code can’t be taken for free. Solutions based upon smart contracts primarily depend on the properties of the virtual machine that executes their code. The main properties of a virtual machine are:

  • Deterministic execution. The ability to produce exactly the same output from the same input regardless of the platform, CPU or memory type, workload or exceptions in execution. Even in cases of errors and misbehaviour of participants.
  • Resource limitations. A block producer has to limit the amount of resources dedicated to execution of a smart contract, as he can not afford to “freeze up” when performing a single transaction, and risking the chance of not forming a block in time.
  • These properties determine the specifics of smart-contract development. Smart contracts are somewhat close to well-optimized C++ objects, where every method performs a limited amount of operations, trying to avoid cycles, complex branching and other bottlenecks with non-constant execution time. Any operations forcing different results on various hardware configurations are explicitly forbidden in a virtual machine. Here are the examples of limitations imposed by the problems described above:

  • Any cycle needs to have a cap on iterations or a cap on max number of objects iterated
  • Aggregation of data from a large number of entities is not allowed
  • No floating point operations
  • No effective iterators for mappings (associative arrays). Any group operations require auxiliary data structures (e.g. reverse index array for goal-seeking mapping keys)
  • All variables and functions’ code is kept in contract storage: memory organized by key-value principle (in order for memory to possess the determinacy property as well)
  • any undefined behaviour leads to deterministic error or constant value
  • The development of smart contracts involves a lot of low-level programming, even though the most widespread programming language for smart contracts (Solidity) seems like a JavaScript. In practice though, it’s a low-level access instrument with very strict types, low amount of primitives, compilable to a very limited set of instructions. During the process of development the need arises to again implement a lot of algorithms already built into other programming languages. The use of libraries is also limited, due to the fact that libraries are similar to contracts, and suffer from the same limitations.

    In addition to the low-level limitations and problems in connection to the virtual machine, smart contract development has a number of other peculiarities, stemming from the blockchain architecture in general, for instance:

  • It’s not possible to completely rely on current time, block’s hash, order of transactions or any other block producer-controlled parameters
  • The only data operable by smart contracts is the balance of addresses of parties to it, current time (not completely reliable, as mentioned above) and contract’s own data
  • Access to the contract cannot be limited, rendering defense from spam or bots impossible
  • All types of resources are strictly limited, including CPU, memory, storage, network. Including the code itself, for example bytecode with size over a certain low amount cannot be deployed
  • This adds to the fact that modifications to correct errors in smart contracts can be quite costly. In theory, the final resort may sometimes be hardforking the whole blockchain. In reality, various versions of contracts can co-exist (via the use of proxy-contracts), but in any case these procedures unnecessarily complicate maintenance, while a simple software update to the code in a centralized system appears to be quite simple.

    These facts lead us to the conclusion that in terms of development, smart contract-based solutions are costly and complicated. Only two major blockchains currently have a complete ecosystem for developers — Ethereum and, to a lesser extent, EOS. Smart contract development hasn’t yet gathered a large enough community or worked out any best practices. There is a lot of work to be done if a billion transactions are ever to be made on smart contracts, but steps towards it are taken every day and there is considerable progress being made.

    Performance and Quality of Service

    One of the favourite topics of discussion among blockchain enthusiasts is TPS (transactions per second). It’s believed that decentralized solutions are inferior in this regard to centralized services, which operate at thousands of transactions per second. However, without a context or specific cases, the discussion is pointless. First off, when speaking about TPS, it’s important to define what a transaction is, define when it is considered finished and define rules of measurement of TPS. Only then can the different systems be compared. VISA outputs are judged to be about 2500 TPS — simple money remittances. If money is transferred between accounts within one company, a simple spreadsheet database can sustain heavy workloads, as far as simple balance updates are concerned. When speaking of transfers between different systems, an entirely different approach is required. Do these 2500 TPS allow for failed or incomplete transactions? If, due to some business logic discrepancy, money is withdrawn from VISA but isn’t credited to MasterCard, how should the time of this transaction be calculated? Should a SWIFT transaction be considered completed if it can be reversed by a regulator within 24 hours?

    I do not claim that smart contract solutions are faster. Quite the opposite! They are in fact slower than their centralized counterparts. However, in terms of complexity, the length of links a transaction has to make, while conforming to certain security standards, are not as complex as VISA transactions. They are far more complex than smart contract transactions. Even the most simple bank money transfer must go through a local gateway, queue for processing, be processed, queue for regulator’s approval, be approved, invoke database update and wait for it to be replicated. This is all while being ready to roll-back within the next 24 hours.

    A smart contract transaction is subject to one queue, with one of the block producers and one ledger update with replication. While retaining the same capability as a bank, an entity can easily bypass all the intermediary steps, while still controlling the logic of the operations via smart contracts. This is without the need to authenticate and protect the transaction at any of the intermediary hubs.

    In the case of smart contracts, another factor comes into play — the market. Blockchain ecosystems, consisting of many participants, can be subjects to serious volatility, even higher than that of banking systems with centralized governance. Fees can grow, performance may slump and controlling it all is next to impossible.

    In the long term, the advantages of transparent processing should outweigh those of centralized software solutions, but now it’s isn’t anything other than a weak spot.

    User Experience

    Some people argue that users struggle with private keys, seed phrases and all the crypto stuff. I believe the issue is blown out of proportion and that it’s only a temporary problem. Nowadays, you can hardly find a person who’s never used passwords, never reset his e-mail access etc. People are more than capable, given the proper motivation, to adopt the software required to deal with crypto products, and it doesn’t even require any specific or deep knowledge to comprehend. The concept of private and public keys is easy to grasp, as proven by my experience of teaching it.

    Responsibility for one’s private data is a completely separate issue. Losing one’s private key is something that can’t happen in a real deal, but can spell disaster for a smart contract deal. In the real world, the situation is easier to correct retroactively, as things can be amended and easily rewritten. Smart contracts leave no room for error(unless specific error handling was implemented in the contract’s logic). It’s a users’ duty to keep track of their own accounts, and manage them without losing or breaking anything. In the absence of centralized services ready to help reset the password, or customer support, it’s all on the users shoulders in the case of blockchain.

    That is quite a challenge for a user, in contrast with their ability to reset access to centralized systems, even if mechanisms weren’t always in place for that. Many people abandon all thought of using blockchain upon realization that it requires some special software or knowledge. With their services, that equates to about 80% of users. There is no direct way around this problem. There is no way around generating digital signature on the client’s side, as no other party is responsible for doing it. It may take the industry ten years or more to shape the tools for critical private data management to match the best practices used in web services these days.


    Summing it all up, smart contracts allow users to settle deals with low costs, simplify interconnectivity and improve safety and security by moving critical actions to the client’s side. They require the users to take full responsibility for their keys. They just can’t work as fast as centralized solutions and can’t boast a developed ecosystem, unlike regular web software solutions.

    In the long run, I keep faith in decentralized solutions, and don’t see any alternatives to them in a world where people want to be independent economic agents. If humanity stands for honest competition in economy, equal opportunity for everyone and real personal digital rights, it has to work towards adoption of decentralized software, a process comparable in magnitude to moving from monarchy to democracy. This is an ambitious undertaking both for users and developers, which will inevitably lead to abandoning many old ways and re-inventing them.

    So, will it all work? And will they see the coveted 1 billion transactions soon? I believe the answers to those questions are — yes and no. Blockchain will work, but it will take some time: 5–10 years or even longer. The core advantages of processing software are indisputably better in decentralized solutions, but for those to really make an impact, a lot of people have to put in lots of work, which normally doesn’t happen outside of global crisis situations. The likes of which no one in his right mind would wish upon the world.

    Follow these steps to assign vSphere permissions and roles | real questions and Pass4sure dumps

    The role of permissions in vSphere management is to segregate VM control among different application support teams....

    A permission is a pairing of a user or group with a role, and it is applied to an object, such as a data store or VM.

    To understand how to use vSphere permissions, it's helpful to follow an example. To give the networking team the ability to attach a VM to a port group, for instance, you'll need to create a role and then assign the networking team that role.

    Step 1: Create the role

    To open the vSphere Web Client and go to the homepage, click the house icon at the top, and then click Roles under Administration.

    Homepage Image 1: The vSphere Web Client homepage.

    To create a new role, click the green plus button. Give your role a name. In this example, the name is Connect_Network. Then, assign some privileges to the role. They will only add the Assign network privilege from the network group. Click OK to create your new role.

    Create a role Image 2: Create a role in vSphere. Step 2: Assign the role to a group

    Switch to an inventory view. In this example, they will use the Networking view. To assign the Admin_Network group the ability to connect VMs to any port group in my Lab data center, right click the data center and click Add Permission...

    Add Permission Image 3: Add Permissions.

    In the Add Permission dialogue, click the Add button at the bottom. In the Select Users/Groups dialogue, find the user that you want to assign permission -- in this case, the Admin_Network group -- then click Add and OK.

    Assign permission Image 4: Choose a user or group.

    Back on the Add Permission screen, select the role they created, Connect_Network, from the drop-down list in the Assigned Role box, and then click OK.

    Add Permission screen Image 5: Select a role.

    Now all members of the Admin_Network group can connect VMs to any port group in the data center. Click the Manage tab and then the Permissions tab to see each user's vSphere permissions. You can see the most recently added permission at the top of this list, along with all of the other permissions.

    Permissions tab Image 6: See each user's permissions in the Permissions tab.

    vSphere permissions are a little complicated. To change and connect the VM to a port group and then add it to the Connect_Network role, the Admin_Network team must have the Virtual Machine Settings privilege.

    Edit role Image 7: Give the Admin_Network team the Virtual Machine Settings privilege.

    You can use the same methods to control which users can put VM disks on data stores or create VMs on particular vSphere clusters. Create the roles you need and assign them to groups for different objects.

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