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000-601 - IBM System z Solution sales V5 - Dump Information

Vendor : IBM
Exam Code : 000-601
Exam Name : IBM System z Solution sales V5
Questions and Answers : 77 Q & A
Updated On : April 17, 2019
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000-601 Questions and Answers

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000-601 IBM System z Solution sales V5

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000-601 exam Dumps Source : IBM System z Solution sales V5

Test Code : 000-601
Test Name : IBM System z Solution sales V5
Vendor Name : IBM
Q&A : 77 Real Questions

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IBM IBM System z Solution

ScyllaDB declares help for IBM vigor techniques for actual Time massive facts | killexams.com Real Questions and Pass4sure dumps

these days precise-time database enterprise ScyllaDB introduced a brand new Scylla business release with optimizations for IBM vigour device Servers with the IBM POWER9 multi-core structure. via combining Scylla’s extremely performant, shut-to-the-hardware design with subsequent-technology IBM vigour equipment Servers, corporations can reach new degrees of efficiency while also cutting back the footprint, can charge and complexity of their systems.

ScyllaDB has designed a magnificent allotted database that extends the performance merits we’ve brought with our multi-core POWER9 processors,” said Tim Vincent, IBM Fellow and vp of IBM Cognitive techniques. “The mixture of the Scylla NoSQL database and our vigour system Servers allows for our shared customers to scale up their techniques in preference to perpetually scaling out, developing new alternatives for records middle consolidation and price performance.”

This integration builds upon a multi-faceted relationship between ScyllaDB and IBM. In 2016, IBM Compose all started proposing Scylla as a part of their database-as-a-carrier providing. The collaboration has due to the fact grown to encompass further IBM divisions, including IBM systems (each IBM energy methods and Z programs), IBM Cloud (including IBM Graph as a service) and IBM’s internal use of Scylla to power the IBM Cloud service Catalog.

Scylla is an open supply drop-in alternative for Apache Cassandra. It provides scale-up efficiency of 1,000,000 IOPS per node, scales out to lots of of nodes, and consistently achieves a 99% tail latency of less than 1 millisecond. Scylla’s pioneering shard-per-core implementation, asynchronous structure and auto-tuning capabilities permit corporations to immediately leverage the full merits of the multi-core POWER9 chip.

IBM vigor methods servers are designed for mission-essential functions and rising Cognitive era workloads including synthetic intelligence, desktop researching, deep studying, superior analytics and high-performance computing. no matter if deployed in a non-public, public or hybrid cloud, vigor device Servers are capable of performing tens of millions of I/O operations per 2nd. because Scylla operates asynchronously, it's in a position to take full potential of the pace of the POWER9 processor, riding each I/O and CPU processing in a method that scales linearly with the number of cores on the CPU.

we are excited with the aid of the various developments IBM has made with its energy device Servers,” pointed out Dor Laor, CEO of ScyllaDB. “As information volumes proceed to boost, organizations should technique superior workloads yet additionally steer clear of introducing extra complexity into their programs. The mixture of Scylla and IBM POWER9 promises unprecedented performance, scale, density and efficiency while tremendously simplifying the administrative burden of large statistics techniques.”

Scylla database versions with aid for IBM vigor programs can be found for down load from the ScyllaDB site.

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IBM Db2 question Optimization the usage of AI | killexams.com Real Questions and Pass4sure dumps

In September 2018, IBM introduced a new product, IBM Db2 AI for z/OS. This artificial intelligence engine screens information entry patterns from executing SQL statements, uses machine researching algorithms to pick out top-rated patterns and passes this suggestions to the Db2 query optimizer to be used via subsequent statements.

computing device gaining knowledge of on the IBM z Platform

In may additionally of 2018, IBM announced edition 1.2 of its computer studying for z/OS (MLz) product. here is a hybrid zServer and cloud application suite that ingests efficiency statistics, analyzes and builds fashions that symbolize the health popularity of a lot of indications, screens them over time and gives real-time scoring capabilities.

a couple of elements of this product providing are aimed toward helping a group of model developers and managers. for instance:

  • It supports distinct programming languages such as Python, Scala and R. This allows statistics modelers and scientists to make use of a language with which they're typical;
  • A graphical user interface referred to as the visible model Builder guides mannequin developers devoid of requiring incredibly-technical programming talents;
  • It contains distinct dashboards for monitoring model consequences and scoring features, as well as controlling the device configuration.
  • This machine gaining knowledge of suite become at the start aimed at zServer-based mostly analytics purposes. one of the most first evident selections changed into zSystem performance monitoring and tuning. gadget administration Facility (SMF) statistics that are instantly generated by means of the working device provide the uncooked records for system useful resource consumption corresponding to relevant processor usage, I/O processing, reminiscence paging and so on. IBM MLz can assemble and keep these records over time, and construct and coach models of gadget habits, ranking those behaviors, identify patterns now not with no trouble foreseen by people, advance key performance warning signs (KPIs) after which feed the mannequin outcomes lower back into the device to have an effect on equipment configuration alterations that can enrich efficiency.

    The subsequent step become to enforce this suite to research Db2 performance facts. One answer, referred to as the IBM Db2 IT Operational Analytics (Db2 ITOA) answer template, applies the computing device researching technology to Db2 operational records to gain an understanding of Db2 subsystem health. it may possibly dynamically build baselines for key performance indicators, provide a dashboard of these KPIs and give operational group of workers real-time insight into Db2 operations.

    while customary Db2 subsystem performance is an important component in ordinary utility health and performance, IBM estimates that the DBA support body of workers spends 25% or greater of its time, " ... combating entry route complications which trigger efficiency degradation and service affect.". (See Reference 1).

    AI comes to Db2

    trust the plight of up to date DBAs in a Db2 environment. In present day IT world they need to support one or more big facts applications, cloud software and database features, application installing and configuration, Db2 subsystem and software performance tuning, database definition and administration, disaster recuperation planning, and greater. question tuning has been in existence given that the origins of the database, and DBAs are always tasked with this as neatly.

    The heart of query direction analysis in Db2 is the Optimizer. It accepts SQL statements from purposes, verifies authority to entry the facts, stories the locations of the objects to be accessed and develops an inventory of candidate statistics entry paths. These entry paths can consist of indexes, table scans, numerous desk join methods and others. in the facts warehouse and massive facts environments there are continually further decisions attainable. One of those is the existence of abstract tables (from time to time referred to as materialized query tables) that comprise pre-summarized or aggregated statistics, hence allowing Db2 to steer clear of re-aggregation processing. a different option is the starjoin access course, typical in the facts warehouse, where the order of table joins is changed for performance causes.

    The Optimizer then stories the candidate access paths and chooses the access direction, "with the bottom can charge." can charge in this context capability a weighted summation of resource utilization together with CPU, I/O, memory and other supplies. eventually, the Optimizer takes the lowest cost access route, stores it in reminiscence (and, optionally, in the Db2 directory) and starts entry course execution.

    large facts and facts warehouse operations now include software suites that permit the business analyst to use a graphical interface to build and manipulate a miniature statistics model of the records they need to analyze. The packages then generate SQL statements in line with the clients’ requests.

    The problem for the DBA

    as a way to do good analytics in your assorted statistics stores you need a pretty good understanding of the information requirements, an figuring out of the analytical functions and algorithms obtainable and a excessive-performance facts infrastructure. alas, the quantity and location of information sources is increasing (both in size and in geography), information sizes are turning out to be, and purposes continue to proliferate in number and complexity. How may still IT managers support this environment, certainly with essentially the most skilled and mature staff nearing retirement?

    bear in mind additionally that a big part of decreasing the full charge of ownership of these techniques is to get Db2 purposes to run quicker and more efficaciously. This continually translates into the usage of fewer CPU cycles, doing fewer I/Os and transporting much less information across the community. due to the fact that it's often elaborate to even identify which purposes may advantage from efficiency tuning, one approach is to automate the detection and correction of tuning considerations. this is where computing device getting to know and synthetic intelligence will also be used to super impact.

    Db2 12 for z/OS and artificial Intelligence

    Db2 version 12 on z/OS uses the machine gaining knowledge of amenities outlined above to accumulate and save SQL query text and entry path details, in addition to precise performance-connected historical guidance such as CPU time used, elapsed times and influence set sizes. This offering, described as Db2 AI for z/OS, analyzes and stores the statistics in computing device studying models, with the mannequin evaluation outcomes then being scored and made obtainable to the Db2 Optimizer. The subsequent time a scored SQL commentary is encountered, the Optimizer can then use the mannequin scoring records as enter to its entry direction option algorithm.

    The outcomes may still be a reduction in CPU consumption because the Optimizer uses model scoring input to choose more suitable entry paths. This then lowers CPU charges and speeds software response instances. a big competencies is that the usage of AI application doesn't require the DBA to have information science competencies or deep insights into question tuning methodologies. The Optimizer now chooses the foremost entry paths based no longer most effective on SQL question syntax and information distribution statistics but on modelled and scored historic efficiency.

    This will also be specially vital if you shop facts in assorted locations. as an instance, many analytical queries against massive information require concurrent entry to definite facts warehouse tables. These tables are often called dimension tables, and that they contain the records elements constantly used to control subsetting and aggregation. as an example, in a retail environment consider a desk known as StoreLocation that enumerates each shop and its area code. Queries in opposition t keep revenue facts may also wish to mixture or summarize sales via area; hence, the StoreLocation table should be used by means of some massive information queries. in this atmosphere it is regular to take the dimension tables and duplicate them constantly to the big statistics software. within the IBM world this area is the IBM Db2 Analytics Accelerator (IDAA).

    Now think about SQL queries from each operational functions, statistics warehouse users and massive records enterprise analysts. From Db2's perspective, all these queries are equal, and are forwarded to the Optimizer. despite the fact, within the case of operational queries and warehouse queries they may still undoubtedly be directed to entry the StoreLocation table within the warehouse. in spite of this, the query from the enterprise analyst in opposition t big information tables should probably access the copy of the desk there. This consequences in a proliferations of talents entry paths, and more work for the Optimizer. luckily, Db2 AI for z/OS can supply the Optimizer the assistance it must make wise entry route selections.

    the way it Works

    The sequence of routine in Db2 AI for z/OS (See Reference 2) is commonly right here:

  • all through a bind, rebind, prepare or explain operation, an SQL commentary is passed to the Optimizer;
  • The Optimizer chooses the statistics entry direction; as the choice is made, Db2 AI captures the SQL syntax, access path alternative and question performance facts (CPU used, and many others.) and passes it to a "gaining knowledge of task";
  • The studying assignment, which can also be done on a zIIP processor (a non-general-aim CPU core that doesn't component into application licensing prices), interfaces with the machine getting to know application (MLz model capabilities) to shop this suggestions in a mannequin;
  • because the quantity of facts in each and every model grows, the MLz Scoring carrier (which also can be executed on a zIIP processor) analyzes the model facts and rankings the conduct;
  • throughout the subsequent bind, rebind, prepare or clarify, the Optimizer now has entry to the scoring for SQL models, and makes acceptable changes to entry route selections.
  • There are also a considerable number of person interfaces that give the administrator visibility to the popularity of the gathered SQL commentary efficiency data and model scoring.

    abstract

    IBM's machine gaining knowledge of for zOS (MLz) offering is getting used to incredible impact in Db2 version 12 to enrich the performance of analytical queries as well as operational queries and their linked purposes. This requires administration consideration, as you should verify that your business is prepared to eat these ML and AI conclusions. How will you measure the fees and advantages of the usage of laptop discovering? Which IT support workforce need to be tasked to reviewing the influence of mannequin scoring, and perhaps approving (or overriding) the effects? How will you review and justify the assumptions that the application makes about access route decisions?

    In other phrases, how smartly were you aware your records, its distribution, its integrity and your existing and proposed access paths? this could examine the place the DBAs spend their time in helping analytics and operational utility performance.

    # # #

    Reference 1

    John Campbell, IBM Db2 exceptional EngineerFrom "IBM Db2 AI for z/OS: increase IBM Db2 utility performance with computing device discovering"https://www.worldofdb2.com/movements/ibm-db2-ai-for-z-os-raise-ibm-db2-application-efficiency-with-ma

    Reference 2

    Db2 AI for z/OShttps://www.ibm.com/assist/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html

    See all articles via Lockwood Lyon


    MongoDB declares help for IBM z methods' Mainframe solutions | killexams.com Real Questions and Pass4sure dumps

    big apple, the big apple and PALO ALTO, CA--(Marketwired - Aug 17, 2015) - MongoDB these days deepened its partnership with IBM, announcing plans to offer support for MongoDB's items on IBM z methods mainframe. This aid reflects the amazing momentum for mission essential deployments of the world's quickest becoming database via including aid for the unmatched pace, safety and availability of z systems.

    The announcement comes as IBM launches LinuxONE, a brand new portfolio of hardware, utility and services options in line with the z methods mainframe this is designed for enterprise-grade Linux applications.

    "MongoDB has become the world's fastest growing database by enabling companies to quite simply capitalize on the energy of up to date functions and facts to benefit a aggressive competencies," observed Dev Ittycheria, president and CEO, MongoDB. "For years, the realm's largest organizations have run important functions on IBM mainframes. Our flow to support IBM z techniques is a testament to our commitment to our clients and consumers to make MongoDB available on all primary systems. With this announcement, groups can now construct and run modern, mission-important applications on proven mainframe technologies."

    MongoDB is working closely with IBM to engineer MongoDB enterprise Server to be optimized for Linux on z systems and the new LinuxONE techniques. As part of the settlement, MongoDB's world support and engineering firm will proceed to collaborate with IBM to be sure business continuity for our joint purchasers running MongoDB on IBM z techniques.

    "we're consistently attempting to find best-of-category open supply applied sciences to set up on IBM z techniques," noted Ross Mauri, typical manager, IBM z systems. "Now, agencies can mix the imaginitive features of MongoDB with the unrivaled efficiency of the z methods mainframe to create solutions with new ranges of pace, scale and flexibility."

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    IBM System z Solution sales V5

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    IBM Db2 Query Optimization Using AI | killexams.com real questions and Pass4sure dumps

    In September 2018, IBM announced a new product, IBM Db2 AI for z/OS. This artificial intelligence engine monitors data access patterns from executing SQL statements, uses machine learning algorithms to decide on optimal patterns and passes this information to the Db2 query optimizer for use by subsequent statements.

    Machine Learning on the IBM z Platform

    In May of 2018, IBM announced version 1.2 of its Machine Learning for z/OS (MLz) product. This is a hybrid zServer and cloud software suite that ingests performance data, analyzes and builds models that represent the health status of various indicators, monitors them over time and provides real-time scoring capabilities.

    Several features of this product offering are aimed at supporting a community of model developers and managers. For example:

  • It supports multiple programming languages such as Python, Scala and R. This allows data modelers and scientists to use a language with which they are familiar;
  • A graphical user interface called the Visual Model Builder guides model developers without requiring highly-technical programming skills;
  • It includes multiple dashboards for monitoring model results and scoring services, as well as controlling the system configuration.
  • This machine learning suite was initially aimed at zServer-based analytics applications. One of the first obvious choices was zSystem performance monitoring and tuning. System Management Facility (SMF) records that are automatically generated by the operating system provide the raw data for system resource consumption such as central processor usage, I/O processing, memory paging and the like. IBM MLz can collect and store these data over time, and build and train models of system behavior, score those behaviors, identify patterns not readily foreseen by humans, develop key performance indicators (KPIs) and then feed the model results back into the system to affect system configuration changes that can improve performance.

    The next step was to implement this suite to analyze Db2 performance data. One solution, called the IBM Db2 IT Operational Analytics (Db2 ITOA) solution template, applies the machine learning technology to Db2 operational data to gain an understanding of Db2 subsystem health. It can dynamically build baselines for key performance indicators, provide a dashboard of these KPIs and give operational staff real-time insight into Db2 operations.

    While general Db2 subsystem performance is an important factor in overall application health and performance, IBM estimates that the DBA support staff spends 25% or more of its time, " ... fighting access path problems which cause performance degradation and service impact.". (See Reference 1).

    AI Comes to Db2

    Consider the plight of modern DBAs in a Db2 environment. In today's IT world they must support one or more big data applications, cloud application and database services, software installation and configuration, Db2 subsystem and application performance tuning, database definition and management, disaster recovery planning, and more. Query tuning has been in existence since the origins of the database, and DBAs are usually tasked with this as well.

    The heart of query path analysis in Db2 is the Optimizer. It accepts SQL statements from applications, verifies authority to access the data, reviews the locations of the objects to be accessed and develops a list of candidate data access paths. These access paths can include indexes, table scans, various table join methods and others. In the data warehouse and big data environments there are usually additional choices available. One of these is the existence of summary tables (sometimes called materialized query tables) that contain pre-summarized or aggregated data, thus allowing Db2 to avoid re-aggregation processing. Another option is the starjoin access path, common in the data warehouse, where the order of table joins is changed for performance reasons.

    The Optimizer then reviews the candidate access paths and chooses the access path, "with the lowest cost." Cost in this context means a weighted summation of resource usage including CPU, I/O, memory and other resources. Finally, the Optimizer takes the lowest cost access path, stores it in memory (and, optionally, in the Db2 Directory) and begins access path execution.

    Big data and data warehouse operations now include software suites that allow the business analyst to use a graphical interface to build and manipulate a miniature data model of the data they wish to analyze. The packages then generate SQL statements based on the users’ requests.

    The Problem for the DBA

    In order to do good analytics on your multiple data stores you need a good understanding of the data requirements, an understanding of the analytical functions and algorithms available and a high-performance data infrastructure. Regrettably, the number and location of data sources is expanding (both in size and in geography), data sizes are growing, and applications continue to proliferate in number and complexity. How should IT managers support this environment, especially with the most experienced and mature staff nearing retirement?

    Understand also that a large part of reducing the total cost of ownership of these systems is to get Db2 applications to run faster and more efficiently. This usually translates into using fewer CPU cycles, doing fewer I/Os and transporting less data across the network. Since it is often difficult to even identify which applications could benefit from performance tuning, one approach is to automate the detection and correction of tuning issues. This is where machine learning and artificial intelligence can be used to great effect.

    Db2 12 for z/OS and Artificial Intelligence

    Db2 version 12 on z/OS uses the machine learning facilities mentioned above to gather and store SQL query text and access path details, as well as actual performance-related historical information such as CPU time used, elapsed times and result set sizes. This offering, described as Db2 AI for z/OS, analyzes and stores the data in machine learning models, with the model analysis results then being scored and made available to the Db2 Optimizer. The next time a scored SQL statement is encountered, the Optimizer can then use the model scoring data as input to its access path choice algorithm.

    The result should be a reduction in CPU consumption as the Optimizer uses model scoring input to choose better access paths. This then lowers CPU costs and speeds application response times. A significant advantage is that using AI software does not require the DBA to have data science skills or deep insights into query tuning methodologies. The Optimizer now chooses the best access paths based not only on SQL query syntax and data distribution statistics but on modelled and scored historical performance.

    This can be particularly important if you store data in multiple places. For example, many analytical queries against big data require concurrent access to certain data warehouse tables. These tables are commonly called dimension tables, and they contain the data elements usually used to control subsetting and aggregation. For example, in a retail environment consider a table called StoreLocation that enumerates every store and its location code. Queries against store sales data may wish to aggregate or summarize sales by Location; hence, the StoreLocation table will be used by some big data queries. In this environment it is common to take the dimension tables and copy them regularly to the big data application. In the IBM world this location is the IBM Db2 Analytics Accelerator (IDAA).

    Now think about SQL queries from both operational applications, data warehouse users and big data business analysts. From Db2's perspective, all these queries are equal, and are forwarded to the Optimizer. However, in the case of operational queries and warehouse queries they should most likely be directed to access the StoreLocation table in the warehouse. On the other hand, the query from the business analyst against big data tables should probably access the copy of the table there. This results in a proliferations of potential access paths, and more work for the Optimizer. Luckily, Db2 AI for z/OS can provide the Optimizer the information it needs to make smart access path selections.

    How It Works

    The sequence of events in Db2 AI for z/OS (See Reference 2) is generally the following:

  • During a bind, rebind, prepare or explain operation, an SQL statement is passed to the Optimizer;
  • The Optimizer chooses the data access path; as the choice is made, Db2 AI captures the SQL syntax, access path choice and query performance statistics (CPU used, etc.) and passes it to a "learning task";
  • The learning task, which can be executed on a zIIP processor (a non-general-purpose CPU core that does not factor into software licensing charges), interfaces with the machine learning software (MLz Model Services) to store this information in a model;
  • As the amount of data in each model grows, the MLz Scoring Service (which also can be executed on a zIIP processor) analyzes the model data and scores the behavior;
  • During the next bind, rebind, prepare or explain, the Optimizer now has access to the scoring for SQL models, and makes appropriate changes to access path choices.
  • There are also various user interfaces that give the administrator visibility to the status of the accumulated SQL statement performance data and model scoring.

    Summary

    IBM's machine learning for zOS (MLz) offering is being used to great effect in Db2 version 12 to improve the performance of analytical queries as well as operational queries and their associated applications. This requires management attention, as you must verify that your business is prepared to consume these ML and AI conclusions. How will you measure the costs and benefits of using machine learning? Which IT support staff must be tasked to reviewing the result of model scoring, and perhaps approving (or overriding) the results? How will you review and justify the assumptions that the software makes about access path choices?

    In other words, how well do you know your data, its distribution, its integrity and your current and proposed access paths? This will determine where the DBAs spend their time in supporting analytics and operational application performance.

    # # #

    Reference 1

    John Campbell, IBM Db2 Distinguished EngineerFrom "IBM Db2 AI for z/OS: Boost IBM Db2 application performance with machine learning"https://www.worldofdb2.com/events/ibm-db2-ai-for-z-os-boost-ibm-db2-application-performance-with-ma

    Reference 2

    Db2 AI for z/OShttps://www.ibm.com/support/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html

    See all articles by Lockwood Lyon


    IBM outlines trans-Tasman channel priorities in 2019 | killexams.com real questions and Pass4sure dumps

    IBM partners are gearing up for a year of increased enablement, support and joint customer opportunities, as part of deepened channel plans across Australia and New Zealand.

    According to Alison Freeman - general manager of global business partners A/NZ at IBM - the vendor is doubling down on its partner ecosystem on both sides of the Tasman, amid plans to drive more software-as-a-service (SaaS) opportunities through the channel.

    "Our priorities for this year are very clear client segmentation, particularly from a software perspective on how and where we go to market with our partners,” Freeman said. “There’s a big focus on embedded solution agreements - where partners are developing solutions with our technology.

    "We’ve had some great success and growth there in the past year. We’re getting the whole business behind achieving double digit growth for our partners in 2019, that’s our focus."

    Last year, Big Blue wanted partners to have a larger handle on commercial business opportunities, an area the vendor will continue working through with partners specific to software.

    "There’s a lot of work going into that this year," added Freeman, when speaking to ARN on the sidelines of PartnerWorld at Think in San Francisco. "We did that with a number of accounts last year, and this is a continuation.

    "It’s a shift that allows partners to invest and bring on the right people and skills. We’re working with them by client, on which partners we shift, based on their skills. It’s a big move and that’s how we’ll help them grow, which will also help us grow."

    Another point of focus for Freeman this year, is creating a better alignment with global system integrators (GSIs) such as KPMG, Wipro, HCL and DXC Technology.

    This move may raise some intrigue over the competitiveness between IBM and such GSIs, but Freeman said the vendor was looking at how it can strengthen its go-to-market strategy in other areas of the business.

    Partner enhancements

    Another particular area that IBM is concentrating on is centred around providing more enablement and skills for partners, Freeman said.

    “We’re providing the tools to enable our partners in the same way that we skill our IBM teams,” she said. “We’re pushing better enablement, better connection with our partners, protection under deal registration and much quicker pricing."

    The tech giant formally rolled out new initiatives, including its Watson-based collaboration engine called IBM Business Partner Connect - which A/NZ partners piloted last year; My PartnerWorld; new software deal registration and IBM Power Systems, LinuxONE and Z ‘In It to Win It’ incentives.

    In San Francisco, John Teltsch - partner ecosystem general manager at IBM - revealed that partners created more than 84,000 opportunities and drove over US$14 billion in revenue during 2018.

    Such growth was triggered by the migration of multiple tools to My Sales Activity (MySA), a single portal that helps partners manage their business with the vendor.

    Enhancements were also made to the PartnerWorld on-boarding process, cutting the time to on-board new partners by 70 per cent in the process, resulting in the addition of 13,000 net new ecosystem partners globally in 2018.

    “From a partner point of view, we’re seeing a lot of growth in security, SaaS solutions, as well as the Internet of Things," Freeman added.

    "We’re very strong in those areas. From a systems point of view, we’re seeing growth in storage and that’s another area where we’ll look to bring on new partners."

    Julia Talevski attended PartnerWorld at Think in San Francisco as a guest of IBM

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    Compuware Announces Day One Support for IBM CICS Transaction Server for z/OS V5.5 | killexams.com real questions and Pass4sure dumps

    DETROIT, Dec. 14, 2018 (GLOBE NEWSWIRE) — Compuware Corporation, the world’s leading mainframe-dedicated software company, is pleased to announce Day One support for IBM CICS Transaction Server (TS) for z/OS V5.5.

    Compuware worked in collaboration with IBM to ensure that its solutions—including Abend-AID, File-AID, Strobe, Topaz and Xpediter—work seamlessly for z/OS customers installing or upgrading to this newest version of CICS TS.

    New capabilities within CICS TS V5.5 include support for JavaScript Node.js applications; an enhanced CICS Explorer; as well as advancements in systems management and enhanced security.

    “Compuware has an extensive history of ensuring our products work seamlessly with IBM releases and the release of CICS Transaction Server V5.5 is no exception,” said Sam Knutson, Vice President of Product Management for Compuware. “Our solutions excel at helping enterprises include the mainframe in their cross-platform Agile DevOps practices and processes so they can succeed in today’s digital markets.”

    Compuware CorporationCompuware empowers the world’s largest companies to excel in the digital economy by fully leveraging their high-value mainframe investments. We do this by delivering highly innovative solutions that uniquely enable IT professionals with mainstream skills to manage mainframe applications, data, and platform operations. Learn more at compuware.com.

    Follow us on:

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  • Press ContactKristina LeBlanc, The Medialink Group, [email protected], (508) 930-5636Mary McCarthy, Public Relations Manager, Compuware, [email protected], (313) 227-7088

    For Sales and Marketing InformationCompuware Corporation, One Campus Martius, Detroit MI 48226, 800-521-9353, compuware.com.

    Copyright © 2018, Compuware Corporation. All rights reserved. The Compuware products and services listed within this release are trademarks or registered trademarks of Compuware Corporation.

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    IBM 000-601 Exam (IBM System z Solution sales V5) Detailed Information



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