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000-601 exam Dumps Source : IBM System z Solution sales V5
Test Code : 000-601
Test designation : IBM System z Solution sales V5
Vendor designation : IBM
: 77 actual Questions
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IBM IBM System z Solution
these days precise-time database enterprise ScyllaDB introduced a brand modern 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 paraphernalia Servers, corporations can compass modern degrees of efficiency while too cutting back the footprint, can impregnate and complexity of their systems.
ScyllaDB has designed a magnificent allotted database that extends the performance merits we’ve brought with their multi-core POWER9 processors,” said Tim Vincent, IBM Fellow and vp of IBM Cognitive techniques. “The admixture of the Scylla NoSQL database and their vigour system Servers allows for their shared customers to scale up their techniques in preference to perpetually scaling out, developing modern alternatives for records middle consolidation and cost performance.”
This integration builds upon a multi-faceted relationship between ScyllaDB and IBM. In 2016, IBM Compose every started proposing Scylla as a piece 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 employ 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 verve 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, verve 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 retract full potential of the pace of the POWER9 processor, riding each I/O and CPU processing in a manner 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 admixture of Scylla and IBM POWER9 promises unprecedented performance, scale, density and efficiency while tremendously simplifying the administrative tribulation of big statistics techniques.”
Scylla database versions with aid for IBM verve programs can breathe institute for down load from the ScyllaDB site.
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In September 2018, IBM introduced a modern product, IBM Db2 AI for z/OS. This ersatz 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 breathe used via subsequent statements.
computing device gaining lore 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 do employ 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 lore 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 material processor usage, I/O processing, reminiscence paging and so on. IBM MLz can assemble and reserve these records over time, and construct and coach models of gadget habits, ranking those behaviors, identify patterns now not with no ache foreseen by people, forward key performance warning signs (KPIs) after which feed the mannequin outcomes lower back into the device to absorb an outcome on paraphernalia configuration alterations that can enrich efficiency.
The subsequent step become to implement this suite to research Db2 performance facts. One answer, referred to as the IBM Db2 IT Operational Analytics (Db2 ITOA) reply 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 valuable component in ordinary utility health and performance, IBM estimates that the DBA back carcass 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 necessity to back one or more expansive 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 being 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 breathe accessed and develops an inventory of candidate statistics entry paths. These entry paths can consist of indexes, table scans, numerous desk combine methods and others. in the facts warehouse and massive facts environments there are continually further decisions attainable. One of those is the being 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 impregnate in this context capability a weighted summation of resource utilization together with CPU, I/O, recollection 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 embrace software suites that permit the business analyst to employ a graphical interface to build and maneuver a miniature statistics model of the records they necessity to analyze. The packages then generate SQL statements in line with the clients’ requests.
The problem for the DBA
as a passage to enact noble analytics in your assorted statistics stores you necessity a pretty noble 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 quiet IT managers back this environment, certainly with essentially the most skilled and ripen staff nearing retirement?
bear in mind additionally that a expansive piece of decreasing the full impregnate of ownership of these techniques is to congregate Db2 purposes to race 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 edge 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 too breathe used to super impact.
Db2 12 for z/OS and ersatz Intelligence
Db2 version 12 on z/OS uses the machine gaining lore 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 employ the mannequin scoring records as enter to its entry direction option algorithm.
The outcomes may quiet breathe 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 expansive competencies is that the usage of AI application doesn't require the DBA to absorb information science competencies or deep insights into question tuning methodologies. The Optimizer now chooses the foremost entry paths based no longer most efficient on SQL question syntax and information distribution statistics but on modelled and scored historic efficiency.
This will too breathe specially vital if you shop facts in assorted locations. as an instance, many analytical queries against massive information require concurrent entry to obvious facts warehouse tables. These tables are often called dimension tables, and that they hold the records elements constantly used to control subsetting and aggregation. as an example, in a retail environment deem a desk known as StoreLocation that enumerates each shop and its district code. Queries in opposition t reserve revenue facts may too wish to admixture or summarize sales via area; hence, the StoreLocation table should breathe used by means of some massive information queries. in this atmosphere it is regular to retract the dimension tables and duplicate them constantly to the expansive statistics software. within the IBM world this district is the IBM Db2 Analytics Accelerator (IDAA).
Now assume about SQL queries from each operational functions, statistics warehouse users and massive records enterprise analysts. From Db2's perspective, every these queries are equal, and are forwarded to the Optimizer. despite the fact, within the case of operational queries and warehouse queries they may quiet undoubtedly breathe directed to entry the StoreLocation table within the warehouse. in spite of this, the query from the enterprise analyst in opposition t expansive 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 do sane entry route selections.
the passage it Works
The sequence of routine in Db2 AI for z/OS (See Reference 2) is commonly perquisite here:
all through a bind, rebind, prepare or define 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 lore of task";
The studying assignment, which can too breathe 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 too can breathe 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 too a considerable number of person interfaces that give the administrator visibility to the popularity of the gathered SQL commentary efficiency data and model scoring.
IBM's machine gaining lore 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 devour these ML and AI conclusions. How will you measure the fees and advantages of the usage of laptop discovering? Which IT back workforce necessity to breathe 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 alert your records, its distribution, its integrity and your existing and proposed access paths? this could examine the station the DBAs expend their time in helping analytics and operational utility performance.
# # #
John Campbell, IBM Db2 exceptional EngineerFrom "IBM Db2 AI for z/OS: enlarge 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
Db2 AI for z/OShttps://www.ibm.com/assist/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html
See every articles via Lockwood Lyon
big apple, the expansive apple and PALO ALTO, CA--(Marketwired - Aug 17, 2015) - MongoDB these days deepened its partnership with IBM, announcing plans to proffer back for MongoDB's items on IBM z methods mainframe. This aid reflects the wonderful 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 modern 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 absorb race valuable functions on IBM mainframes. Their rush to back IBM z techniques is a testament to their commitment to their clients and consumers to do MongoDB available on every primary systems. With this announcement, groups can now construct and race modern, mission-important applications on proven mainframe technologies."
MongoDB is working closely with IBM to engineer MongoDB enterprise Server to breathe optimized for Linux on z systems and the modern LinuxONE techniques. As piece of the settlement, MongoDB's world back and engineering firm will proceed to collaborate with IBM to breathe positive business continuity for their 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," celebrated Ross Mauri, typical manager, IBM z systems. "Now, agencies can amalgamate the imaginitive features of MongoDB with the unrivaled efficiency of the z methods mainframe to create solutions with modern ranges of pace, scale and flexibility."
About MongoDB MongoDB is the subsequent-generation database that helps agencies seriously change their industries by using harnessing the power of statistics. the world's most subtle organizations, from reducing-facet startups to the biggest corporations, employ MongoDB to create applications not ever before viable at a fraction of the impregnate of legacy databases. MongoDB is the quickest-turning out to breathe database ecosystem, with over 10 million downloads, lots of customers, and over 1,000 know-how and service companions. gain lore of extra at www.mongodb.com.
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IBM System z Solution sales V5
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In September 2018, IBM announced a modern product, IBM Db2 AI for z/OS. This ersatz intelligence engine monitors data access patterns from executing SQL statements, uses machine learning algorithms to choose on optimal patterns and passes this information to the Db2 query optimizer for employ 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 portray 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 employ 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, recollection 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 strike system configuration changes that can improve performance.
The next step was to implement this suite to dissect 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 universal Db2 subsystem performance is an valuable factor in overall application health and performance, IBM estimates that the DBA back staff spends 25% or more of its time, " ... fighting access path problems which understanding 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 back one or more expansive 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 being 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 breathe accessed and develops a list of candidate data access paths. These access paths can embrace indexes, table scans, various table combine methods and others. In the data warehouse and expansive data environments there are usually additional choices available. One of these is the being of summary tables (sometimes called materialized query tables) that hold 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, recollection and other resources. Finally, the Optimizer takes the lowest cost access path, stores it in recollection (and, optionally, in the Db2 Directory) and begins access path execution.
Big data and data warehouse operations now embrace software suites that allow the business analyst to employ a graphical interface to build and maneuver 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 enact noble analytics on your multiple data stores you necessity a noble 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 back this environment, especially with the most experienced and ripen staff nearing retirement?
Understand too that a big piece of reducing the total cost of ownership of these systems is to congregate Db2 applications to race 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 ersatz intelligence can breathe used to august effect.
Db2 12 for z/OS and ersatz Intelligence
Db2 version 12 on z/OS uses the machine learning facilities mentioned above to congregate 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 employ the model scoring data as input to its access path choice algorithm.
The result should breathe 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 edge is that using AI software does not require the DBA to absorb 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 breathe particularly valuable if you store data in multiple places. For example, many analytical queries against expansive data require concurrent access to inescapable data warehouse tables. These tables are commonly called dimension tables, and they hold the data elements usually used to control subsetting and aggregation. For example, in a retail environment deem 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 breathe used by some expansive data queries. In this environment it is common to retract the dimension tables and copy them regularly to the expansive data application. In the IBM world this location is the IBM Db2 Analytics Accelerator (IDAA).
Now assume about SQL queries from both operational applications, data warehouse users and expansive data business analysts. From Db2's perspective, every these queries are equal, and are forwarded to the Optimizer. However, in the case of operational queries and warehouse queries they should most likely breathe directed to access the StoreLocation table in the warehouse. On the other hand, the query from the business analyst against expansive 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 do 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 define 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 breathe 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 too can breathe 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 confiscate changes to access path choices.
There are too various user interfaces that give the administrator visibility to the status of the accumulated SQL statement performance data and model scoring.
IBM's machine learning for zOS (MLz) offering is being used to august outcome 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 back staff must breathe 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 enact you know your data, its distribution, its integrity and your current and proposed access paths? This will determine where the DBAs expend their time in supporting analytics and operational application performance.
# # #
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
Db2 AI for z/OShttps://www.ibm.com/support/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html
See every articles by Lockwood Lyon
IBM partners are gearing up for a year of increased enablement, back and joint customer opportunities, as piece of deepened channel plans across Australia and modern Zealand.
According to Alison Freeman - universal manager of global business partners A/NZ at IBM - the vendor is doubling down on its confederate 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 they Go to market with their partners,” Freeman said. “There’s a expansive focus on embedded solution agreements - where partners are developing solutions with their technology.
"We’ve had some august success and growth there in the past year. We’re getting the all business behind achieving double digit growth for their partners in 2019, that’s their focus."
Last year, expansive Blue wanted partners to absorb a larger maneuver on commercial business opportunities, an district 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 assume in San Francisco. "We did that with a number of accounts terminal year, and this is a continuation.
"It’s a shift that allows partners to invest and bring on the perquisite people and skills. We’re working with them by client, on which partners they shift, based on their skills. It’s a expansive rush and that’s how we’ll aid them grow, which will too aid 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 rush 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.
Another particular district that IBM is concentrating on is centred around providing more enablement and skills for partners, Freeman said.
“We’re providing the tools to enable their partners in the selfsame passage that they skill their IBM teams,” she said. “We’re pushing better enablement, better connection with their partners, protection under deal registration and much quicker pricing."
The tech giant formally rolled out modern initiatives, including its Watson-based collaboration engine called IBM business confederate Connect - which A/NZ partners piloted terminal year; My PartnerWorld; modern software deal registration and IBM Power Systems, LinuxONE and Z ‘In It to Win It’ incentives.
In San Francisco, John Teltsch - partner ecosystem universal 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 unique portal that helps partners manage their business with the vendor.
Enhancements were too made to the PartnerWorld on-boarding process, cutting the time to on-board modern partners by 70 per cent in the process, resulting in the addition of 13,000 net modern ecosystem partners globally in 2018.
“From a confederate 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 tenacious in those areas. From a systems point of view, we’re seeing growth in storage and that’s another district where we’ll explore to bring on modern partners."
Julia Talevski attended PartnerWorld at assume in San Francisco as a guest of IBM
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DETROIT, Dec. 14, 2018 (GLOBE NEWSWIRE) — Compuware Corporation, the world’s leading mainframe-dedicated software company, is pleased to publish Day One back 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.
“Compuware has an extensive history of ensuring their 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 outdo at helping enterprises embrace 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 outdo in the digital economy by fully leveraging their high-value mainframe investments. They enact 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.
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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
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Copyright © 2018, Compuware Corporation. every rights reserved. The Compuware products and services listed within this release are trademarks or registered trademarks of Compuware Corporation.
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