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C4070-603 IBM System z Sales Solutions V6

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C4070-603 exam Dumps Source : IBM System z Sales Solutions V6

Test Code : C4070-603
Test designation : IBM System z Sales Solutions V6
Vendor designation : IBM
real questions : 86 actual Questions

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IBM IBM System z Sales

IBM sets forth with a powerful cybersecurity message | killexams.com actual Questions and Pass4sure dumps

I simply got lower back from attending IBM account in San Francisco. although it became a brief commute across the nation, i used to breathe inundated with IBM’s imaginative and prescient, covering topics from A (i.e. synthetic intelligence) to Z (i.e. gear Z) and every diminutive thing in between.

regardless of the wide-ranging discussion, IBM’s main focal point changed into on three areas: 1) hybrid cloud, 2) advanced analytics, and 3) safety. for instance, IBM’s hybrid cloud discussion based on digital transformation and leaned closely on its purple Hat acquisition, while superior analytics covered synthetic intelligence (AI), cognitive computing (Watson), neural networks, and so forth. To array its capabilities in these areas, IBM paraded out valued clientele corresponding to Geico, Hyundai credit service provider, and Santander bank, who're making a pot on IBM for video game-altering digital transformation projects.

IBM's cybersecurity plans

As for cybersecurity, listed below are a number of of my take-aways about IBM's plans:

  • not surprisingly, IBM is all-in on cybersecurity capabilities, which now account for more than 50 % of its cybersecurity revenue. in response to ESG analysis (and a lot of other trade sources), cybersecurity services boom will continue to outpace products due to the global cybersecurity competencies shortage. (observe: i'm an employee of ESG.) IBM is banking on this vogue by using including personnel, investing in backend methods and strategies, and rolling out fresh service offerings. for example, IBM is working with partners on a managed capabilities program where native partners improvement from IBM’s global supplies, analytics, and danger intelligence. standard, IBM has a unique probability to divide itself from the pack and could become the de facto enterprise cybersecurity features leader.
  • Most cybersecurity specialists account of IBM QRadar as a SIEM, competing with the likes of ArcSight, LogRhythm, and Splunk. while this standpoint is right, it minimizes its price. QRadar is in fact a safety operations and analytics platform structure (SOAPA). shoppers can expend QRadar as a safety operations nexus, adding functionality similar to network site visitors evaluation (NTA), vulnerability administration (VM), and user conduct analytics (UBA) to the core equipment. What’s more, QRadar presents a number of helper purposes, similar to DNS analytics, most of that are free. eventually, QRadar has thousands of shoppers around the world. IBM has some work forward here – it must profit cybersecurity street cred by advertising and marketing QRadar as a SOAPA providing and international cybersecurity group, as opposed to a undeniable historical SIEM.
  • IBM is embracing security “from the cloud.” for example, QRadar on cloud (QROC) profits grew over 20 %, demonstrating that shoppers want the value of QRadar with out the infrastructure baggage of on-premises collectors, databases, servers, and so forth. IBM is additionally poised to roll out its IBM protection connected (ICS) platform in Q2. per its minimalist communications, IBM hasn’t trumpeted the ICS initiative, but in my humble opinion, it represents a major exchange in direction. For ICS, IBM rewrote its safety applications as microservices to construct a foundation of cloud integration and scale. hence, ICS purposes will grow from discrete SaaS choices to an built-in cloud-scale cybersecurity structure over time. Oh, and ICS will Come with lots of services alternatives for every diminutive thing from body of workers augmentation to outsourcing. ICS has the skills to breathe a big deal for overwhelmed CISOs with global responsibilities and the want for huge cybersecurity scale.
  • Resilient is an enterprise-classification security operations platform. When IBM bought Resilient techniques just a few years in the past, it won a know-how leader however variety of ceded the soar buzz to different carriers. this is a disgrace. Resilient may likewise require slightly more work than a few of its competitors, however I determine that valued clientele are the expend of Resilient to re-architect their safety operations processes and establish actual and measurable safety operations metrics. To me, here is where security operations systems must retrograde – past quick automation and orchestration wins to anchoring security routine re-engineering.
  • four techniques IBM can enhance its cybersecurity game

    IBM’s protection portfolio is pretty solid, and the enterprise appears to breathe more energized than in the past. After attending IBM think, I accomplish hold a pair of cybersecurity suggestions for folks in Armonk and Cambridge, Massachusetts:

  • whereas IBM believe has a tenacious hybrid cloud theme, the IBM protection hybrid cloud yarn remains disjointed – an identification yarn perquisite here, a data security yarn there, etc. This results in IBM being outflanked through cloud-savvy protection startups. IBM wants a cohesive, tightly built-in product offering and messaging framework perquisite here.
  • IBM’s random administration capabilities are tenacious however slightly hidden. in accordance with recent ESG research, there is a becoming cyber random management pocket between what enterprise executives want and what cybersecurity gurus can deliver. Given its trade potential and relationships, IBM should soundless breathe doing extra in the cyber risk management space – at the product and functions degree.
  • closely regarding #2, cybersecurity is actually a boardroom-level situation – peculiarly for common IBM consumers. I find that there is a disconnect between IBM’s company headquarters of attention on digital transformation, trade solutions, and hybrid clouds and its cybersecurity go-to-market, which is soundless established within the bits and bytes. again, IBM is in a different location to work out a greater desirable-down approach (i.e. from the trade outright the course down to the technology) and convey business-centric cybersecurity options to purchasers.
  • IBM spent millions of bucks on a Watson for a cybersecurity advertising campaign, but few cybersecurity experts hold a clue about what Watson for cybersecurity is. The matches in Armonk should pump the promoting brakes and dedicate more towards market training via working with expert companies corresponding to ISSA, ISC2, SANS, the Infosec Institute, and so on.
  • In accepted, Armonk necessity to maintain in intellect that the IBM manufacturer is a advertising obstruction when competing for mindshare with providers enjoy CrowdStrike, FireEye, Palo Alto Networks, and so forth. accordingly, IBM security necessity to work more durable and smarter to procure the breathe watchful out.

    Many because of IBM for hosting me in San Francisco this week. I’ll breathe again on the Moscone middle for RSA within the wink of a watch. 


    IBM: Speculating On The Opaque engage Of Blockchain | killexams.com actual Questions and Pass4sure dumps

    summary

    IBM is enforcing a technique wherein in change for helping convey the advantages of blockchain to a growing checklist of industrial clients, it’s taking pictures seat and cloud revenues.

    although, from its monetary reporting, it’s not feasible to examine how much of massive Blue’s exact line is attributable to its blockchain activities.

    “demonstrate me” investors are left to invest and that i am supplementing their complete location in IBM with short-dated calls expiring simply after successive salary releases.

    Over a 12 months and a half in the past, I wrote my first article about blockchain and massive Blue (IBM) entitled, “may This breathe IBM’s earnings Catalyst?” In it, i discussed a number of such initiatives including a world container transport blockchain led via Maersk (OTCPK:AMKBY), a fruit and vegetable consortium led by using Walmart (WMT), and a exchange finance blockchain involving quite a lot of strange banks, HSBC (HSBC) amongst them. These programs stand to bring wonderful – “sooner, enhanced, more cost-effective” – effects for his or her participants:

  • faster time-to-market from continuous straight-via-processing whereas cutting back/doing away with boundaries and pink tape associated with business-as-usual.
  • more suitable first-class on account of much less infection and spoilage of perishable products.
  • more affordable as in lessen operations and administrative charges when guide, non-commonplace, paper-based mostly forms is replaced with uniform, expertise-brought on, interactions.
  • In that first article, I postulated that IBM would monetize blockchain from seat licenses not diverse to Bloomberg’s very successful model. In a comply with-up piece, I linked to an IBM page that established the validity of this inspiration with fees correlating to the measurement of the blockchain participant; larger groups paying greater, dinky ones gaining free access to the platform. essential mass and extensive participation are essential to multilateral processing represented by means of blockchain.

    enterprise keeps Coming

    issues haven’t stood nonetheless. Blockchain initiatives involving IBM are proliferating straight away and in areas that I could not hold imagined in 2017. okay, so Abu Dhabi’s ADNOC oil blockchain turned into less complicated to foresee. however, past my sphere of vision had been a healthcare blockchain and an additional having to accomplish with scientific drug trials, blockchains for overseas change and derivatives, a gear to video array groundwater usage, yet another worried with mining, and a blockchain for digital advertising. infrequently a week goes through where they don’t remark fresh blockchain initiatives spawned by using corporations desperate to work with IBM; at the least dozens of programs are underway involving a entire bunch of major groups.

    but Its impact is Opaque

    transport, meals, finance, oil, healthcare, medication, strange exchange, derivatives, water, mining, advertising – obviously it isn’t misplaced on any person that these are areas of big economic activity. For me, this makes it even more fascinating that nowhere of their numbers does IBM document earnings at once brought on by blockchain, neither with seat licenses, nor cloud revenues, nor cost-added services corresponding to AI. No, IBM has chosen to preserve blockchain-derived revenues opaque. They hold no course of connecting the dots.

    I don’t understand why IBM has assumed this posture. youngsters, as adversarial to obsessing about it, I actually hold chosen to flux on to the query, “Given this opaqueness coupled with the big odds significance of blockchain, how could one factor it into an IBM investment choice?” answer, actually: “with the aid of speculating, as in with acceptable bets that blockchain will power results in different areas for which IBM does account.”

    Speculating with Calls

    in opposition t massive Blue’s mainly afflicted legacy of salary growth, cautious buyers say, “show me; present it”. To this I add, “sure, however they don’t want to miss out if issues continue to breathe elevated trait as they suspect they might.”

    So, ultimate month they begun an scan wherein I bought very short-dated at-the-cash calls usurp earlier than IBM’s quarterly revenue free up. And, barring fresh information or sparkling insights, I diagram to accomplish so once more next quarter and successively thereafter.

  • short-dated calls as a result of they’re affordable. For a relatively wee investment, you'll ply a comparatively tremendous variety of shares such that: a) If blockchain doesn’t deliver the downstream revenues, one might likewise crash and burn on their calls however as a minimum their loss is contained, but b) If blockchain does bring, one’s gain can breathe massive.
  • At-the-money best because my head hurts doing the math on options bought in or out-of-the-cash.
  • Quarterly in succession because IBM should merit returned the believe of skeptics and there is no judgement to believe that this could entangle location any faster than did the loss of self-confidence in IBM.
  • exceptionally, and with apologies for less than one data aspect, the morning before IBM’s end-of-day income announcement of January twenty second, for $3.20 I purchased calls expiring Feb 1st. now not 24 hours later, I closed out the contracts for $eight.sixty six realizing a capitalize of one hundred seventy%.

    A side potential of buying and selling in IBM alternatives is that, in contrast to most others that expire but as soon as a month, IBM’s calls (and puts) are available with weekly expirations. This potential that one necessity not “pay for more time price” than is really warranted. If IBM’s salary are due out, say, on a fourth Tuesday, you can soundless purchase options expiring on that Friday; but pay attention as a result of things transpire perquisite away.

    options buying and selling is risky which is why brokerages pre-approve investors for it. As skilled as i am within the securities trade and as an investor, I simplest exchange alternate options a few times each and every yr when I actually hold a compelling hunch. furthermore, I try this company not through my discount broking service-broking, but somewhat, through their full-carrier B-D wherein I consult with a relied on and professional professional through the lifestyles-cycle of every trade. I certainly not wager greater than they are able to hold the funds for to lose and, on occasion, I lose it all, i.e. calls expiring nugatory. My adviser is watchful of I hold in intellect and might ply the dangers.

    it's obtrusive that IBM should re-set up its economic credibility by means of delivering consistent salary increase. Of the 22 analysts that commemorate the stock, wholly half hold it as a “dangle” with the community forecasting a median charge at about the location the stock is trading today. because it is with blockchain and other unique developments, I’m more confident than most in IBM which is why they are likewise lengthy the inventory. That talked about, I wouldn’t intellect boosting their recrudesce which is why i will breathe able to pursue this convene strategy for a long time.

    Disclosure: i am/we are long IBM. I wrote this text myself, and it expresses my very own opinions. i'm not receiving compensation for it (apart from from seeking Alpha). I don't hold any trade relationship with any enterprise whose inventory is outlined in this article.

    further disclosure: always accomplish your own due diligence in session with an authorized and able economic adviser who is watchful your wonderful needs and puts your interests ahead of their own. tolerate in mind, there are added concerns in owning international securities including these associated with ADR sponsorship, purchasing and selling the pinks, overseas withholding taxes on dividends, and charges. (All my proceeds from contributing to SA retrograde to charity.)

    SeekingAlpha

    IBM Db2 question Optimization using AI | killexams.com actual Questions and Pass4sure dumps

    In September 2018, IBM announced a fresh product, IBM Db2 AI for z/OS. This synthetic intelligence engine displays facts access patterns from executing SQL statements, uses computer discovering algorithms to opt for top-quality patterns and passes this counsel to the Db2 question optimizer for expend via subsequent statements.

    desktop discovering on the IBM z Platform

    In might likewise of 2018, IBM introduced edition 1.2 of its desktop gaining information of for z/OS (MLz) product. here's a hybrid zServer and cloud utility suite that ingests efficiency records, analyzes and builds models that characterize the health fame of a lot of symptoms, screens them over time and gives actual-time scoring capabilities.

    a few aspects of this product providing are aimed at supporting a group of model builders and executives. as an example:

  • It helps distinctive programming languages corresponding to Python, Scala and R. This enables records modelers and scientists to build expend of a language with which they're well-known;
  • A graphical person interface referred to as the visual model Builder guides model developers devoid of requiring tremendously-technical programming capabilities;
  • It includes assorted dashboards for monitoring mannequin results and scoring capabilities, in addition to controlling the gadget configuration.
  • This computing device learning suite became at first aimed toward zServer-based mostly analytics purposes. probably the most first glaring decisions was zSystem efficiency monitoring and tuning. gadget administration Facility (SMF) statistics which are instantly generated by using the working device provide the uncooked facts for gear resource consumption reminiscent of material processor utilization, I/O processing, reminiscence paging etc. IBM MLz can assemble and reclaim these facts over time, and construct and train fashions of device behavior, rating these behaviors, identify patterns now not effortlessly foreseen by means of people, help key performance warning signs (KPIs) after which feed the model consequences lower back into the gear to hold an outcome on gadget configuration changes that may enhance efficiency.

    The subsequent step turned into to implement this suite to investigate Db2 performance facts. One answer, known as the IBM Db2 IT Operational Analytics (Db2 ITOA) reply template, applies the computer gaining information of expertise to Db2 operational facts to gain an knowing of Db2 subsystem fitness. it might probably dynamically construct baselines for key efficiency symptoms, supply a dashboard of these KPIs and provides operational body of workers precise-time insight into Db2 operations.

    whereas everyday Db2 subsystem performance is a crucial component in typical software health and performance, IBM estimates that the DBA aid body of workers spends 25% or more of its time, " ... fighting entry route problems which judgement efficiency degradation and repair affect.". (See Reference 1).

    AI comes to Db2

    consider the plight of modern DBAs in a Db2 environment. In state-of-the-art IT world they must aid one or extra massive information purposes, cloud utility and database features, software installation and configuration, Db2 subsystem and utility performance tuning, database definition and management, catastrophe healing planning, and extra. query tuning has been in existence on account that the origins of the database, and DBAs are usually tasked with this as neatly.

    The coronary heart of query path analysis in Db2 is the Optimizer. It accepts SQL statements from applications, verifies authority to access the records, experiences the places of the objects to breathe accessed and develops an inventory of candidate records entry paths. These access paths can embrace indexes, desk scans, a number of desk combine strategies and others. within the data warehouse and big statistics environments there are continually extra selections obtainable. One of those is the existence of abstract tables (once in a while called materialized question tables) that comprise pre-summarized or aggregated records, for this judgement allowing Db2 to remain away from re-aggregation processing. one more preference is the starjoin entry direction, habitual within the statistics warehouse, the location the order of table joins is changed for performance explanations.

    The Optimizer then reviews the candidate access paths and chooses the access course, "with the bottom charge." impregnate during this context skill a weighted summation of useful resource usage including CPU, I/O, memory and different supplies. eventually, the Optimizer takes the lowest cost entry direction, outlets it in memory (and, optionally, within the Db2 directory) and starts entry path execution.

    large statistics and facts warehouse operations now consist of application suites that permit the enterprise analyst to expend a graphical interface to construct and exploit a miniature facts mannequin of the information they wish to analyze. The packages then generate SQL statements in keeping with the users’ requests.

    The vicissitude for the DBA

    to breathe able to accomplish splendid analytics in your diverse records retailers you necessity an excellent understanding of the statistics requirements, an realizing of the analytical services and algorithms available and a high-efficiency statistics infrastructure. lamentably, the quantity and placement of records sources is expanding (both in measurement and in geography), information sizes are starting to be, and purposes continue to proliferate in number and complexity. How should soundless IT managers waiton this atmosphere, in particular with the most skilled and ripen body of workers nearing retirement?

    consider additionally that a big Part of cutting back the complete cost of possession of these techniques is to procure Db2 functions to dash faster and more successfully. This always interprets into using fewer CPU cycles, doing fewer I/Os and transporting much less information across the network. because it's regularly problematic to even identify which functions could improvement from performance tuning, one routine is to automate the detection and correction of tuning considerations. this is where computer studying and synthetic intelligence will likewise breathe used to awesome impact.

    Db2 12 for z/OS and synthetic Intelligence

    Db2 edition 12 on z/OS makes expend of the computer gaining information of amenities mentioned above to congregate and maintain SQL query textual content and entry path particulars, in addition to exact efficiency-connected historic information such as CPU time used, elapsed instances and influence set sizes. This offering, described as Db2 AI for z/OS, analyzes and outlets the facts in computing device getting to know fashions, with the mannequin evaluation results then being scored and made attainable to the Db2 Optimizer. The next time a scored SQL observation is encountered, the Optimizer can then expend the model scoring records as enter to its access route option algorithm.

    The outcome should breathe a reduction in CPU consumption because the Optimizer uses mannequin scoring input to opt for greater entry paths. This then lowers CPU charges and speeds software response instances. a big information is that the usage of AI software does not require the DBA to hold facts science talents or deep insights into question tuning methodologies. The Optimizer now chooses the finest entry paths based mostly now not best on SQL query syntax and information distribution facts but on modelled and scored historic performance.

    This will likewise breathe chiefly vital if you shop records in dissimilar places. as an instance, many analytical queries in opposition t huge records require concurrent entry to inevitable records warehouse tables. These tables are often known as dimension tables, and that they hold the statistics aspects continually used to control subsetting and aggregation. for example, in a retail ambiance harmonize with a table called StoreLocation that enumerates every shop and its location code. Queries against shop income records might likewise wish to combination or summarize income with the aid of location; therefore, the StoreLocation desk can breathe used with the aid of some big statistics queries. in this environment it is ordinary to entangle the dimension tables and copy them regularly to the big information utility. in the IBM world this location is the IBM Db2 Analytics Accelerator (IDAA).

    Now account about SQL queries from each operational applications, statistics warehouse clients and massive statistics trade analysts. From Db2's point of view, outright these queries are equal, and are forwarded to the Optimizer. besides the fact that children, within the case of operational queries and warehouse queries they should soundless certainly breathe directed to access the StoreLocation table in the warehouse. having said that, the question from the company analyst towards huge statistics tables should likely access the copy of the table there. This consequences in a proliferations of abilities access paths, and greater work for the Optimizer. fortunately, Db2 AI for z/OS can provide the Optimizer the information it needs to build smart entry course choices.

    how it Works

    The sequence of hobbies in Db2 AI for z/OS (See Reference 2) is frequently perquisite here:

  • during a bind, rebind, allocate together or explicate operation, an SQL statement is passed to the Optimizer;
  • The Optimizer chooses the statistics entry course; as the alternative 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 "studying assignment";
  • The getting to know assignment, which will likewise breathe accomplished on a zIIP processor (a non-frequent-goal CPU core that doesn't factor into utility licensing fees), interfaces with the machine discovering utility (MLz mannequin functions) to maintain this information in a mannequin;
  • as the quantity of data in each mannequin grows, the MLz Scoring carrier (which likewise can breathe accomplished on a zIIP processor) analyzes the mannequin data and scores the conduct;
  • throughout the next bind, rebind, prepare or clarify, the Optimizer now has entry to the scoring for SQL fashions, and makes usurp changes to access route selections.
  • There are additionally various person interfaces that supply the administrator visibility to the repute of the accrued SQL commentary performance records and mannequin scoring.

    summary

    IBM's machine learning for zOS (MLz) providing is getting used to outstanding impact in Db2 version 12 to help the efficiency of analytical queries in addition to operational queries and their linked functions. This requires administration attention, as you ought to investigate that your company is prepared to devour these ML and AI conclusions. How will you measure the prices and advantages of the expend of computing device studying? Which IT steer team of workers ought to breathe tasked to reviewing the outcome of mannequin scoring, and maybe approving (or overriding) the results? How will you assessment and justify the assumptions that the application makes about entry direction decisions?

    In different phrases, how neatly did you know your statistics, its distribution, its integrity and your current and proposed entry paths? this can determine the location the DBAs spend their time in assisting analytics and operational software performance.

    # # #

    Reference 1

    John Campbell, IBM Db2 exotic EngineerFrom "IBM Db2 AI for z/OS: raise IBM Db2 utility performance with computer researching"https://www.worldofdb2.com/activities/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


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    IBM outlines trans-Tasman channel priorities in 2019 | killexams.com actual 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 fresh Zealand.

    According to Alison Freeman - universal manager of global trade partners A/NZ at IBM - the vendor is doubling down on its colleague 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 transparent client segmentation, particularly from a software perspective on how and where they retrograde to market with their partners,” Freeman said. “There’s a big focus on embedded solution agreements - where partners are developing solutions with their technology.

    "We’ve had some mighty success and growth there in the past year. We’re getting the entire trade behind achieving double digit growth for their partners in 2019, that’s their focus."

    Last year, big Blue wanted partners to hold a larger ply on commercial business opportunities, an belt 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 ultimate 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 big mosey and that’s how we’ll waiton them grow, which will likewise waiton 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 mosey 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 belt 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 identical course 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 fresh initiatives, including its Watson-based collaboration engine called IBM trade colleague Connect - which A/NZ partners piloted ultimate year; My PartnerWorld; fresh 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 single portal that helps partners manage their trade with the vendor.

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

    “From a colleague 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 belt where we’ll peep to bring on fresh partners."

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

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

    In September 2018, IBM announced a fresh product, IBM Db2 AI for z/OS. This synthetic intelligence engine monitors data access patterns from executing SQL statements, uses machine learning algorithms to resolve on optimal patterns and passes this information to the Db2 query optimizer for expend 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 limn 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 expend 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 impress system configuration changes that can help performance.

    The next step was to implement this suite to resolve 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 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 judgement 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, cataclysm 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 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 big data environments there are usually additional choices available. One of these is the existence 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, 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 embrace software suites that allow the trade analyst to expend a graphical interface to build and exploit 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 accomplish splendid analytics on your multiple data stores you necessity a splendid 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 ripen staff nearing retirement?

    Understand likewise that a big Part of reducing the total cost of ownership of these systems is to procure Db2 applications to dash 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 capitalize from performance tuning, one approach is to automate the detection and correction of tuning issues. This is where machine learning and synthetic intelligence can breathe used to mighty effect.

    Db2 12 for z/OS and synthetic 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 expend the model scoring data as input to its access path preference algorithm.

    The result should breathe a reduction in CPU consumption as the Optimizer uses model scoring input to elect better access paths. This then lowers CPU costs and speeds application response times. A significant odds is that using AI software does not require the DBA to hold 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 Important if you store data in multiple places. For example, many analytical queries against big data require concurrent access to inevitable 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 account 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 big data queries. In this environment it is common to entangle 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 assume about SQL queries from both operational applications, data warehouse users and big data trade analysts. From Db2's perspective, outright 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 trade 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 build 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 explicate operation, an SQL statement is passed to the Optimizer;
  • The Optimizer chooses the data access path; as the preference is made, Db2 AI captures the SQL syntax, access path preference 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 likewise 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 usurp changes to access path choices.
  • There are likewise 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 mighty outcome in Db2 version 12 to help the performance of analytical queries as well as operational queries and their associated applications. This requires management attention, as you must verify that your trade 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 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 accomplish 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


    IBM: More Sluggishness, Surprising tenacious Guidance | killexams.com actual questions and Pass4sure dumps

    No result found, try fresh keyword!With flattish sales in the cognitive solutions trade and modest ... fell 3% to $8.93 billion as revenues in the Systems trade more or less collapsed by 21% to $2.62 billion following the "IBM Z ...


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