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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
: 86 true Questions
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IBM IBM System z Sales
I simply got lower back from attending IBM consider 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. tackle 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 simulated intelligence (AI), cognitive computing (Watson), neural networks, and so forth. To panoply its capabilities in these areas, IBM paraded out valued clientele corresponding to Geico, Hyundai credit service provider, and Santander bank, who're making a bet 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 business 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 unique 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 sunder itself from the pack and could become the de facto enterprise cybersecurity features leader.
Most cybersecurity specialists consider 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 exercise 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 toil 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 further 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 moreover require slightly more toil than a few of its competitors, however I ascertain that valued clientele are the exercise 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 retract – past quick automation and orchestration wins to anchoring security manner 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 effect maintain a pair of cybersecurity suggestions for folks in Armonk and Cambridge, Massachusetts:
whereas IBM believe has a stout hybrid cloud theme, the IBM protection hybrid cloud tale remains disjointed – an identification tale privilege here, a data security tale 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 privilege here.
IBM’s random administration capabilities are stout however slightly hidden. in accordance with recent ESG research, there is a becoming cyber random management cavity between what enterprise executives want and what cybersecurity gurus can deliver. Given its trade potential and relationships, IBM should quiet 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, business solutions, and hybrid clouds and its cybersecurity go-to-market, which is quiet established within the bits and bytes. again, IBM is in a different region to toil out a greater desirable-down approach (i.e. from the business consummate the artery 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 maintain 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 requisite to retain in intuition that the IBM manufacturer is a advertising barrier when competing for mindshare with providers like CrowdStrike, FireEye, Palo Alto Networks, and so forth. accordingly, IBM security requisite to toil more durable and smarter to pick up the breathe vigilant 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 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 practicable 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 replete region 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 exotic 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 diminish 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 maintain 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 effect with scientific drug trials, blockchains for overseas change and derivatives, a tackle to video panoply groundwater usage, yet another worried with mining, and a blockchain for digital advertising. infrequently a week goes through where they don’t see unique blockchain initiatives spawned by using corporations desperate to toil with IBM; at the least dozens of programs are underway involving a whole bunch of major groups.
but Its impact is Opaque
transport, meals, finance, oil, healthcare, medication, exotic 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 maintain no artery of connecting the dots.
I don’t understand why IBM has assumed this posture. youngsters, as adversarial to obsessing about it, I actually maintain chosen to current 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; expose it”. To this I add, “sure, however they don’t want to miss out if issues continue to breathe towering quality as they suspect they might.”
So, final month they begun an scan wherein I bought very short-dated at-the-cash calls appropriate earlier than IBM’s quarterly revenue free up. And, barring unique information or sparkling insights, I blueprint to effect so once more next quarter and successively thereafter.
short-dated calls as a result of they’re affordable. For a relatively diminutive investment, you'll handle a comparatively tremendous variety of shares such that: a) If blockchain doesn’t deliver the downstream revenues, one might moreover crash and char 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 win returned the believe of skeptics and there is no antecedent to believe that this could select region any faster than did the loss of self-possession 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 requisite not “pay for more time price” than is really warranted. If IBM’s salary are due out, say, on a fourth Tuesday, you can quiet purchase options expiring on that Friday; but pay attention as a result of things transpire privilege 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 maintain 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 maintain the funds for to lose and, on occasion, I lose it all, i.e. calls expiring nugatory. My adviser is vigilant of I maintain in intuition and might handle 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 celebrate the stock, wholly half maintain it as a “dangle” with the community forecasting a median cost at about the region 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 moreover lengthy the inventory. That talked about, I wouldn’t intuition boosting their revert which is why i will breathe able to pursue this summon 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 maintain any business relationship with any enterprise whose inventory is outlined in this article.
further disclosure: always effect your own due diligence in session with an authorized and able economic adviser who is vigilant your wonderful needs and puts your interests ahead of their own. bear 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 retract to charity.)
In September 2018, IBM announced a unique 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 exercise via subsequent statements.
desktop discovering on the IBM z Platform
In might moreover 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 beget exercise 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 tackle resource consumption reminiscent of material processor utilization, I/O processing, reminiscence paging etc. IBM MLz can assemble and redeem 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, ameliorate key performance warning signs (KPIs) after which feed the model consequences lower back into the tackle to maintain an effect 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) answer 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 antecedent 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 actuality 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 connect strategies and others. within the data warehouse and big statistics environments there are continually extra selections obtainable. One of those is the actuality of abstract tables (once in a while called materialized question tables) that comprise pre-summarized or aggregated records, for this intuition allowing Db2 to sojourn away from re-aggregation processing. one more preference is the starjoin entry direction, habitual within the statistics warehouse, the region 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." freight during this context skill a weighted summation of useful resource usage including CPU, I/O, remembrance and different supplies. eventually, the Optimizer takes the lowest cost entry direction, outlets it in remembrance (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 exercise a graphical interface to construct and maneuver a miniature facts mannequin of the information they wish to analyze. The packages then generate SQL statements in keeping with the users’ requests.
The hardship for the DBA
to breathe able to effect friendly analytics in your diverse records retailers you requisite 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 quiet IT managers abet this atmosphere, in particular with the most skilled and mature body of workers nearing retirement?
consider additionally that a gigantic section of cutting back the replete cost of possession of these techniques is to pick up 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 manner is to automate the detection and correction of tuning considerations. this is where computer studying and simulated intelligence will moreover breathe used to awesome impact.
Db2 12 for z/OS and simulated Intelligence
Db2 edition 12 on z/OS makes exercise of the computer gaining information of amenities mentioned above to assemble and retain 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 exercise the model scoring records as enter to its access route option algorithm.
The effect 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 gigantic information is that the usage of AI software does not require the DBA to maintain 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 moreover 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 unavoidable records warehouse tables. These tables are often known as dimension tables, and that they accommodate the statistics aspects continually used to control subsetting and aggregation. for example, in a retail ambiance agree with a table called StoreLocation that enumerates every shop and its region code. Queries against shop income records might moreover 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 select the dimension tables and copy them regularly to the gigantic information utility. in the IBM world this region is the IBM Db2 Analytics Accelerator (IDAA).
Now consider about SQL queries from each operational applications, statistics warehouse clients and massive statistics business analysts. From Db2's point of view, consummate 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 quiet 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 toil for the Optimizer. fortunately, Db2 AI for z/OS can provide the Optimizer the information it needs to beget smart entry course choices.
how it Works
The sequence of hobbies in Db2 AI for z/OS (See Reference 2) is frequently privilege here:
during a bind, rebind, do together or define 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 moreover 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 retain this information in a mannequin;
as the quantity of data in each mannequin grows, the MLz Scoring carrier (which moreover 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 appropriate 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.
IBM's machine learning for zOS (MLz) providing is getting used to outstanding impact in Db2 version 12 to ameliorate 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 ingest these ML and AI conclusions. How will you measure the prices and advantages of the exercise of computing device studying? Which IT guide 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 region the DBAs spend their time in assisting analytics and operational software performance.
# # #
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
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 System z Sales Solutions V6
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IBM partners are gearing up for a year of increased enablement, uphold and joint customer opportunities, as section of deepened channel plans across Australia and unique Zealand.
According to Alison Freeman - general manager of global business 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 pellucid client segmentation, particularly from a software perspective on how and where they retract to market with their partners,” Freeman said. “There’s a gigantic focus on embedded solution agreements - where partners are developing solutions with their technology.
"We’ve had some powerful success and growth there in the past year. We’re getting the whole business behind achieving double digit growth for their partners in 2019, that’s their focus."
Last year, gigantic Blue wanted partners to maintain a larger handle on commercial business opportunities, an region the vendor will continue working through with partners specific to software.
"There’s a lot of toil going into that this year," added Freeman, when speaking to ARN on the sidelines of PartnerWorld at mediate in San Francisco. "We did that with a number of accounts final year, and this is a continuation.
"It’s a shift that allows partners to invest and bring on the privilege people and skills. We’re working with them by client, on which partners they shift, based on their skills. It’s a gigantic stride and that’s how we’ll abet them grow, which will moreover abet 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 stride 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 region 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 same artery 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 unique initiatives, including its Watson-based collaboration engine called IBM business colleague Connect - which A/NZ partners piloted final year; My PartnerWorld; unique 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 solitary portal that helps partners manage their business with the vendor.
Enhancements were moreover made to the PartnerWorld on-boarding process, cutting the time to on-board unique partners by 70 per cent in the process, resulting in the addition of 13,000 net unique 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 stout in those areas. From a systems point of view, we’re seeing growth in storage and that’s another region where we’ll peek to bring on unique partners."
Julia Talevski attended PartnerWorld at mediate in San Francisco as a guest of IBM
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In September 2018, IBM announced a unique product, IBM Db2 AI for z/OS. This simulated intelligence engine monitors data access patterns from executing SQL statements, uses machine learning algorithms to settle on optimal patterns and passes this information to the Db2 query optimizer for exercise 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 depict 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 exercise 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, remembrance 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 ameliorate 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 general Db2 subsystem performance is an primary factor in overall application health and performance, IBM estimates that the DBA uphold staff spends 25% or more of its time, " ... fighting access path problems which antecedent 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 uphold one or more gigantic data applications, cloud application and database services, software installation and configuration, Db2 subsystem and application performance tuning, database definition and management, calamity recovery planning, and more. Query tuning has been in actuality 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 connect methods and others. In the data warehouse and gigantic data environments there are usually additional choices available. One of these is the actuality of summary tables (sometimes called materialized query tables) that accommodate 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, remembrance and other resources. Finally, the Optimizer takes the lowest cost access path, stores it in remembrance (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 exercise 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 effect friendly analytics on your multiple data stores you requisite a friendly 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 uphold this environment, especially with the most experienced and mature staff nearing retirement?
Understand moreover that a big section of reducing the total cost of ownership of these systems is to pick up 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 simulated intelligence can breathe used to powerful effect.
Db2 12 for z/OS and simulated Intelligence
Db2 version 12 on z/OS uses the machine learning facilities mentioned above to assemble 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 exercise 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 choose 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 maintain 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 primary if you store data in multiple places. For example, many analytical queries against gigantic data require concurrent access to unavoidable data warehouse tables. These tables are commonly called dimension tables, and they accommodate 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 breathe used by some gigantic data queries. In this environment it is common to select the dimension tables and copy them regularly to the gigantic data application. In the IBM world this location is the IBM Db2 Analytics Accelerator (IDAA).
Now mediate about SQL queries from both operational applications, data warehouse users and gigantic data business analysts. From Db2's perspective, consummate 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 gigantic data tables should probably access the copy of the table there. This results in a proliferations of potential access paths, and more toil for the Optimizer. Luckily, Db2 AI for z/OS can provide the Optimizer the information it needs to beget 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 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 moreover 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 appropriate changes to access path choices.
There are moreover 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 powerful effect in Db2 version 12 to ameliorate 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 uphold 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 effect 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.
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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
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