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000-600 System z Solution Sales V4

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000-600 exam Dumps Source : System z Solution Sales V4

Test Code : 000-600
Test appellation : System z Solution Sales V4
Vendor appellation : IBM
: 57 true Questions

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IBM System z Solution Sales

IBM revamps channel strategy to boost ally skills and enablement | killexams.com true Questions and Pass4sure dumps

John Teltsch (IBM)

John Teltsch (IBM)

credit score: IBM

IBM has published a string of recent channel initiatives and investments because it seeks to build key accomplice skills and alternatives in the year ahead.

Unveiled indecent the artery through PartnerWorld at suppose in San Francisco, mountainous Blue rolled out unusual initiatives including its Watson-based collaboration engine known as IBM enterprise accomplice connect; My PartnerWorld; unusual application deal registration and IBM energy techniques, LinuxONE and Z ‘In It to Win It’ incentives.

On properly of this, the dealer also minute the artery it become extra investing in ally competencies and enablement, in addition to driving growth alternatives in market segments reminiscent of protection and hybrid cloud.

in particular, IBM business companion connect focuses on assisting companions tap into the global ecosystem to collaborate on customer alternatives, develop unusual company and convey unusual solutions to market.

in the meantime, the Watson-primarily based engine will enable partners with complementary capabilities to locate each other, Get to market sooner and promote more collectively, in response to John Teltsch, common manager of associate ecosystem at IBM.

My PartnerWorld, set to launch on 15 February, goals to present a customized on-boarding and enablement journey, giving companions the chance to control PartnerWorld materiel and shortcuts in one region. This contains My revenue pastime (MySA), the PartnerWorld Console, Co-advertising Centre and enterprise associate combine platform.

moreover the unusual software deal registration will present partners with precedence entry to particular bid pricing in order to chop back affray between know-how suppliers while improving monetary consequences.

Teltsch added this changed into a allotment of the supplier's ongoing transformation efforts, which it kicked off final year, to enhance companion journey and profitability, together with initiatives round channel pricing, incentives and streamlined resale authorisation.

In April remaining 12 months, large Blue, restructured its accomplice tryst fashions throughout the channel.

in addition, the IBM energy methods, LinuxONE and Z 'In It to Win It' incentives maintain now been made purchasable to companions, which additionally extends the motivation for IBM storage into 2019, along with deploying unusual demand technology tactics to obligate unique leads.

On precise of these unusual initiatives, the vendor is also launching an ESA co-advertising providing for partners embedding IBM know-how, and throughout third-celebration marketplaces, apart from bettering its digital APIs amid plans to double the variety of digital offerings.

"We proceed to uphold partners on their adventure to SaaS, with more than four hundred as-a-provider choices, coupled with unusual demos, trials and incentives to accelerate boom,” Teltsch noted.

IBM has additionally launched SaaS deal registration and P360, which is a device enabling partners to hunt their client’s analytics.

companion capabilities and enablement

Delving deeper, IBM additionally showcased its unusual sales enablement answer, Seismic@IBM, a device that features revenue kits, plays, presentations, expend cases and different assets.

The expense package sheperd bundle is additionally being multiplied into version 2.0, whereby partners can double their IBM cloud credits; supply entry to a building sandbox atmosphere; access chop expense vouchers for opt for training movements and additional subscription coupon codes.

as a result, Teltsch observed it become additionally doubling the variety of device channel technical retailers to befriend companions within the box.

Going forward, a brand unusual managed capabilities security provider application (MSSP) goals to aid partners construct protection practices and carry solutions in either an embedded or reseller model.

A ‘understand Your IBM’ protection income incentive will serve up reloadable IBM Rewards debit cards to partners who promote unusual protection licenses and SaaS.

Teltsch talked about it has additionally enabled partners to construct ‘Cloud Paks,’ which are business-grade container software applications with suggestions and step-by-step guidelines for partners to directly build with open specifications a auspicious artery to permit hybrid and multi-cloud deployments.

“constructed for Kubernetes on the IBM Cloud and IBM Cloud private, IBM Cloud Paks are designed to accelerate time to construction and enrich readiness at lower can impregnate than containers by myself,” he explained.

moreover, a unusual Cloud Digital Technical tryst program will deliver companions with entry to technical assets concentrated on product demos and tutorials.

“These belongings deliver a deep understanding of product choices and how they can aid consumers along their journey to Cloud and AI," Teltsch delivered. "Our unusual Seismic@IBM will embrace a link to this Cloud Digital Technical tryst software so partners can quite simply access this vital assistance whereas preparing for customer engagements."

in accordance with Teltsch, the brand unusual initiatives and investments were a response to the "continuous comments" from companions and shoppers.

Teltsch revealed that indecent over the past year, partners had doubled their variety of specialist and knowledgeable erudition across its know-how stack.

“We’ve also considered mammoth momentum in the variety of partners building their skills and promoting options in strategic areas enjoy IBM security, Cloud, IoT and Watson AI, with their second Watson build challenge producing more than 500 AI options and prototypes that rush on the IBM Cloud,” Teltsch observed.

all the artery through its fourth quarter outcomes, massive Blue mentioned that its cloud company, grew 12 per cent to US$19.2 billion in 2018.

in addition, the business's cognitive application company, including its synthetic intelligence platform Watson, analytics and cyber protection functions and is its 2d biggest, suggested earnings of US$5.46 billion, in comparison with analysts' expectation of US$5.25 billion.

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

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IBM Db2 question Optimization the expend of AI | killexams.com true Questions and Pass4sure dumps

In September 2018, IBM introduced a brand unusual product, IBM Db2 AI for z/OS. This ersatz intelligence engine monitors records entry patterns from executing SQL statements, uses computer gaining erudition of algorithms to rule upon most effective patterns and passes this tips to the Db2 query optimizer for expend with the aid of subsequent statements.

desktop discovering on the IBM z Platform

In may additionally of 2018, IBM announced version 1.2 of its desktop researching for z/OS (MLz) product. here's a hybrid zServer and cloud application suite that ingests performance data, analyzes and builds fashions that symbolize the fitness repute of a number of indicators, screens them over time and offers actual-time scoring capabilities.

a few features of this product offering are aimed at assisting a group of model builders and executives. as an instance:

  • It helps varied programming languages reminiscent of Python, Scala and R. This allows data modelers and scientists to expend a language with which they're widespread;
  • A graphical consumer interface called the visual model Builder courses mannequin builders devoid of requiring incredibly-technical programming potential;
  • It contains numerous dashboards for monitoring model outcomes and scoring features, as well as controlling the device configuration.
  • This computing device discovering suite become originally aimed toward zServer-primarily based analytics purposes. one of the vital first glaring decisions became zSystem efficiency monitoring and tuning. device management Facility (SMF) data that are automatically generated by the working system give the raw information for device resource consumption akin to vital processor utilization, I/O processing, remembrance paging etc. IBM MLz can assemble and reclaim these statistics over time, and construct and instruct fashions of system habits, rating these behaviors, establish patterns not comfortably foreseen via people, multiply key efficiency warning signs (KPIs) and then feed the mannequin results returned into the device to maintain an sequel on device configuration changes that may enhance performance.

    The subsequent step became to enforce this suite to investigate Db2 performance statistics. One solution, known as the IBM Db2 IT Operational Analytics (Db2 ITOA) solution template, applies the laptop studying technology to Db2 operational information to capitalize an figuring out of Db2 subsystem health. it may well dynamically build baselines for key performance indications, supply a dashboard of these KPIs and provides operational corpse of workers real-time perception into Db2 operations.

    while widespread Db2 subsystem efficiency is an distinguished ingredient in common application fitness and efficiency, IBM estimates that the DBA sheperd corpse of workers spends 25% or more of its time, " ... combating access direction issues which trigger performance degradation and service impact.". (See Reference 1).

    AI comes to Db2

    believe the plight of contemporary DBAs in a Db2 environment. In modern day IT world they maintain to sheperd one or greater large data applications, cloud application and database features, utility setting up and configuration, Db2 subsystem and software performance tuning, database definition and administration, catastrophe recuperation planning, and extra. query tuning has been in existence given that the origins of the database, and DBAs are usually tasked with this as well.

    The heart of question path analysis in Db2 is the Optimizer. It accepts SQL statements from applications, verifies authority to entry the records, experiences the areas of the objects to be accessed and develops a listing of candidate data access paths. These entry paths can consist of indexes, table scans, quite a few table combine strategies and others. within the facts warehouse and large records environments there are constantly extra selections accessible. One of those is the existence of abstract tables (every now and then called materialized question tables) that embrace pre-summarized or aggregated statistics, as a consequence enabling Db2 to remain away from re-aggregation processing. a further option is the starjoin entry direction, commonplace within the data warehouse, the district the order of desk joins is changed for performance factors.

    The Optimizer then stories the candidate access paths and chooses the entry direction, "with the lowest cost." can impregnate in this context potential a weighted summation of resource utilization together with CPU, I/O, remembrance and different components. finally, the Optimizer takes the bottom impregnate access route, stores it in reminiscence (and, optionally, within the Db2 directory) and starts access course execution.

    massive facts and statistics warehouse operations now consist of application suites that allow the enterprise analyst to expend a graphical interface to construct and manipulate a miniature statistics mannequin of the statistics they necessity to analyze. The programs then generate SQL statements in line with the users’ requests.

    The issue for the DBA

    so as to execute decent analytics to your numerous facts stores you necessity an excellent figuring out of the statistics necessities, an knowing of the analytical capabilities and algorithms accessible and a high-efficiency facts infrastructure. regrettably, the number and site of data sources is increasing (each in size and in geography), information sizes are becoming, and functions proceed to proliferate in quantity and complexity. How should IT managers aid this ambiance, peculiarly with probably the most experienced and age personnel nearing retirement?

    take into account additionally that a mountainous allotment of decreasing the plenary can impregnate of possession of those systems is to Get Db2 purposes to rush quicker and extra successfully. This continually translates into the usage of fewer CPU cycles, doing fewer I/Os and transporting less records throughout the community. for the judgement that it is often knotty to even establish which purposes might handicap from efficiency tuning, one system is to automate the detection and correction of tuning concerns. here's where computer studying and ersatz intelligence will also be used to astonishing effect.

    Db2 12 for z/OS and synthetic Intelligence

    Db2 version 12 on z/OS uses the machine studying facilities mentioned above to accumulate and store SQL question textual content and entry route details, in addition to genuine efficiency-linked historical counsel comparable to CPU time used, elapsed instances and outcomes set sizes. This offering, described as Db2 AI for z/OS, analyzes and stores the statistics in computing device discovering models, with the mannequin evaluation outcomes 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 statistics as enter to its entry direction alternative algorithm.

    The sequel may noiseless be a reduction in CPU consumption because the Optimizer uses model scoring enter to select more suitable entry paths. This then lowers CPU fees and speeds application response instances. a significant talents is that the usage of AI software does not require the DBA to maintain facts science abilities or deep insights into query tuning methodologies. The Optimizer now chooses the most efficient access paths based mostly not best on SQL query syntax and information distribution information but on modelled and scored ancient performance.

    This will also be specially crucial in case you store records in dissimilar areas. for example, many analytical queries towards large facts require concurrent access to certain information warehouse tables. These tables are commonly known as dimension tables, and that they embrace the facts elements usually used to manage subsetting and aggregation. for example, in a retail atmosphere believe a desk known as StoreLocation that enumerates each store and its region code. Queries against reclaim revenue records can also necessity to blend or summarize income by location; therefore, the StoreLocation desk might be used by using some large records queries. in this ambiance it is regular to occupy the dimension tables and copy them always to the massive records application. within the IBM world this vicinity is the IBM Db2 Analytics Accelerator (IDAA).

    Now reckon about SQL queries from both operational purposes, statistics warehouse users and mountainous facts enterprise analysts. From Db2's point of view, indecent these queries are equal, and are forwarded to the Optimizer. besides the fact that children, in the case of operational queries and warehouse queries they should undoubtedly be directed to entry the StoreLocation desk in the warehouse. even so, the query from the enterprise analyst in opposition t massive statistics tables should noiseless doubtless access the reproduction of the desk there. This consequences in a proliferations of skills entry paths, and more travail for the Optimizer. thankfully, Db2 AI for z/OS can provide the Optimizer the suggestions it needs to bear smart entry path selections.

    the artery it Works

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

  • all through a bind, rebind, Put together or clarify operation, an SQL observation is passed to the Optimizer;
  • The Optimizer chooses the statistics entry course; as the alternative is made, Db2 AI captures the SQL syntax, access direction alternative and question efficiency records (CPU used, and so on.) and passes it to a "getting to know assignment";
  • The researching assignment, which will also be achieved on a zIIP processor (a non-customary-purpose CPU core that doesn't ingredient into software licensing costs), interfaces with the laptop gaining erudition of utility (MLz model functions) to store this tips in a model;
  • because the amount of information in each model grows, the MLz Scoring service (which also can be done on a zIIP processor) analyzes the mannequin facts and scores the conduct;
  • all through the subsequent bind, rebind, Put together or clarify, the Optimizer now has access to the scoring for SQL fashions, and makes acceptable adjustments to access direction choices.
  • There are also numerous user interfaces that supply the administrator visibility to the fame of the accrued SQL remark efficiency data and mannequin scoring.

    abstract

    IBM's desktop learning for zOS (MLz) providing is being used to notable sequel in Db2 edition 12 to ameliorate the efficiency of analytical queries in addition to operational queries and their associated functions. This requires administration attention, as you maintain to investigate that your business is ready to consume these ML and AI conclusions. How will you measure the costs and advantages of the usage of laptop gaining erudition of? Which IT aid group of workers maintain to be tasked to reviewing the sequel of mannequin scoring, and maybe approving (or overriding) the consequences? How will you overview and warrant the assumptions that the application makes about access direction selections?

    In different words, how neatly execute you know your information, its distribution, its integrity and your existing and proposed entry paths? this may investigate the district the DBAs expend their time in supporting analytics and operational application performance.

    # # #

    Reference 1

    John Campbell, IBM Db2 exclusive EngineerFrom "IBM Db2 AI for z/OS: raise IBM Db2 software efficiency with desktop studying"https://www.worldofdb2.com/activities/ibm-db2-ai-for-z-os-raise-ibm-db2-software-efficiency-with-ma

    Reference 2

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


    Catalina faucets IBM analytics to befriend in digital transformation | killexams.com true Questions and Pass4sure dumps

    Feb 11, 2019

    Joyce Wells

    Catalina, a issuer of consumer intelligence and personalised digital media, has adopted the IBM integrated Analytics system (IIAS) as a key pillar of its expanding analytics software to assist provide its consumers greater distinguished consumer insights.

    Catalina companions with greater than 10,000 of the realm's main buyer packaged goods (CPG) manufacturers throughout each main category to convey significant value for valued clientele each time, any place. Its personalized digital media networks and client intelligence insights judgement more constructive client engagements.

    Having helped CPG manufacturers and agents circle consumers into buyers with focused advertising solutions pushed by artery of records analytics in view that 1983, the enterprise is embarking on finished digital transformation to enable sellers and types to seamlessly connect with patrons regardless of their vicinity, be it in-shop, online or via their cellular instruments—anytime and any place.

    As marketers and brands continue to Put money into insights-driven marketing courses—to uphold them execute every minute thing from engage the privilege purchasers and multiply income heave and loyalty, to power each visits and "basket" sizes—data science is on the core of Catalina.

    A key piece of its transformation is the IBM IIAS, a unified data materiel designed to supply users access to advanced statistics science capabilities, as well as the skill to travail with facts throughout hybrid multiclouds, which can be a blend of public and personal cloud environments, and traditional on-premise IT. The device comes with quite a lot of information science equipment developed-in, and because it is according to the IBM common SQL engine, valued clientele can expend the system to drag workloads from on-premise to cloud environments to start automating their companies with laptop gaining erudition of.

    moreover, IIAS comes with records virtualization, a developed-in capability that makes it viable for individuals to write down analytics queries that may access facts any district throughout the business. The system also facets IBM enjoy a shimmer information circulation, which makes it viable for companies to easily flux information between Hadoop clusters and the IIAS, extending and scaling their processing environments.

    "Catalina sits squarely on the intersection of marketing, technology and analytics," mentioned Wes Chaar, chief information and analytics officer at Catalina. "As a allotment of their broader facts science strategic initiative, Catalina is leveraging IBM's know-how to energy greater significant consequences from Catalina's analytics options, from personalization and customer preference modeling, to profits management and multi contact attribution—all to operate at most commandeer stages. The solutions they are building are distinguished for their customers because they optimize media and promotional choices for every solitary consumer, and enable seamless, integrated person experiences that power results."

    For greater information, fade to www.catalina.com and www.ibm.com/analytics.


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    System z Solution Sales V4

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    Quantzig's Market Basket Analysis Solution Helped a Retailer to multiply Quarterly Sales by 50% - | killexams.com true questions and Pass4sure dumps

    LONDON--(BUSINESS WIRE)--Feb 14, 2019--A global data analytics and advisory firm, Quantzig, that delivers actionable analytics solutions to resolve knotty business problems has announced the completion of their latest market basket analysis tryst for a food retailer. During the course of this engagement, the market basket analysis experts at Quantzig adopted a comprehensive approach to befriend the client tackle their challenges and develop a highly scalable system.

    This press release features multimedia. View the plenary release here: https://www.businesswire.com/news/home/20190214005612/en/

    Market Basket Analysis tryst for a Food Retailer (Graphic: business Wire)

    The retail industry is a highly fragmented and knotty one. The rapid technological advancements and the ongoing digital shifts pose several problems for players in this sector. This is one of the main reasons why retailers are now looking at leveraging the expend of analytic models and techniques to cater to the dynamic shifts in customer preferences. One such instrument used by them is – Market Basket Analysis. Market basket analysis empowers retailers to identify product groups that a customer is more likely to buy based on their previous purchase history.

    However, only a fraction of the retail companies today bear decisions based on their customers' purchase behaviors. This turns out to be a major roadblock for an industry whose sole purpose is to fulfill the needs of its customers. By leveraging market basket analysis retailers can develop commandeer strategies to compass their business goals.

    The business Problem: The client is a leading organic food retailer with business units spread across Southern and Western Germany. The client' major challenge revolved around improving sales. The client witnessed a depreciating profit margin for three consecutive years due to which they wanted to leverage market basket analysis to develop unusual cross-selling strategies and identify unusual sales opportunities.

    Our market basket analysis expertise will befriend you gain lucid insights into the purchase behaviors of various customer groups. Get in palpate with their experts to know how market basket analysis can befriend you ameliorate sales.

    "As market leaders continue to capitalize on analytic techniques, a unusual predictive model of market basket analysis is paving its artery across industries in an distress to identify consecutive purchase pattern,"says a market basket analysis expert from Quantzig.

    The Solution Offered: The market basket analysis experts at Quantzig adopted a comprehensive approach to tackle the client's challenges. As a result, the client was well positioned to identify profitable marketing campaigns and products with a elevated affinity. The skill to better referee the overall effectiveness of these campaigns resulted in a 15% lessen in the company's overall marketing budget. Also, by keeping track of key metrics like- tolerable expend per basket and tolerable profit per basket the client was able to track product affinity for their top-selling product categories.

    To accurately forecast consumer demand is not an light task. Request a free proposal to know how their market basket analysis solutions can assist you in increasing sales.

    Quantzig's market basket analysis solutions helped the client to:

  • Increase quarterly sales by 50%
  • Develop unusual products to cater to the needs of their customers
  • Request a free demo to know how market basket analysis can befriend you gain a leading edge.
  • Quantzig's market basket analysis solutions offered predictive insights on:

  • Implementing unusual dashboards
  • Leveraging the expend of transaction-level data
  • Wonder how market basket analysis could open-up a world of unusual opportunities for your organization?Request for more information now!
  • About Quantzig

    Quantzig is a global analytics and advisory firm with offices in the US, UK, Canada, China, and India. For more than 15 years, they maintain assisted their clients across the globe with end-to-end data modeling capabilities to leverage analytics for prudent decision making. Today, their firm consists of 120+ clients, including 45 Fortune 500 companies. For more information on their tryst policies and pricing plans, visit:https://www.quantzig.com/request-for-proposal

    View source version on businesswire.com:https://www.businesswire.com/news/home/20190214005612/en/

    CONTACT: Quantzig

    Anirban Choudhury

    Marketing Manager

    US: +1 630 538 7144

    UK: +44 208 629 1455

    https://www.quantzig.com/contact-us

    KEYWORD:

    INDUSTRY KEYWORD: RETAIL FOOD/BEVERAGE OTHER RETAIL

    SOURCE: Quantzig

    Copyright business Wire 2019.

    PUB: 02/14/2019 10:48 AM/DISC: 02/14/2019 10:48 AM

    http://www.businesswire.com/news/home/20190214005612/en


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

    In September 2018, IBM announced a unusual product, IBM Db2 AI for z/OS. This ersatz intelligence engine monitors data access patterns from executing SQL statements, uses machine learning algorithms to rule 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 represent the health status of various indicators, monitors them over time and provides real-time scoring capabilities.

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

  • It supports multiple programming languages such as Python, Scala and R. This allows data modelers and scientists to 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, 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 influence 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 common Db2 subsystem performance is an distinguished 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 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 uphold one or more mountainous data applications, cloud application and database services, software installation and configuration, Db2 subsystem and application performance tuning, database definition and management, disaster recovery planning, and more. Query tuning has been in existence since the origins of the database, and DBAs are usually tasked with this as well.

    The heart of query path analysis in Db2 is the Optimizer. It accepts SQL statements from applications, verifies authority to access the data, reviews the locations of the objects to be accessed and develops a list of candidate data access paths. These access paths can embrace indexes, table scans, various table combine methods and others. In the data warehouse and mountainous 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, 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 expend a graphical interface to build and manipulate a miniature data model of the data they wish to analyze. The packages then generate SQL statements based on the users’ requests.

    The Problem for the DBA

    In order to execute auspicious analytics on your multiple data stores you necessity a auspicious 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 age staff nearing retirement?

    Understand also that a large allotment of reducing the total cost of ownership of these systems is to Get Db2 applications to rush 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 ersatz intelligence can be used to considerable effect.

    Db2 12 for z/OS and ersatz Intelligence

    Db2 version 12 on z/OS uses the machine learning facilities mentioned above to accumulate 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 be a reduction in CPU consumption as the Optimizer uses model scoring input to select better access paths. This then lowers CPU costs and speeds application response times. A significant handicap 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 be particularly distinguished if you store data in multiple places. For example, many analytical queries against mountainous data require concurrent access to certain 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 reckon a table called StoreLocation that enumerates every store and its location code. Queries against store sales data may wish to aggregate or summarize sales by Location; hence, the StoreLocation table will be used by some mountainous data queries. In this environment it is common to occupy the dimension tables and copy them regularly to the mountainous data application. In the IBM world this location is the IBM Db2 Analytics Accelerator (IDAA).

    Now cerebrate about SQL queries from both operational applications, data warehouse users and mountainous data business analysts. From Db2's perspective, indecent these queries are equal, and are forwarded to the Optimizer. However, in the case of operational queries and warehouse queries they should most likely be directed to access the StoreLocation table in the warehouse. On the other hand, the query from the business analyst against mountainous data tables should probably access the copy of the table there. This results in a proliferations of potential access paths, and more travail for the Optimizer. Luckily, Db2 AI for z/OS can provide the Optimizer the information it needs to bear 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 account for 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 be executed on a zIIP processor (a non-general-purpose CPU core that does not factor into software licensing charges), interfaces with the machine learning software (MLz Model Services) to store this information in a model;
  • As the amount of data in each model grows, the MLz Scoring Service (which also can be executed on a zIIP processor) analyzes the model data and scores the behavior;
  • During the next bind, rebind, prepare or explain, the Optimizer now has access to the scoring for SQL models, and makes commandeer changes to access path choices.
  • There are also various user interfaces that give the administrator visibility to the status of the accumulated SQL statement performance data and model scoring.

    Summary

    IBM's machine learning for zOS (MLz) offering is being used to considerable sequel 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 be tasked to reviewing the result of model scoring, and perhaps approving (or overriding) the results? How will you review and warrant the assumptions that the software makes about access path choices?

    In other words, how well execute 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.

    # # #

    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



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