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000-600 exam Dumps Source : System z Solution Sales V4
Test Code : 000-600
Test denomination : System z Solution Sales V4
Vendor denomination : IBM
: 57 actual Questions
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IBM System z Solution Sales
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 everyone the way through PartnerWorld at suppose in San Francisco, mammoth Blue rolled out original initiatives including its Watson-based collaboration engine known as IBM enterprise accomplice connect; My PartnerWorld; original application deal registration and IBM energy techniques, LinuxONE and Z ‘In It to Win It’ incentives.
On properly of this, the dealer too detailed the way it become extra investing in partner 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 original company and convey original solutions to market.
in the meantime, the Watson-primarily based engine will enable partners with complementary capabilities to locate each other, come by to market sooner and promote more collectively, in response to John Teltsch, universal 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 opening to control PartnerWorld apparatus and shortcuts in one region. This contains My revenue pastime (MySA), the PartnerWorld Console, Co-advertising Centre and enterprise associate join platform.
moreover the original software deal registration will present partners with precedence entry to particular bid pricing in order to lop back conflict between know-how suppliers while improving monetary consequences.
Teltsch added this changed into a fraction 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 appointment fashions throughout the channel.
in addition, the IBM energy methods, LinuxONE and Z 'In It to Win It' incentives acquire now been made purchasable to companions, which additionally extends the motivation for IBM storage into 2019, along with deploying original require technology tactics to obligate unique leads.
On precise of these original initiatives, the vendor is too 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 support partners on their adventure to SaaS, with more than four hundred as-a-provider choices, coupled with original 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 leer their client’s analytics.
companion capabilities and enablement
Delving deeper, IBM additionally showcased its original sales enablement answer, Seismic@IBM, a device that features revenue kits, plays, presentations, exhaust cases and different assets.
The expense package guide bundle is additionally being multiplied into version 2.0, whereby partners can double their IBM cloud credits; supply entry to a pile sandbox atmosphere; access lop 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 attend companions within the box.
Going forward, a brand original 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 original 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 pleasurable way 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 permeate than containers by myself,” he explained.
moreover, a original Cloud Digital Technical appointment 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 original Seismic@IBM will embrace a link to this Cloud Digital Technical appointment software so partners can quite simply access this vital assistance whereas preparing for customer engagements."
in accordance with Teltsch, the brand original initiatives and investments were a response to the "continuous comments" from companions and shoppers.
Teltsch revealed that everyone over the past year, partners had doubled their variety of specialist and knowledgeable knowledge across its know-how stack.
“We’ve too considered huge momentum in the variety of partners pile their skills and promoting options in strategic areas relish IBM security, Cloud, IoT and Watson AI, with their second Watson build challenge producing more than 500 AI options and prototypes that sprint on the IBM Cloud,” Teltsch observed.
all the way 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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In September 2018, IBM introduced a brand original product, IBM Db2 AI for z/OS. This simulated intelligence engine monitors records entry patterns from executing SQL statements, uses computer gaining knowledge of algorithms to determine upon most effectual patterns and passes this tips to the Db2 query optimizer for exhaust 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 exhaust 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 save 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 acquire an result 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 benefit 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 cadaver of workers real-time perception into Db2 operations.
while widespread Db2 subsystem efficiency is an primary factor in universal application fitness and efficiency, IBM estimates that the DBA guide cadaver 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 synchronous DBAs in a Db2 environment. In modern day IT world they acquire to guide 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 subsist accessed and develops a listing of candidate data access paths. These entry paths can consist of indexes, table scans, quite a few table join 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 sojourn away from re-aggregation processing. a further option is the starjoin entry direction, commonplace within the data warehouse, the situation 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 permeate 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 permeate 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 exhaust a graphical interface to construct and maneuver a miniature statistics mannequin of the statistics they need to analyze. The programs then generate SQL statements in line with the users’ requests.
The issue for the DBA
so as to achieve decent analytics to your numerous facts stores you need 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 mammoth fraction of decreasing the replete can permeate of possession of those systems is to come by Db2 purposes to sprint 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 understanding that it is often complicated to even establish which purposes might odds from efficiency tuning, one manner is to automate the detection and correction of tuning concerns. here's where computer studying and simulated intelligence will too subsist used to bizarre 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 exhaust the model scoring statistics as enter to its entry direction alternative algorithm.
The result may soundless subsist 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 acquire 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 too subsist 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 save revenue records can too need to amalgam or summarize income by location; therefore, the StoreLocation desk might subsist used by using some large records queries. in this ambiance it is regular to select 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 account about SQL queries from both operational purposes, statistics warehouse users and mammoth facts enterprise analysts. From Db2's point of view, everyone 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 subsist directed to entry the StoreLocation desk in the warehouse. even so, the query from the enterprise analyst in opposition t massive statistics tables should soundless doubtless access the reproduction of the desk there. This consequences in a proliferations of skills entry paths, and more labor for the Optimizer. thankfully, Db2 AI for z/OS can provide the Optimizer the suggestions it needs to compose smart entry path selections.
the way it Works
The sequence of hobbies in Db2 AI for z/OS (See Reference 2) is frequently here:
all through a bind, rebind, attach 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 too subsist achieved on a zIIP processor (a non-customary-purpose CPU core that doesn't ingredient into software licensing costs), interfaces with the laptop gaining knowledge 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 too can subsist done on a zIIP processor) analyzes the mannequin facts and scores the conduct;
all through the subsequent bind, rebind, attach together or clarify, the Optimizer now has access to the scoring for SQL fashions, and makes acceptable adjustments to access direction choices.
There are too numerous user interfaces that supply the administrator visibility to the fame of the accrued SQL remark efficiency data and mannequin scoring.
IBM's desktop learning for zOS (MLz) providing is being used to notable result in Db2 edition 12 to better the efficiency of analytical queries in addition to operational queries and their associated functions. This requires administration attention, as you acquire 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 knowledge of? Which IT aid group of workers acquire to subsist tasked to reviewing the result of mannequin scoring, and maybe approving (or overriding) the consequences? How will you overview and justify the assumptions that the application makes about access direction selections?
In different words, how neatly achieve you know your information, its distribution, its integrity and your existing and proposed entry paths? this may investigate the situation the DBAs disburse their time in supporting analytics and operational application performance.
# # #
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
Db2 AI for z/OShttps://www.ibm.com/support/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html
Feb 11, 2019
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 primary 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 cause more constructive client engagements.
Having helped CPG manufacturers and agents spin consumers into buyers with focused advertising solutions pushed by way 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, subsist it in-shop, online or via their cellular instruments—anytime and any place.
As marketers and brands continue to attach money into insights-driven marketing courses—to support them achieve every exiguous thing from engage the perquisite purchasers and multiply income raise 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 apparatus designed to supply users access to advanced statistics science capabilities, as well as the skill to labor with facts throughout hybrid multiclouds, which can subsist a amalgam 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 exhaust the system to inch workloads from on-premise to cloud environments to start automating their companies with laptop gaining knowledge of.
moreover, IIAS comes with records virtualization, a developed-in capability that makes it workable for individuals to write down analytics queries that may access facts any situation throughout the business. The system too facets IBM hastily information circulation, which makes it workable for companies to easily rush 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 fraction 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 selection modeling, to profits management and multi contact attribution—all to operate at most usurp stages. The solutions they are pile are primary for their customers because they optimize media and promotional choices for every single 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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LONDON--(BUSINESS WIRE)--Feb 14, 2019--A global data analytics and advisory firm, Quantzig, that delivers actionable analytics solutions to resolve complicated business problems has announced the completion of their latest market basket analysis appointment for a food retailer. During the course of this engagement, the market basket analysis experts at Quantzig adopted a comprehensive approach to attend the client tackle their challenges and develop a highly scalable system.
This press release features multimedia. View the replete release here: https://www.businesswire.com/news/home/20190214005612/en/
Market Basket Analysis appointment for a Food Retailer (Graphic: business Wire)
The retail industry is a highly fragmented and complicated 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 exhaust of analytic models and techniques to cater to the dynamic shifts in customer preferences. One such tool 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 compose decisions based on their customers' purchase behaviors. This turns out to subsist 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 usurp 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 original cross-selling strategies and identify original sales opportunities.
Our market basket analysis expertise will attend you gain clear insights into the purchase behaviors of various customer groups. come by in finger with their experts to know how market basket analysis can attend you better sales.
"As market leaders continue to capitalize on analytic techniques, a original predictive model of market basket analysis is paving its way across industries in an exertion 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 towering affinity. The competence to better umpire the overall effectiveness of these campaigns resulted in a 15% abate in the company's overall marketing budget. Also, by keeping track of key metrics like- middling disburse per basket and middling profit per basket the client was able to track product affinity for their top-selling product categories.
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In September 2018, IBM announced a original product, IBM Db2 AI for z/OS. This simulated intelligence engine monitors data access patterns from executing SQL statements, uses machine learning algorithms to determine on optimal patterns and passes this information to the Db2 query optimizer for exhaust 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 picture 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 exhaust 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 affect system configuration changes that can better performance.
The next step was to implement this suite to anatomize 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 primary factor in overall application health and performance, IBM estimates that the DBA support staff spends 25% or more of its time, " ... fighting access path problems which cause performance degradation and service impact.". (See Reference 1).
AI Comes to Db2
Consider the plight of modern DBAs in a Db2 environment. In today's IT world they must support one or more mammoth 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 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 subsist accessed and develops a list of candidate data access paths. These access paths can embrace indexes, table scans, various table join methods and others. In the data warehouse and mammoth data environments there are usually additional choices available. One of these is the existence of summary tables (sometimes called materialized query tables) that contain pre-summarized or aggregated data, thus allowing Db2 to avoid re-aggregation processing. Another option is the starjoin access path, common in the data warehouse, where the order of table joins is changed for performance reasons.
The Optimizer then reviews the candidate access paths and chooses the access path, "with the lowest cost." Cost in this context means a weighted summation of resource usage including CPU, I/O, 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 exhaust 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 achieve pleasurable analytics on your multiple data stores you need a pleasurable 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 age staff nearing retirement?
Understand too that a large fraction of reducing the total cost of ownership of these systems is to come by Db2 applications to sprint faster and more efficiently. This usually translates into using fewer CPU cycles, doing fewer I/Os and transporting less data across the network. Since it is often difficult to even identify which applications could benefit from performance tuning, one approach is to automate the detection and correction of tuning issues. This is where machine learning and simulated intelligence can subsist used to stately effect.
Db2 12 for z/OS and simulated Intelligence
Db2 version 12 on z/OS uses the machine learning facilities mentioned above to gather and store SQL query text and access path details, as well as actual performance-related historical information such as CPU time used, elapsed times and result set sizes. This offering, described as Db2 AI for z/OS, analyzes and stores the data in machine learning models, with the model analysis results then being scored and made available to the Db2 Optimizer. The next time a scored SQL statement is encountered, the Optimizer can then exhaust the model scoring data as input to its access path selection algorithm.
The result should subsist a reduction in CPU consumption as the Optimizer uses model scoring input to pick 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 acquire 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 subsist particularly primary if you store data in multiple places. For example, many analytical queries against mammoth data require concurrent access to certain data warehouse tables. These tables are commonly called dimension tables, and they contain the data elements usually used to control subsetting and aggregation. For example, in a retail environment 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 subsist used by some mammoth data queries. In this environment it is common to select the dimension tables and copy them regularly to the mammoth 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 mammoth data business analysts. From Db2's perspective, everyone these queries are equal, and are forwarded to the Optimizer. However, in the case of operational queries and warehouse queries they should most likely subsist directed to access the StoreLocation table in the warehouse. On the other hand, the query from the business analyst against mammoth data tables should probably access the copy of the table there. This results in a proliferations of potential access paths, and more labor for the Optimizer. Luckily, Db2 AI for z/OS can provide the Optimizer the information it needs to compose 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 selection is made, Db2 AI captures the SQL syntax, access path selection and query performance statistics (CPU used, etc.) and passes it to a "learning task";
The learning task, which can subsist executed on a zIIP processor (a non-general-purpose CPU core that does not factor into software licensing charges), interfaces with the machine learning software (MLz Model Services) to store this information in a model;
As the amount of data in each model grows, the MLz Scoring Service (which too can subsist 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 too various user interfaces that give the administrator visibility to the status of the accumulated SQL statement performance data and model scoring.
IBM's machine learning for zOS (MLz) offering is being used to stately result in Db2 version 12 to better the performance of analytical queries as well as operational queries and their associated applications. This requires management attention, as you must verify that your business is prepared to consume these ML and AI conclusions. How will you measure the costs and benefits of using machine learning? Which IT support staff must subsist 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 achieve you know your data, its distribution, its integrity and your current and proposed access paths? This will determine where the DBAs disburse 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