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C2090-603 IBM System z Solution Sales V6
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C2090-603 exam Dumps Source : IBM System z Solution Sales V6
Test Code : C2090-603
Test name : IBM System z Solution Sales V6
Vendor name : IBM
: 52 actual Questions
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IBM IBM System z Solution
IBM (IBM) Up three.9% due to the fact final profits file: Can It proceed? | killexams.com actual Questions and Pass4sure dumps
A
month has passed by considering the fact that the ultimate income
record for IBM (IBM). Shares fill added about three.9% in that point
frame, underperforming the S&P 500.
Will
the recent high-quality vogue proceed main up to its next salary
liberate, or is IBM due for a pullback? before they dive into how
investors and analysts fill reacted as of late, let's occupy a brief
look to exist at the most coincident salary record in order to bag a
higher address on the vital drivers.
IBM q4 earnings Beat Estimates, Revenues Decline Y/Y
overseas
company Machines Corp delivered fourth-quarter 2018 non-GAAP revenue of
$four.87 per share, which beat the Zacks Consensus rate of
$four.eighty one per share. despite the fact, revenue per participate
(EPS) decreased 5.9% from the 12 months-ago quarter. The 12
months-over-year decline in EPS will besides exist attributed to
higher tax expense.
Revenues
of $21.76 billion fill been very nearly based on the Zacks Consensus
rate of $21.74 billion and declined three.5% on a year-over-12 months
basis. At uniform currency (cc), revenues dipped 1%. The yr-over-12
months decline can basically exist attributed to currency fluctuation
and headwinds from IBM Z product cycle.
primarily,
IBM cited that signings surged 21% on cc basis to $15.8 billion.
features backlog declined 1% 12 months over 12 months and got here in at
$116 billion.
Geographic salary details
Revenues
from Americas were down 4%, reflecting the headwind from the IBM Z
product cycle. although, persisted boom in Latin the us become a
positive.
Europe,
middle-East and Africa expanded 2% from the year-ago quarter chiefly
because of enlarge in Spain, Germany, Italy and the U.ok.
Asia-Pacific revenues declined 1% on a 12 months-over-year foundation with modest enlarge in Japan.
Strategic Imperatives enlarge Continues
Strategic
Imperatives (cloud, analytics, mobility and protection) grew 5% at cc
from the year-in the past quarter to $11.5 billion. with the exception
of IBM Z product cycle repercussion Strategic Imperatives grew eleven%
12 months over yr.
security
revenues surged 17% (excluding IBM Z product cycle affect) and declined
3% on cc groundwork. On a trailing 12-month groundwork, Strategic
Imperatives revenues were $40 billion, up 9%.
Cloud
revenues surged 6% from the 12 months-ago quarter to $5.7 billion and
19% (with the exception of IBM Z product cycle influence). The annual
race cost for cloud as-a-carrier revenues expanded 21% at cc on a 12
months-over-12 months foundation to $12.2 billion.
Cloud revenues of $19.2 billion on a trailing 12-month basis increased 12% year over 12 months.
Cognitive Revenues Surge
Cognitive
solutions’ revenues-exterior increased 2% yr over yr (on cc basis) to
$5.5 billion. Revenues from Cognitive solutions (including options
application and transaction processing) accelerated essentially due to
enlarge in solutions application, including analytics and synthetic
intelligence (AI).
Segmental
revenues touching on Strategic Imperatives and Cloud improved 9% and
5%, respectively. Cloud as-a-carrier income annual race rate was $2
billion.
solutions
utility includes choices in strategic verticals infatuation health,
area-selected capabilities infatuation analytics and protection, and
IBM’s rising technologies of AI and blockchain. The section besides
comprises offerings that address horizontal domains infatuation
collaboration, commerce and ability. in the said quarter, options
software revenues improved 3% 12 months over 12 months.
IBM
brought up that the integration of AI into choices infatuation
consumer journey analytics in commerce domain helped SaaS signings to
develop in double digit in the reported quarter.
Transaction
Processing software contains utility that runs mission-vital workloads,
leveraging IBM’s hardware structures. Revenues had been up 1% on a 12
months-over-yr foundation.
IBM
witnessed boom in trade verticals infatuation health, key areas of
analytics and safety in the quarter. Watson health witnessed huge-based
enlarge in Payer, issuer, Imaging and lifestyles Sciences domains.
during
the fourth quarter, IBM accelerated partnership with Vodafone
neighborhood. Per the deal, IBM’s superior hybrid cloud platform, AI,
web of things (“IoT”) capabilities will uphold Vodafone company with
digital transformation initiatives.
IBM
cited that analytics performed well within the quarter, pushed by means
of facts science choices and IBM Cloud deepest for facts offering.
protection enlarge become driven by choices in orchestration, records protection and endpoint administration.
Story continues
In
blockchain, IBM announced addition of a yoke of current customers
outright over the quarter, which contains “work with smart Dubai on the
hub East’s first government-endorsed blockchain platform.” The
enterprise additionally unveiled an on-prem providing outright the
route through the suggested quarter, the IBM Blockchain Platform for IBM
Cloud private. a number of current deal wins is additionally assisting
IBM to manufacture stronger its foothold in blockchain technology.
global company functions Revenues raise
Revenues
from world business services-external section fill been $4.3 billion,
up 4% from the 12 months-in the past quarter (up 6% at cc). The 12
months-over-12 months boost was basically as a result of enlarge across
outright three enterprise areas particularly consulting, application
management and world technique features.
Segmental
revenues relating Strategic Imperatives grew 14%. Cloud ensue surged
34%. Cloud as-a-carrier profits annual race cost turned into $2.1
billion.
application
administration revenues accelerated 4% from the yr-in the past quarter.
international method features revenues climbed 5%. additionally,
Consulting revenues elevated 10% yr over year, pushed by route of
improbable efficiency from IBM’s digital enterprise.
technology services & Cloud platforms: Revenues Dip
Revenues
from expertise services & Cloud platforms-exterior decreased 3%
from the 12 months-in the past quarter (flat at cc) to $eight.9 billion.
Segmental revenues pertaining to Strategic Imperatives superior 13%,
driven through hybrid cloud capabilities. Cloud surged 22% from the
yr-in the past quarter. Cloud as-a-carrier income annual race fee
became $8 billion.
Integration
utility elevated 4% from the year-in the past quarter. outright over
the pronounced quarter, greater than 100 corporations outright over
selected IBM Cloud inner most offering. Infrastructure features revenues
were flat on a yr-over-12 months groundwork.
Technical assist capabilities revenues decreased 3% from the 12 months-in the past quarter.
power & z14 pressure systems Revenues
systems
revenues decreased 21% on a 12 months-over-year groundwork (down 20% at
cc) to $2.6 billion, primarily due to repercussion of the IBM Z
product cycle. Segmental revenues concerning Strategic Imperatives
plunged 22%, while Cloud revenues declined 31%.
IBM
Z revenues reduced forty four% yr over 12 months. although, MIPS skill
has extended around 20%, driven with the aid of extensive-based
adoption of the z14 mainframe.
vigor
revenues improved 10% from the 12 months-in the past quarter. The
upside turned into in particular as a result of Linux and effective
adoption across the latest POWER9-primarily based structure.
throughout
the fourth quarter, IBM completed the launch of its next technology
POWER9 processors for midrange and high-conclusion methods which are
designed for handling advanced analytics, cloud environments and
facts-intensive workloads in AI, HANA, and UNIX markets.
IBM
additionally added current offerings optimizing each hardware and
application for AI. administration believes that products infatuation
PowerAI imaginative and prescient and PowerAI business will uphold
constrain current consumer adoption.
although,
storage hardware revenues declined owing to infirm efficiency within
the mid-latitude conclusion, in fraction offset via powerful boom in
outright glint Arrays. IBM pointed out that pricing drive in the
immensely competitive storage market is hurting revenues. The company
introduced its current FlashSystems with subsequent era NVMe technology
during the reported quarter.
working methods application revenues declined three%, whereas programs Hardware slumped 23% from the year-ago quarter.
at
last, international Financing (includes financing and used machine
revenue) revenues diminished 11% yr over yr and 9% at cc to $402
million.
operating details
Non-GAAP
coarse margin remained unchanged from the yr-ago quarter at 49.5%. The
coarse margin benefited primarily by means of one hundred ninety
groundwork elements (bps) enlargement in services margin. youngsters,
negative mix in IBM Z product cycle fully offset this growth.
operating
cost declined 5.three% yr over 12 months, because of recognition of
acquisition synergies and improving operational efficiencies. IBM
continues to space money into rapidly turning out to exist fields
infatuation hybrid cloud, synthetic intelligence (AI), safety and
blockchain.
Pre-tax margin from continuing operations improved 50 bps on a yr-over-year groundwork to 23.1%.
Cognitive
solutions and world enterprise functions aspect pre-tax margins
accelerated 290 bps and 520 bps, respectively, on a 12 months-over-12
months foundation. however, technology capabilities & Cloud
platforms aspect pre-tax margin contracted 20 bps.
Non-GAAP operating margins from continuing operations contracted ninety bps and got here in at 20.3%.
steadiness Sheet & money circulate particulars
IBM
ended fourth-quarter 2018 with $eleven.99 billion in total cash and
marketable securities in comparison with $14.70 billion at the
discontinue of third-quarter 2018. complete debt (together with present
portion) changed into $forty five.8 billion, down from $46.9 million
from the previous quarter.
IBM
reported cash circulate from operations (except international Financing
receivables) of $7.3 billion and generated free money stream of $6.5
billion within the quarter under assessment.
in
the stated quarter, the business back $3.5 billion to shareholders
through dividends and participate repurchases. The company again more
than $10 billion to shareholders through dividends and participate
repurchases for the entire fiscal yr.
on the conclusion of the year, the enterprise had $3.3 billion closing under existing buyback authorization.
Fiscal 2018 Highlights
IBM
stated fiscal 2018 non-GAAP earnings of $13.81 per share, the space as
revenues got here in at $79.6 billion, up 1% each and every year over
yr.
Revenues
from Cognitive solutions, global enterprise services, know-how
capabilities & Cloud platforms, techniques and global Financing came
in at $18.48 billion, $sixteen.82 billion, $34.forty six billion,
$eight.03 billion and $1.fifty nine billion, respectively.
suggestions
IBM expects non-GAAP EPS forecast for 2019 to exist as a minimum $13.90.
IBM still anticipates 2019 free cash movement of $12 billion.
How fill Estimates Been poignant in view that Then?
It seems, fresh estimates flatlined outright over the previous month.
VGM ratings
at
the present, IBM has a subpar boom ranking of D, although its Momentum
score is doing a entire lot more suitable with a B. Charting a to some
extent equivalent path, the inventory turned into allotted a grade of A
on the cost facet, inserting it within the precise quintile for this
investment method.
standard,
the stock has an combination VGM rating of B. in case you don't look
to exist focused on one strategy, this ranking is the one you should
exist drawn to.
Outlook
IBM has a Zacks Rank #3 (dangle). They call an in-line return from the inventory in the next few months.
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that you would exist able to download 7 ultimate stocks for the next 30
Days. click to bag this free document international company Machines
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ScyllaDB proclaims assist for IBM vigour methods for actual Time huge facts | killexams.com actual Questions and Pass4sure dumps
these
days precise-time database enterprise ScyllaDB announced a current
Scylla enterprise free up with optimizations for IBM vigour device
Servers with the IBM POWER9 multi-core structure. by means of combining
Scylla’s particularly performant, close-to-the-hardware design with
subsequent-generation IBM vigor tackle Servers, groups can reach
current degrees of performance while besides reducing the footprint,
can charge and complexity of their programs.ScyllaDB has designed a magnificent distributed database that extends the performance merits we’ve introduced with their multi-core POWER9 processors,” spoke of Tim Vincent, IBM Fellow and vice chairman of IBM Cognitive systems. “The amalgam of the Scylla NoSQL database and their power tackle Servers permits their shared customers to scale up their programs instead of perpetually scaling out, developing current opportunities for information core consolidation and cost efficiency.”
This integration builds upon a multi-faceted relationship between ScyllaDB and IBM. In 2016, IBM Compose outright started presenting Scylla as a fraction of their database-as-a-service providing. The collaboration has because grown to encompass further IBM divisions, together with IBM systems (each IBM vigour techniques and Z methods), IBM Cloud (together with IBM Graph as a carrier) and IBM’s inside use of Scylla to vigor the IBM Cloud carrier Catalog.
Scylla is an open supply drop-in substitute for Apache Cassandra. It can provide scale-up efficiency of 1,000,000 IOPS per node, scales out to tons of of nodes, and normally achieves a ninety nine% tail latency of lower than 1 millisecond. Scylla’s pioneering shard-per-core implementation, asynchronous structure and auto-tuning capabilities permit businesses to immediately leverage the total advantages of the multi-core POWER9 chip.
IBM energy methods servers are designed for mission-vital functions and rising Cognitive era workloads including synthetic intelligence, desktop discovering, profound gaining learning of, advanced analytics and high-performance computing. even if deployed in a personal, public or hybrid cloud, vigor tackle Servers are able to performing millions of I/O operations per 2nd. as a result of Scylla operates asynchronously, it's capable of occupy plenary talents of the pace of the POWER9 processor, riding both I/O and CPU processing in a method that scales linearly with the number of cores on the CPU.
we are excited with the aid of the various advancements IBM has made with its vigor system Servers,” stated Dor Laor, CEO of ScyllaDB. “As records volumes proceed to increase, groups should technique superior workloads yet additionally tarry away from introducing more complexity into their systems. The amalgam of Scylla and IBM POWER9 promises unprecedented efficiency, scale, density and efficiency while drastically simplifying the executive burden of gigantic information systems.”
Scylla database variants with assist for IBM vigour methods are available for down load from the ScyllaDB web page.
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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 use a language with
which they are familiar;
A graphical user interface called the Visual Model Builder guides
model developers without requiring highly-technical programming skills;
It includes multiple dashboards for monitoring model results and
scoring services, as well as controlling the system configuration.
This machine learning suite was initially aimed at zServer-based
analytics applications. One of the first obvious choices was zSystem
performance monitoring and tuning. System Management Facility (SMF)
records that are automatically generated by the operating system provide
the raw data for system resource consumption such as central processor
usage, I/O processing, recollection paging and the like. IBM MLz can
collect and store these data over time, and build and train models of
system behavior, score those behaviors, identify patterns not readily
foreseen by humans, develop key performance indicators (KPIs) and then
feed the model results back into the system to move 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 Important 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 occasions 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, catastrophe 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 exist accessed and develops a list of candidate data access paths. These access paths can comprehend indexes, table scans, various table relate 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 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, recollection and other resources. Finally, the Optimizer takes the lowest cost access path, stores it in recollection (and, optionally, in the Db2 Directory) and begins access path execution.
Big data and data warehouse operations now comprehend software suites that allow the business analyst to use a graphical interface to build and exploit a miniature data model of the data they wish to analyze. The packages then generate SQL statements based on the users’ requests.
The Problem for the DBA
In order to attain capable analytics on your multiple data stores you necessity a capable 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 besides that a large fraction of reducing the total cost of ownership of these systems is to bag Db2 applications to race faster and more efficiently. This usually translates into using fewer CPU cycles, doing fewer I/Os and transporting less data across the network. Since it is often difficult to even identify which applications could benefit from performance tuning, one approach is to automate the detection and correction of tuning issues. This is where machine learning and synthetic intelligence can exist used to Great effect.
Db2 12 for z/OS and synthetic 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 use the model scoring data as input to its access path selection algorithm.
The result should exist a reduction in CPU consumption as the Optimizer uses model scoring input to elect better access paths. This then lowers CPU costs and speeds application response times. A significant handicap is that using AI software does not require the DBA to fill data science skills or profound 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 exist particularly Important if you store data in multiple places. For example, many analytical queries against gigantic data require concurrent access to positive 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 exist used by some gigantic data queries. In this environment it is common to occupy 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, outright these queries are equal, and are forwarded to the Optimizer. However, in the case of operational queries and warehouse queries they should most likely exist 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 work for the Optimizer. Luckily, Db2 AI for z/OS can provide the Optimizer the information it needs to manufacture 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 justify 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 exist 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 besides can exist 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 besides 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 Great 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 uphold staff must exist 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 attain 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.
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
See outright articles by Lockwood Lyon
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IBM System z Solution Sales V6
IBM Db2 Query Optimization Using AI | killexams.com actual questions and Pass4sure dumps
In September 2018, IBM announced a current product, IBM Db2 AI for z/OS. This synthetic 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 use 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:
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 Important 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 occasions 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, catastrophe 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 exist accessed and develops a list of candidate data access paths. These access paths can comprehend indexes, table scans, various table relate 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 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, recollection and other resources. Finally, the Optimizer takes the lowest cost access path, stores it in recollection (and, optionally, in the Db2 Directory) and begins access path execution.
Big data and data warehouse operations now comprehend software suites that allow the business analyst to use a graphical interface to build and exploit a miniature data model of the data they wish to analyze. The packages then generate SQL statements based on the users’ requests.
The Problem for the DBA
In order to attain capable analytics on your multiple data stores you necessity a capable 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 besides that a large fraction of reducing the total cost of ownership of these systems is to bag Db2 applications to race faster and more efficiently. This usually translates into using fewer CPU cycles, doing fewer I/Os and transporting less data across the network. Since it is often difficult to even identify which applications could benefit from performance tuning, one approach is to automate the detection and correction of tuning issues. This is where machine learning and synthetic intelligence can exist used to Great effect.
Db2 12 for z/OS and synthetic 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 use the model scoring data as input to its access path selection algorithm.
The result should exist a reduction in CPU consumption as the Optimizer uses model scoring input to elect better access paths. This then lowers CPU costs and speeds application response times. A significant handicap is that using AI software does not require the DBA to fill data science skills or profound 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 exist particularly Important if you store data in multiple places. For example, many analytical queries against gigantic data require concurrent access to positive 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 exist used by some gigantic data queries. In this environment it is common to occupy 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, outright these queries are equal, and are forwarded to the Optimizer. However, in the case of operational queries and warehouse queries they should most likely exist 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 work for the Optimizer. Luckily, Db2 AI for z/OS can provide the Optimizer the information it needs to manufacture smart access path selections.
How It Works The sequence of events in Db2 AI for z/OS (See Reference 2) is generally the following:
Summary IBM's machine learning for zOS (MLz) offering is being used to Great 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 uphold staff must exist 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 attain you know your data, its distribution, its integrity and your current and proposed access paths? This will determine where the DBAs spend their time in supporting analytics and operational application performance.
# # #
Reference 1John 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
See outright articles by Lockwood Lyon
Microsoft Vs. IBM: One clear Winner | killexams.com actual questions and Pass4sure dumps
No result found, try current keyword!10 and 30 times sales and ... including cognitive solutions, which is home to IBM's much-hyped Watson AI platform. The huge decline in systems was caused by the launch of the Z-mainframe rolling ...IBM (IBM) Up 3.9% Since ultimate Earnings Report: Can It Continue? | killexams.com actual questions and Pass4sure dumps
A
month has gone by since the ultimate earnings report for IBM (IBM).
Shares fill added about 3.9% in that time frame, underperforming the
S&P 500.
Will
the recent positive trend continue leading up to its next earnings
release, or is IBM due for a pullback? Before they dive into how
investors and analysts fill reacted as of late, let's occupy a quick
examine at the most recent earnings report in order to bag a better
handle on the Important drivers.
IBM Q4 Earnings Beat Estimates, Revenues Decline Y/Y
International
business Machines Corp delivered fourth-quarter 2018 non-GAAP earnings
of $4.87 per share, which beat the Zacks Consensus rate of $4.81 per
share. However, earnings per participate (EPS) decreased 5.9% from the
year-ago quarter. The year-over-year decline in EPS can exist
attributed to higher tax rate.
Revenues
of $21.76 billion were almost in line with the Zacks Consensus rate of
$21.74 billion and declined 3.5% on a year-over-year basis. At constant
currency (cc), revenues dipped 1%. The year-over-year decline can
primarily exist attributed to currency fluctuation and headwinds from
IBM Z product cycle.
Notably,
IBM stated that signings surged 21% on cc basis to $15.8 billion.
Services backlog declined 1% year over year and came in at $116 billion.
Geographic Revenue Details
Revenues
from Americas were down 4%, reflecting the headwind from the IBM Z
product cycle. However, continued growth in Latin America was a
positive.
Europe, Middle-East and Africa increased 2% from the year-ago quarter mainly due to growth in Spain, Germany, Italy and the U.K.
Asia-Pacific revenues declined 1% on a year-over-year basis with modest growth in Japan.
Strategic Imperatives Growth Continues
Strategic
Imperatives (cloud, analytics, mobility and security) grew 5% at cc
from the year-ago quarter to $11.5 billion. Excluding IBM Z product
cycle repercussion Strategic Imperatives grew 11% year over year.
Security
revenues surged 17% (excluding IBM Z product cycle impact) and declined
3% on cc basis. On a trailing 12-month basis, Strategic Imperatives
revenues were $40 billion, up 9%.
Cloud
revenues surged 6% from the year-ago quarter to $5.7 billion and 19%
(excluding IBM Z product cycle impact). The annual race rate for cloud
as-a-service revenues increased 21% at cc on a year-over-year basis to
$12.2 billion.
Cloud revenues of $19.2 billion on a trailing 12-month basis increased 12% year over year.
Cognitive Revenues Surge
Cognitive
Solutions’ revenues-external increased 2% year over year (on cc basis)
to $5.5 billion. Revenues from Cognitive Solutions (including solutions
software and transaction processing) increased primarily due to growth
in solutions software, including analytics and synthetic intelligence
(AI).
Segmental
revenues pertaining to Strategic Imperatives and Cloud increased 9% and
5%, respectively. Cloud as-a-service revenue annual race rate was $2
billion.
Solutions
Software includes offerings in strategic verticals infatuation
health, domain-specific capabilities infatuation analytics and
security, and IBM’s emerging technologies of AI and blockchain. The
segment besides includes offerings that address horizontal domains
infatuation collaboration, commerce and talent. In the reported quarter,
Solutions Software revenues increased 3% year over year.
IBM
stated that the integration of AI into offerings infatuation customer
savor analytics in commerce domain helped SaaS signings to grow in
double digit in the reported quarter.
Transaction
Processing Software includes software that runs mission-critical
workloads, leveraging IBM’s hardware platforms. Revenues were up 1% on a
year-over-year basis.
IBM
witnessed growth in industry verticals infatuation health, key areas
of analytics and security in the quarter. Watson Health witnessed
broad-based growth in Payer, Provider, Imaging and Life Sciences
domains.
During
the fourth quarter, IBM expanded partnership with Vodafone Group. Per
the deal, IBM’s advanced hybrid cloud platform, AI, Internet of Things
(“IoT”) capabilities will aid Vodafone business with digital
transformation initiatives.
IBM
stated that analytics performed well in the quarter, driven by data
science offerings and IBM Cloud Private for Data offering.
Security growth was driven by offerings in orchestration, data security and endpoint management.
Story continues
In
blockchain, IBM announced addition of several current clients during
the quarter, which includes “work with Smart Dubai on the Middle East’s
first government-endorsed blockchain platform.” The company besides
unveiled an on-prem offering during the reported quarter, the IBM
Blockchain Platform for IBM Cloud Private. Several current deal wins is
besides aiding IBM to strengthen its foothold in blockchain
technology.
Global business Services Revenues Increase
Revenues
from Global business Services-external segment were $4.3 billion, up 4%
from the year-ago quarter (up 6% at cc). The year-over-year enlarge
was primarily due to growth across outright three business areas namely
consulting, application management and global process services.
Segmental
revenues pertaining to Strategic Imperatives grew 14%. Cloud exercise
surged 34%. Cloud as-a-service revenue annual race rate was $2.1
billion.
Application
Management revenues increased 4% from the year-ago quarter. Global
Process Services revenues climbed 5%. Moreover, Consulting revenues
increased 10% year over year, driven by stout performance from IBM’s
digital business.
Technology Services & Cloud Platforms: Revenues Dip
Revenues
from Technology Services & Cloud Platforms-external decreased 3%
from the year-ago quarter (flat at cc) to $8.9 billion. Segmental
revenues pertaining to Strategic Imperatives advanced 13%, driven by
hybrid cloud services. Cloud surged 22% from the year-ago quarter. Cloud
as-a-service revenue annual race rate was $8 billion.
Integration
Software increased 4% from the year-ago quarter. During the reported
quarter, more than 100 companies around the world selected IBM Cloud
Private offering. Infrastructure Services revenues were flat on a
year-over-year basis.
Technical uphold Services revenues decreased 3% from the year-ago quarter.
Power & z14 Drive Systems Revenues
Systems
revenues decreased 21% on a year-over-year basis (down 20% at cc) to
$2.6 billion, primarily owing to repercussion of the IBM Z product
cycle. Segmental revenues pertaining to Strategic Imperatives plunged
22%, while Cloud revenues declined 31%.
IBM
Z revenues decreased 44% year over year. However, MIPS capacity has
increased around 20%, driven by broad-based adoption of the z14
mainframe.
Power
revenues increased 10% from the year-ago quarter. The upside was mainly
due to Linux and robust adoption across the latest POWER9-based
architecture.
During
the fourth quarter, IBM completed the launch of its next generation
POWER9 processors for midrange and high-end systems that are designed
for handling advanced analytics, cloud environments and data-intensive
workloads in AI, HANA, and UNIX markets.
IBM
besides introduced current offerings optimizing both hardware and
software for AI. Management believes that products infatuation PowerAI
Vision and PowerAI Enterprise will abet drive current customer
adoption.
However,
storage hardware revenues declined owing to infirm performance in the
mid-range end, partially offset by stout growth in outright glint
Arrays. IBM stated that pricing pressure in the immensely competitive
storage market is hurting revenues. The company announced its current
FlashSystems with next generation NVMe technology during the reported
quarter.
Operating Systems Software revenues declined 3%, while Systems Hardware slumped 23% from the year-ago quarter.
Finally,
Global Financing (includes financing and used tackle sales) revenues
decreased 11% year over year and 9% at cc to $402 million.
Operating Details
Non-GAAP
coarse margin remained unchanged from the year-ago quarter at 49.5%.
The coarse margin benefited primarily by 190 basis points (bps)
expansion in services margin. However, unfavorable mix in IBM Z product
cycle fully offset this expansion.
Operating
expense declined 5.3% year over year, due to realization of acquisition
synergies and improving operational efficiencies. IBM continues to
invest in rapidly growing fields infatuation hybrid cloud, synthetic
intelligence (AI), security and blockchain.
Pre-tax margin from continuing operations expanded 50 bps on a year-over-year basis to 23.1%.
Cognitive
Solutions and Global business Services segment pre-tax margins expanded
290 bps and 520 bps, respectively, on a year-over-year basis. However,
Technology Services & Cloud Platforms segment pre-tax margin
contracted 20 bps.
Non-GAAP operating margins from continuing operations contracted 90 bps and came in at 20.3%.
Balance Sheet & Cash flow Details
IBM
ended fourth-quarter 2018 with $11.99 billion in total cash and
marketable securities compared with $14.70 billion at the discontinue
of third-quarter 2018. Total debt (including current portion) was $45.8
billion, down from $46.9 million from the previous quarter.
IBM
reported cash flow from operations (excluding Global Financing
receivables) of $7.3 billion and generated free cash flow of $6.5
billion in the quarter under review.
In
the reported quarter, the company returned $3.5 billion to shareholders
through dividends and participate repurchases. The company returned
more than $10 billion to shareholders through dividends and participate
repurchases for the plenary fiscal year.
At the discontinue of the year, the company had $3.3 billion remaining under current buyback authorization.
Fiscal 2018 Highlights
IBM
reported fiscal 2018 non-GAAP earnings of $13.81 per share, where as
revenues came in at $79.6 billion, up 1% each year over year.
Revenues
from Cognitive Solutions, Global business Services, Technology Services
& Cloud Platforms, Systems and Global Financing came in at $18.48
billion, $16.82 billion, $34.46 billion, $8.03 billion and $1.59
billion, respectively.
Guidance
IBM expects non-GAAP EPS forecast for 2019 to exist at least $13.90.
IBM still anticipates 2019 free cash flow of $12 billion.
How fill Estimates Been poignant Since Then?
It turns out, fresh estimates flatlined during the past month.
VGM Scores
At
this time, IBM has a subpar Growth Score of D, however its Momentum
Score is doing a lot better with a B. Charting a to some extent similar
path, the stock was allocated a grade of A on the value side, putting
it in the top quintile for this investment strategy.
Overall,
the stock has an aggregate VGM Score of B. If you aren't focused on one
strategy, this score is the one you should exist interested in.
Outlook
IBM has a Zacks Rank #3 (Hold). They expect an in-line return from the stock in the next few months.
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