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000-M19 - IBM Information Management DB2 9 Warehouse Technical Sales Mastery Test v1 - BrainDump Information

Vendor Name : IBM
Exam Code : 000-M19
Exam Name : IBM Information Management DB2 9 Warehouse Technical Sales Mastery Test v1
Questions and Answers : 43 Q & A
Updated On : March 21, 2019
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000-M19 exam Dumps Source : IBM Information Management DB2 9 Warehouse Technical Sales Mastery Test v1

Test Code : 000-M19
Test Name : IBM Information Management DB2 9 Warehouse Technical Sales Mastery Test v1
Vendor Name : IBM
Q&A : 43 Real Questions

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IBM IBM Information Management DB2

IBM Db2 query Optimization using AI | killexams.com Real Questions and Pass4sure dumps

In September 2018, IBM announced a new product, IBM Db2 AI for z/OS. This artificial intelligence engine displays records access patterns from executing SQL statements, makes use of computer researching algorithms to choose highest quality patterns and passes this information to the Db2 query optimizer to be used by means of subsequent statements.

laptop gaining knowledge of on the IBM z Platform

In may of 2018, IBM introduced edition 1.2 of its machine learning for z/OS (MLz) product. here is a hybrid zServer and cloud application suite that ingests efficiency information, analyzes and builds fashions that signify the fitness fame of a variety of indications, screens them over time and gives precise-time scoring capabilities.

a few elements of this product offering are geared toward helping a group of mannequin builders and managers. as an instance:

  • It helps dissimilar programming languages equivalent to Python, Scala and R. This enables information modelers and scientists to make use of a language with which they are prevalent;
  • A graphical person interface known as the visual mannequin Builder guides model builders with out requiring extremely-technical programming advantage;
  • It includes diverse dashboards for monitoring mannequin results and scoring services, as well as controlling the device configuration.
  • This computing device getting to know suite become firstly geared toward zServer-primarily based analytics purposes. one of the most first evident selections became zSystem performance monitoring and tuning. gadget management Facility (SMF) records which are automatically generated through the operating equipment deliver the uncooked statistics for equipment resource consumption such as relevant processor usage, I/O processing, memory paging and the like. IBM MLz can bring together and shop these statistics over time, and construct and train models of device habits, rating these behaviors, identify patterns no longer effortlessly foreseen by way of people, improve key performance symptoms (KPIs) and then feed the mannequin effects back into the system to have an effect on device configuration alterations that may increase efficiency.

    The next step turned into to put in force this suite to research Db2 performance statistics. One answer, known as the IBM Db2 IT Operational Analytics (Db2 ITOA) answer template, applies the machine gaining knowledge of technology to Db2 operational information to benefit an figuring out of Db2 subsystem fitness. it may possibly dynamically construct baselines for key efficiency warning signs, provide a dashboard of those KPIs and provides operational group of workers actual-time insight into Db2 operations.

    while ordinary Db2 subsystem efficiency is a vital component in standard utility health and performance, IBM estimates that the DBA guide body of workers spends 25% or more of its time, " ... fighting entry direction issues which cause efficiency degradation and service affect.". (See Reference 1).

    AI comes to Db2

    consider the plight of contemporary DBAs in a Db2 ambiance. In ultra-modern IT world they must aid one or more massive facts functions, cloud utility and database functions, utility installing and configuration, Db2 subsystem and application performance tuning, database definition and management, disaster healing planning, and greater. query tuning has been in existence considering the origins of the database, and DBAs are usually tasked with this as well.

    The coronary heart of question direction evaluation in Db2 is the Optimizer. It accepts SQL statements from applications, verifies authority to access the information, studies the areas of the objects to be accessed and develops an inventory of candidate information access paths. These access paths can include indexes, table scans, a variety of desk be a part of methods and others. in the data warehouse and big information environments there are continually further choices purchasable. One of those is the existence of summary tables (once in a while referred to as materialized question tables) that contain pre-summarized or aggregated facts, as a consequence enabling Db2 to avoid re-aggregation processing. an additional option is the starjoin access path, average in the information warehouse, the place the order of table joins is modified for performance causes.

    The Optimizer then stories the candidate access paths and chooses the access path, "with the bottom cost." charge in this context capability a weighted summation of resource utilization including CPU, I/O, reminiscence and other elements. finally, the Optimizer takes the lowest cost access path, stores it in reminiscence (and, optionally, within the Db2 directory) and starts access course execution.

    big records and facts warehouse operations now encompass software suites that permit the enterprise analyst to make use of a graphical interface to build and manipulate a miniature records model of the data they want to analyze. The packages then generate SQL statements in accordance with the users’ requests.

    The issue for the DBA

    as a way to do first rate analytics to your diverse records shops you want an outstanding realizing of the records requirements, an realizing of the analytical features and algorithms obtainable and a high-efficiency data infrastructure. lamentably, the quantity and placement of facts sources is increasing (both in dimension and in geography), statistics sizes are turning out to be, and functions proceed to proliferate in number and complexity. How should IT managers support this atmosphere, above all with probably the most skilled and mature personnel nearing retirement?

    consider additionally that a huge a part of reducing the entire cost of ownership of those techniques is to get Db2 functions to run quicker and more successfully. This continually translates into using fewer CPU cycles, doing fewer I/Os and transporting much less facts across the community. seeing that it's regularly complicated to even establish which applications could benefit from efficiency tuning, one strategy is to automate the detection and correction of tuning concerns. here is the place computing device getting to know and artificial intelligence may also be used to wonderful impact.

    Db2 12 for z/OS and synthetic Intelligence

    Db2 edition 12 on z/OS makes use of the computer studying facilities mentioned above to gather and keep SQL query textual content and entry route particulars, as well as specific performance-related historic assistance corresponding to CPU time used, elapsed instances and result set sizes. This offering, described as Db2 AI for z/OS, analyzes and shops the facts in desktop getting to know fashions, with the model analysis effects then being scored and made attainable to the Db2 Optimizer. The subsequent time a scored SQL statement is encountered, the Optimizer can then use the model scoring facts as enter to its entry path option algorithm.

    The outcomes may still be a discount in CPU consumption as the Optimizer uses mannequin scoring input to choose better entry paths. This then lowers CPU prices and speeds software response instances. a big talents is that using AI software doesn't require the DBA to have facts science competencies or deep insights into query tuning methodologies. The Optimizer now chooses the optimum access paths based no longer most effective on SQL question syntax and information distribution facts but on modelled and scored ancient performance.

    This may also be exceptionally crucial if you store facts in numerous places. as an instance, many analytical queries against big records require concurrent access to certain statistics warehouse tables. These tables are frequently called dimension tables, and that they comprise the information points constantly used to handle subsetting and aggregation. as an instance, in a retail environment trust a desk called StoreLocation that enumerates each store and its place code. Queries in opposition t keep revenue statistics may additionally need to aggregate or summarize income by means of region; therefore, the StoreLocation table might be used by some huge information queries. during this environment it is average to take the dimension tables and replica them regularly to the big data software. in the IBM world this place is the IBM Db2 Analytics Accelerator (IDAA).

    Now consider about SQL queries from both operational purposes, records warehouse clients and massive information business analysts. From Db2's perspective, all these queries are equal, and are forwarded to the Optimizer. youngsters, within the case of operational queries and warehouse queries they should undoubtedly be directed to access the StoreLocation desk within the warehouse. nevertheless, the query from the company analyst against massive records tables should probably entry the replica of the desk there. This consequences in a proliferations of knowledge access paths, and extra work for the Optimizer. fortunately, Db2 AI for z/OS can supply the Optimizer the assistance it should make sensible entry route choices.

    the way it Works

    The sequence of pursuits in Db2 AI for z/OS (See Reference 2) is often right here:

  • all the way through a bind, rebind, prepare or clarify operation, an SQL commentary is passed to the Optimizer;
  • The Optimizer chooses the data access direction; because the alternative is made, Db2 AI captures the SQL syntax, access course alternative and question performance statistics (CPU used, and many others.) and passes it to a "studying assignment";
  • The gaining knowledge of assignment, which will also be completed on a zIIP processor (a non-universal-purpose CPU core that does not element into software licensing costs), interfaces with the laptop discovering software (MLz mannequin capabilities) to store this assistance in a model;
  • as the amount of facts in each model grows, the MLz Scoring carrier (which also can be finished on a zIIP processor) analyzes the model information and ratings the habits;
  • throughout the next bind, rebind, prepare or explain, the Optimizer now has access to the scoring for SQL fashions, and makes applicable alterations to entry path choices.
  • There are also a number of consumer interfaces that provide the administrator visibility to the repute of the amassed SQL remark performance records and model scoring.

    abstract

    IBM's desktop studying for zOS (MLz) offering is being used to remarkable effect in Db2 version 12 to enhance the efficiency of analytical queries as well as operational queries and their associated functions. This requires management attention, as you should examine that your enterprise is ready to devour these ML and AI conclusions. How will you measure the prices and merits of using laptop getting to know? Which IT guide team of workers have to be tasked to reviewing the effect of model scoring, and perhaps approving (or overriding) the effects? How will you assessment and justify the assumptions that the utility makes about access route decisions?

    In other words, how well did you know your information, its distribution, its integrity and your existing and proposed access paths? this will check where the DBAs spend their time in aiding analytics and operational software efficiency.

    # # #

    Reference 1

    John Campbell, IBM Db2 unique EngineerFrom "IBM Db2 AI for z/OS: enhance IBM Db2 software efficiency with machine researching"https://www.worldofdb2.com/routine/ibm-db2-ai-for-z-os-raise-ibm-db2-utility-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

    See all articles through Lockwood Lyon


    IBM PowerVM: Product Overview and perception | killexams.com Real Questions and Pass4sure dumps

    See the total checklist of ideal server virtualization software.

    final analysis:

    IBM PowerVM can virtualize AIX, IBM Linux, and IBM i valued clientele working on its vigour server platform. certainly, it is likely one of the most full featured virtulization courses on the market – no surprise, given IBM's deep legacy in the statistics center.

    however may additionally now not be the least difficult platform to put into effect. it'll require specialists to install it. because of this, mid-sized and big companies may still do great, however SMBs can be top-rated to stay away from it unless they could have the funds for outdoor support. IBM PowerVM is geared notably for brand new superior information facilities with demanding software workloads.

    Product Description:

    IBM PowerVM can consolidate varied workloads onto fewer methods, expanding server utilization and decreasing can charge. PowerVM provides a secure and scalable server virtualization atmosphere for AIX, IBM i and Linux purposes built upon the RAS aspects of the energy programs platform. in brief, its hypervisor is rather bendy. it may support supply features in the cloud quicker by way of automating deployment of VMs and storage. it will possibly also support get rid of downtime by the use of are living mobility between servers.

    PowerVM 2.2.6 grants commercial enterprise-grade virtualization, providing the foundation for cloud computing on IBM vigor techniques. it could correctly share components among applications, consolidate distinctive workloads, and provide the utility mobility in a multi-cloud infrastructure. It is declared to raise resource utilization, reduce working fees, and supply a extra agile atmosphere for IBM AIX, IBM i, and IBM Linux purposes running on vigor programs.

    in the most contemporary liberate, IBM has greater tightly built-in PowerVM with the energy platform. every POWER9 server comes with POWERVM commercial enterprise version. there is additionally a typical version as well as an IBM PowerVM, Linux version. PowerVM regular version contains right here add-ons:

  • Micro-Partitioning expertise
  • N-Port identification Virtualization (NPIV)
  • Partition droop and resume is supported on POWER8 processor-based servers when the firmware is at stage 8.4.0, or later.
  • PowerVM NovaLink
  • Shared processor swimming pools
  • Shared storage pools
  • Single Root I/O Virtualization (SR-IOV)
  • skinny provisioning
  • digital I/O Server (VIOS)
  • virtual network Interface Controller adapters
  • “It has been very respectable with little to no downtime. we have been in a position to stretch our IT greenbacks since the refresh cost on IBM energy can run for years. also, we now have been capable of add many extra VMs to actual machines than different platforms can run,” observed a knowledge core supervisor in manufacturing.

    Servers/operating systems:

    AIX, Linux and IBM i purchasers

    “Our enterprise utilizes VMware and PowerVM. VMware is consumer pleasant and makes supporting home windows OS less difficult. PowerVM is relocating in that course. PowerVM is superior in for you to prioritize workloads across different VMs and be granular in your reservation of cores and virtual CPUs. PowerVM lets you adjust VM characteristics while the VM is up and running,” talked about a gadget Admin in oil & gas.

    Implementation:

    PowerVM is a application down load.

    Scalability:

    up to 1000 VMs on a single server.

    Overhead:

    10% to 15%

    administration:

    management equipment comparable to Hardware administration Console (HMC), integrated Virtualization supervisor (IVM), and PowerVC support to aggregate and manipulate materials by using a consolidated logical view. that you can allocate processors to partitions in increments of 0.01, which permits distinctive partitions to share the processing power of the equipment. When the firmware is at stage 7.6, or later, micropartitions will also be defined as small as 0.05 of a processor and can be changed in increments as small as 0.01 of a processor. A optimum of 20 micropartitions can be created per core.

    A working AIX, Linux, or IBM i logical partition may also be suspended along with its operating device and functions. that you could share memory among partitions in a shared reminiscence pool, by using PowerVM energetic memory Sharing. vigour Virtualization performance (PowerVP) is a efficiency monitoring answer that provides targeted and actual-time assistance about virtualized workloads that are working on vigor programs. which you could use PowerVP to keep in mind how virtual workloads use substances, to research performance bottlenecks, and to make counseled decisions about aid allocation and virtualized computing device placement.

    Patching/Backup:

    provided through other IBM energy equipment.

    Migration:

    that you would be able to migrate an lively or inactive AIX, Linux, or IBM i logical partition from one device to yet another through the use of reside Partition Mobility.

    safety:

    energy systems provide a secured server platform. POWER9 hardware and firmware make it much more comfortable for cloud deployment with key elements for PowerVM servers. Implementation comprises:

  • A at ease IPL technique or secure Boot which handiest allows for platform manufacturer signed Hostboot and power Hypervisor (PHYP) connected firmware up via and together with Partition Firmware (PFW) to run on the gadget.
  • A framework to aid faraway Attestation of the equipment firmware stack via a hardware depended on Platform Module (TPM).
  • Key Markets:

    Virtualization for AIX, Linux and IBM i shoppers operating IBM vigor platforms.

    “It may be over engineered for smaller purposes. although, if the infrastructure is in location that you may utilize it to run Linux VMs as neatly,” observed a system Admin in Oil & fuel

    cost:

    starting at $590 per core, free with any other IBM items.

    Product

    IBM PowerVM

    platforms

    AIX, Linux and IBM i valued clientele

    Scalability

    1000 VMs on a single server

    Overhead %

    10 to 15

    Markets

    Virtualization for AIX, Linux and IBM i customers operating IBM power systems

    cost

    $590 per core

    Migration

    flow lively or inactive VMs

    Key Differentiator

    best suited for IBM environments

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    There is a war a-brewin’, but this war will be fought with wits and not brute strength. Ever since Russian President Vladimir Putin’s declaration that “the nation that leads in AI (Artificial Intelligence) will be the ruler of the world,” the press and analysts have created hysteria regarding the ramifications of artificial intelligence on everything from public education to unemployment to healthcare to Skynet.

    Note: artificial intelligence (AI) endows applications with the ability to automatically learn and adapt from experience via interacting with the surroundings / environment. See the blog “Artificial Intelligence is not Fake Intelligence” for a more detailed explanation on artificial intelligence and machine learning.

    The Fast Company article “How to Stop Worrying and Love the Great AI War of 2018,” projected that the AI battle would ultimately boil down between the “AI Big 6”:  Alphabet/Google, Amazon, Apple, Facebook, IBM, and Microsoft. However, there are other contenders worthy of consideration including GE, Tesla, Netflix, Baidu, Tencent, and Albaba.

    But what are the characteristics of organizations that will be the ultimate winners in this Great AI War? What are the behaviors and actions that will distinguish those organizations that capitalize on this AI gold rush while others “fumble the future”?

    I believe that the AI winners will have the following characteristics:

  • Users, not purveyors, of AI technology
  • Embrace open source for technology agility (independence)
  • Mastery of Big Data (and no, Big Data is not dead)
  • Let me state my case.

    #1 Users, Not Purveyors, of AI TechnologyThe Market Capitalization Leaderboard shown in Figure 1 offers important clues as to which organizations will likely be the AI winners. What will set these organizations apart will be not the selling of technology, but their ability leverage AI for “value capture.”

    Figure 1: Marketing Capitalization Leaders as of May 26, 2017.

    By the way, I think Kleiner Perkins was lazy in classifying “Industry Segment.” The market leaders are less purveyors of AI technology than they are users of AI technology.

  • Less than 10% of Amazon’s revenue comes from technology (cloud); $12B in cloud revenue out of a total revenue of $136B in 2016. So what Industry Segment are they in?
  • Google had quarterly revenues (Q1, 2016) of $26B of which digital media/advertising (search) represented $23B. Their “other” businesses (including Google Cloud) were only $3B. So what Industry Segment are they in?
  • Apple’s most recent quarterly (Q3, 2016) revenues were $42B out of which the iPhone (personal communications, information and entertainment) and the associated iPhone ecosystem (iTunes, Apple Music, App Store) comprised an aggregated $37.5B.
  • Finally, I’m not aware of any AI or data technologies that Facebook sells to the general market. Facebook generated $9.3B in revenue in Q2, 2017 of which $9.16B came from Ad revenue. So what Industry Segment are they in?
  • Mastering Value Capture. Just having the technology is not sufficient; it’s how you use the technology to derive and then drive new sources of customer, business, operational, and financial value that matters. Ultimately, the AI war is about “value capture.”

    The companies listed in Figure 1 are trying to dominate markets, not technology. For example:

  • Apple (#1) seeks to dominate personal communications
  • Google/Alphabet (#2) seeks to dominate digital media, advertising and personal communications
  • Amazon (#4) seeks to dominate online commerce
  • Facebook (#5) seeks to dominate social media, and advertising
  • Each of these AI leaders seeks to extend their value capture capabilities into new markets, including transportation (autonomous vehicles), healthcare, finance, media, and entertainment.

    Other market leaders are also moving aggressively to exploit the power of AI to capture more customer, products and operational value. JPM Morgan (#11) is focused on building an AI platform (see “JPMorgan Takes AI Use to the Next Level”) that will allow JPMC to dominate financial trading. And GE (#16) has made a strategic bet with their Predix platform (see “GE’S Big Bet on Data and Analytics”) as the platform for dominating the Industrial Internet of Things.

    Microsoft (#3) is the one exception as Microsoft is a purveyor of technology. But even Microsoft is branching beyond just selling technology into trying to dominate markets such as digital media, entertainment, and social media where their AI “chops” can give them competitive advantages (see “The Jewel of Microsoft’s Earnings”).

    #2 Embrace Open Source for Technology Agility (Independence)AI leaders will exploit open-source business models to establish platform dominance/standardization, and create technology agility and independence. They will develop an enabling technology, and then give it away via open source. This enables them to encourage the growing community of developers, especially those up-and-coming developers in universities and research labs, to build out and create de facto standards around their enabling technologies.

    Open Source Leaders. The Global AI winners are significant contributors to the artificial intelligence and machine learning open source communities. This includes developments such as Amazon Machine Learning, Google TensorFlow, Facebook Caffe2, Microsoft Azure ML Studio, Microsoft Distributed Machine Learning Toolkit, Facebook GraphQL, and Facebook Torch.

    The leadership role that the “Great AI War” combatants are playing can be seen in many open source projects. For example, Torch is an open source machine learning library and scientific computing framework. The “official maintainers” of Torch are:

  • Research Scientist @ Facebook
  • Senior Software Engineer @ Twitter
  • Research Scientist @ Google DeepMind
  • Research Engineer @ Facebook
  • Training and Education. Another strategy from the Global AI leaders the creation of community or industry training and education opportunities around their open source technologies. For example, Google is committing $1 billion to train American workers to build new businesses with Google’s AI tools (see “Google Commits $1 Billion in Grants to Train U.S. Workers for High-Tech Jobs”).

    Avoiding Technology Lock-in.  But equally important is that these AI leaders are seeking to avoid technology and architecture lock-in. They have watched old school organizations struggle with proprietary software packages that took months if not years for upgrades and bug fixes, while paying a burdensome annual maintenance fees (33% of list price means you’re buying the entire software package again every 3 years). In a world where the enabling data and analytic technologies are changing nearly daily, technological and architecture agility (at scale) and independence is mandatory for organizations looking to win the Great AI War.

    #3 Mastery of Big DataEveryone knows about the astounding growth of big data over the last decade as organizations focused on capturing detailed customer, product, operational and market data. Initially fueled by commerce, web and social media data, big data has accelerated with the growth of video, wearables, and the Internet of Things. (See Figure 2).

    However, organizations have struggled to monetize this wealth of data. Enter artificial intelligence.

    Figure 2: Fueling the Insatiable Appetite for Data

    More Data = Better AI. Artificial intelligence can exploit massive data sets to identify patterns on a scale that flummox traditional Business Intelligence “slice and dice” and query technologies. Data is the food that feeds AI. The more data the AI models consume, the smarter AI gets. For example, Facebook is mastering facial recognition via its DeepFace Deep Learning application by virtue of owning the world’s largest repository of photos.

    To illustrate the symbiotic relationship between big data and AI, let’s look at autonomous vehicles (AV). AV require enormous quantities of data to feed the AV machine learning algorithms. It would take tens of thousands of hours of real-world driving data across a variety of driving scenarios to teach cars how to navigate on their own. To address this data volume problem, AV companies are using the video game “Grand Theft Auto” to help generate enough data in order to train Autonomous Vehicles (see “GTA is Teaching Self-Driving Cars How to Navigate Better in the Real World”).

    Data Lake. Leading AI organizations are exploiting the data lake concept to not only store the growing wealth of structured and unstructured (internal and publicly-available) data, but to provide an elastic, scalable, self-provisioning data science platform for “collaborative value creation” in building the machine learning and artificial intelligence models (see “Data Lake Business Model Maturity Index” for more details on data lake business model maturation).

    Exploiting the Economic Value of Data. Leading AI organizations realize that data and analytics are unlike any traditional corporate assets. Data and analytics are digital assets that never wear out, never deplete, and can be used simultaneously at near-zero marginal cost across an infinite business and operational use cases. Understanding the true economic value of the organization’s data can help to prioritize technology and business investments that accelerate value capture from these data sources (see University of San Francisco research paper “Determining the Economic Value of Data” for more details).

    Conclusion: How to Become an AI WinnerAs has been discussed many times in my blog series, and explored in detail in my book, “Big Data MBA: Driving Business Strategies with Data Science,” AI winners will ultimately be those organizations that are the most effective at leveraging data and analytics to power their business models (see Figure 3).

    Figure 3: How Effective Is Your Organization at Leveraging Data and Analytics to Power Your Business Models?

    Ultimately, AI winners will master three key characteristics:

  • Focus on Value Capture by identifying, validating and prioritizing the organization’s key business and operational use cases (see “Use Case Identification, Validation and Prioritization”).
  • Avoid technology and architecture lock-in and create technology independence via an open source technology strategy
  • Mastery of Big Data and the Data Lake to exploit the unique economic value of the data and analytic digital assets (see “Data Lake Business Model Maturity Index”).
  • So in conclusion, let’s have some fun with this blog and think outside of the box about some hypothetical scenarios in which companies exploit this AI gold rush:

  • What would be the business model ramifications to GE if they were to open source Predix and offer Predix training to universities and third party developers?
  • What would be the business model ramifications to JPMC if they were to open source their trading platform to universities and third party developers?
  • What would be the business model ramifications if IBM moved out of the technology purveyor business and instead acquired companies in financial services and healthcare where their Watson AI platform could create market dominance?
  • As the world prepares for the impending great AI war, now is not the time for organizations to be shy or to cling to old, outdated business models.

    Fortune Favors the Brave.

    Sources

    Figure 1: ScoopNest “2017 global market capitalization leader board: tech is 40% of top 20 companies and 100% of top 5” and Consultancy UK “Market capitalisation of world’s 100 biggest companies hits $17.4 trillion”

    The post 3 Keys to Winning the Great Artificial Intelligence (AI) War! appeared first on InFocus Blog | Dell EMC Services.

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  • Unprecedented PR Coverage: Unmatched editorial coverage on Cloud Computing Journal.
  • Tweetup to over 100,000 plus Twitter followers
  • Press releases sent on major wire services to over 500 industry analysts.
  • Secrets of Our Most Popular Sponsors and Exhibitors ▸ Here

    For more information on sponsorship, exhibit, and keynote opportunities, contact [email protected].

    Sponsorship Opportunities ▸ Here

    Download Slide Deck: ▸ Here

    Speaking Opportunities

    The upcoming 22nd International DXWorldEXPO | CloudEXPO November 11-13, 2018 in New York City, NY announces that its Call For Papers for speaking opportunities is now open.

    Secrets of Our Most Popular Faculty Members ▸ Here

    Submit your speaking proposal ▸ Here or by email [email protected].

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    About DXWorldEXPO LLC

    DXWorldEXPO LLC is a Lighthouse Point, Florida-based trade show company and the creator of DXWorldEXPO - Digital Transformation Conference & Expo. The company produces and presents CloudEXPO, DevOpsSummit, FinTechEXPO - Blockchain Event, the world's most influential conferences and trade shows.



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