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BI0-122 - Cognos 8 BI Administrator v2 - Dump Information

Vendor : Cognos
Exam Code : BI0-122
Exam Name : Cognos 8 BI Administrator v2
Questions and Answers : 45 Q & A
Updated On : December 13, 2018
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BI0-122 Questions and Answers

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BI0-122 Cognos 8 BI Administrator v2

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BI0-122 exam Dumps Source : Cognos 8 BI Administrator v2

Test Code : BI0-122
Test Name : Cognos 8 BI Administrator v2
Vendor Name : Cognos
Q&A : 45 Real Questions

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Cognos Cognos 8 BI Administrator

In a Nutshell: Reviewing through IBM Cognos BI audience | killexams.com Real Questions and Pass4sure dumps

This chapter is from the book 

counting on your position in IBM Cognos BI venture implementation, you may additionally be interested to know those points of IBM Cognos query Studio that relate to your role on the assignment. Following aspects evaluation IBM Cognos question Studio through role at a high degree.

Architect standpoint

Architects are interested to understand the capabilities of the product, so they can correctly suggest a tool or component for the overall BI implementation. question Studio is a core component of IBM Cognos BI and is put in with the Cognos BI software set up. query Studio is an conclusion user advert-hoc question tool perfect for company authors and enterprise analysts to support them explore the facts for his or her a variety of company questions. It makes use of the current IBM Cognos BI safety and statistics sources already defined in the IBM Cognos BI atmosphere. one of the key elements to trust observe:

  • Cognos Workspace superior is the logical upgrade direction for question Studio for IBM Cognos BI v10.x clients. for brand new shoppers, Cognos Workspace superior as an alternative of question Studio is recommended. For legacy IBM Cognos 8.x clients, they can continue to use current studies in query Studio; however, it is suggested to use Cognos Workspace superior for creating fresh reviews.
  • question Studio is an end-user device and can be used as quickly as the metadata package has been posted to IBM Cognos Connection.
  • question Studio can use dimensionally and relationally modeled facts from Framework manager.
  • query Studio is an advert-hoc query and statistics exploration tool that may also be advised to business clients.
  • venture manager standpoint

    mission Managers have an interest in knowing if query Studio has been identified and advised as a part of the IBM Cognos BI answer. If it may be used, what are the related tasks, what capabilities and components are required to implement the tasks, and what section do these belong to? listed here are some aspects to accept as true with:

  • Cognos Workspace advanced is the suggested improve course for query Studio for IBM Cognos BI v10.x users. Legacy IBM Cognos 8.x users can use their existing question Studio studies from the IBM Cognos BI 8.x environment within the IBM Cognos BI v10.x atmosphere after the improve.
  • query Studio is an conclusion-user tool for facts exploration and ad-hoc query to answer brief business questions and is centered for business users and company analysts.
  • company clients can start the usage of question Studio as soon as the Framework manager kit is published through the metadata modeler. It doesn't require any other task to be achieved earlier than users can delivery using question Studio in a nonsecured ambiance. If the package carries delicate facts, the kit need to be secured in an IBM Cognos BI ambiance earlier than turning it over to the conclusion clients.
  • question Studio is a component of the IBM Cognos BI application set up and doesn't need to be purchased one by one.
  • business Analyst perspective

    business Analysts should still be familiar with the features of question Studio and Cognos Workspace advanced, so that you can guide the conclusion users correctly. Cognos Workspace superior is the logical improve route for question Studio users in IBM Cognos 8.x environment. purchasers upgrading from IBM Cognos eight.x environment can use their present queries and experiences in IBM Cognos v10.x environment the use of query Studio within the new ambiance. for brand spanking new queries and experiences, it should be counseled that they use Cognos Workspace superior. the usage of Cognos Workspace advanced, they can use the stronger aspects purchasable to them. one of the key features to accept as true with observe:

  • query Studio is an end-user tool for company clients and enterprise analysts to help them reply their many business questions via dragging and dropping gadgets to the file, making use of filters, and developing calculations.
  • query Studio uses dimensionally and relationally modeled records. For dimensionally modeled facts, the scale, hierarchies, degrees, and measures (metrics or Key efficiency warning signs), the requirement should be obviously understood and captured to enable the metadata modeler to construct applicable kit.
  • users can additionally access experiences with aspect statistics by means of the drill-through options. If such need exists you should gather the requirement pertaining to those drill via experiences and doc it appropriately.
  • If the facts within the Framework manager is sensitive and will no longer be obtainable by way of all, remember the requirements and document it for the Cognos development group to assist them cozy the metadata accurately.
  • Developer point of view

    Developer refers to the Cognos development group. Some elements to trust for query Studio implementation are highlighted here:

  • query Studio works with dimensionally as well as relationally modeled facts; make certain the package is built using the applicable style.
  • trap the scale, hierarchies, tiers, and measures per necessities, if the equipment is to be modeled dimensionally.
  • appropriate drill-through stories exist and have the necessary permissions for consumer entry.
  • be certain filters and filter values are install for effective query execution when filtering on non-indexed or non-numeric columns.
  • ensure all security necessities are applied in Framework supervisor in addition to IBM Cognos Connection, as required.
  • end-person standpoint

    end person refers to enterprise clients and business analysts who will be using question Studio to create their queries to explore and analyze statistics. some of the points you should definitely accept as true with are listed here:

  • Cognos Workspace superior is the logical improve path for question Studio. if you are a brand new Cognos user (are not moving from IBM Cognos 8.x or prior versions), it's informed you utilize Cognos Workspace advanced for records exploration and querying to faucet into its rich capabilities.
  • if you're a legacy Cognos 8.x consumer now upgrading to IBM Cognos BI v10.x, Cognos Workspace superior is the logical upgrade route be sure to consider for growing new reviews.
  • query Studio works with each relationally and dimensionally modeled facts. Share with the company Analyst or Cognos construction group your necessities for the diverse paths primary to your reports, the metrics/KPIs you need to computer screen, and the like, so they may also be made obtainable within the metadata package to meet your reporting wants. These necessities inform the metadata modeler how the statistics may still be structured within the equipment to address your facts exploration and reporting wants.
  • If the metadata has delicate information and should no longer be obtainable via all, you must evidently communicate the safety should the company Analyst/IBM Cognos BI Administrator/Cognos development team, as applicable for your corporation.
  • IBM Cognos BI Administrator viewpoint

    IBM Cognos BI administrators should work along side the crew to be sure that the Cognos BI device can cater to those needs with the present Cognos BI installing. there are many cases when a choice needs to be made between easy to put in force and straightforward to hold; that you may help the crew make the right choice according to the infrastructure, entertaining consumer condition, and tradition. one of the most aspects worth when you consider that follow:

  • be certain question Studio is obtainable to the entire clients who need access.
  • Is there any protection requirement for who can see the metadata equipment required for querying and reporting in query Studio?
  • make sure programs that ought to be accessed through question Studio had been modeled with conclusion clients in intellect.

  • Cognos proclaims the supply of Cognos 8 company Intelligence records Modeler | killexams.com Real Questions and Pass4sure dumps

    Cognos, a issuer in company intelligence and corporate efficiency administration (CPM), recently introduced the customary availability of the Cognos 8 enterprise Intelligence facts Modeler for IBM Lotus Domino. agencies will now be capable of entry crucial company information determined in isolated silos of IBM Lotus Notes applications and databases. This extra extends the capabilities of Cognos 8 company Intelligence to access heterogeneous facts sources and supply the most complete view of corporate assistance for company intelligence reporting, analysis, scorecarding, dashboards, and experience management.

    Cognos 8 business Intelligence facts Modeler integrates Lotus Notes databases and functions. Cognos 8 enterprise Intelligence gives you the comprehensive range of company intelligence capabilities on a single, proven architecture.

    The Cognos eight company Intelligence statistics Modeler permits enterprise intelligence users to quite simply opt for Lotus Notes facts sources, tables, and columns, and label them with names which are significant to the company, with out making any changes to existing Lotus Notes purposes. The Cognos eight business Intelligence information Modeler optimizes the information for quick entry and creates a metadata mannequin for Cognos eight company Intelligence. This data is then as obtainable as any other business facts source and might be joined to other commercial enterprise data sources with out coding. The place of work Managed client integration makes it possible for users to collaborate with a full knowing of business performance.

    This BeyeNETWORK news item consists of information from a fresh press unencumber by means of the company outlined.


    Cognos basics Tutorial — ABC be trained | killexams.com Real Questions and Pass4sure dumps

    data Warehouse ideas

    what is a knowledge Warehouse?

    A warehouse in normal terms is a repository where we store our advice. Coming to the records warehouse, it is a group of facts equipped in a selected method and categorized guidance.

    a knowledge warehouse retailers ancient records of a company that allows you to analyze their performance over the last years and plan for the long run.

    The popular definition of the facts warehouse via WH Inmon:

    an information warehouse is:

  • area oriented: information within the warehouse is categorized in diverse area areas. as an instance, agree with a KFC keep. It has many branches all over the realm. If we ought to analyze “sales” for India, this is termed as “discipline”.
  • built-in: a data warehouse has records coming from assorted sources that are integrated into the warehouse. for example, accept as true with the same KFC save, shops in India may shop date box as “dd/mm/yyyy” whereas the same statistics in a further nation may be kept as “MM/DD/YYYY”. The information warehouse will have only 1 layout fastened to assert “MM/DD/YYYY”.
  • Time-Variant: a knowledge warehouse shops historic statistics with which we can determine patterns of revenue over a time length of 3 months,6 months or 2 years of any firm which has a warehouse.
  • Non-volatile: data in the warehouse will no longer trade once it's entered.
  • a different definition from Ralph Kimball is extra exact:

    an information warehouse is a replica of transaction facts exceptionally structured for query and evaluation.

    what's a data mart?

    a knowledge mart is a subset of a knowledge warehouse. believe we've a firm established in many different places and every area maintains a data warehouse which we name it as records mart as a result of a warehouse could have the entire data built-in and as far as information mart is regarded it might be a part of the records warehouse.

    What are makes use of of getting a data Warehouse?

    a knowledge Warehouse, in well-known, is used to analyze tendencies over a duration of time and enhances the decision making of an organization. once the information is loaded into the warehouse can be growing an OLAP dice or directly use the facts to investigate trends. Basing on which the desirable level administration will strategy their future enterprise innovations.

    information warehouse structure

    we are able to multiple sources there all of the operational facts should be saved. Some might also keep them in flat information and some in databases. we will have to read all the sources and operate ETL (Extract, seriously change and load operations) which may be used to integrate records from these sources and seriously change them into one unique constitution and then load into our goal records warehouse.

    statistics Warehouse structure

    There are 2 tactics in designing your statistics warehouse. First one is “appropriate-Down strategy” and 2nd is “bottom-up strategy”.

    proper — Down method: The above graphic indicates the properly-down method, where we are reading data from dissimilar sources and transforming the data and loading into your warehouse, then on suitable of that we are creating our information marts.

    bottom-up method: The opposite of the above approach is that this. right here we may be developing records marts first after which we are able to create our records ware condominium on exact of all the facts marts.

    facts Warehouse terms

  • Dimension: A Dimension is a specific assistance which is kept in a data warehouse.
  • fact: fact is a measurable amount by which we will basically figure out what the dimension does.
  • Attribute: Attributes are the points in a dimension  Ex: consider we now have product suggestions categorized in a dimension. The attributes of the “Product dimension” are: “Product_ID, Product_Name, Product_Color” and so on.  income is a truth desk the place you store numeric values associated with the dimension attributes.as an instance, we ought to calculate the number of earnings of every product for the current month.in this case, Product is the dimension and Month is a dimension and numbers of product offered is the fact.
  • OLAP: online Analytical Processing is a multidimensional model the use of which users can view facts in distinctive dimensions at a single glance.
  • kinds of OLAP’s:
  • Relational OLAP
  • Multi-dimensional OLAP
  • Hybrid OLAP
  • OLTP: online transactional processing is a transaction based mostly model the place the main purpose might be to retrieve facts sooner and replace transactions right now.
  • Schemas in Datawarehouse

    The schema is a logical association of tables in a knowledge warehouse. we have schemas in relational databases. Very tons like equal information warehouse has schema’s particularly star schema, Snowflake schema, and reality-Constellation.

    famous person Schema

    in this logical association of tables, the reality desk can be at the core of the schema surrounded by distinct dimension tables. truth and dimension tables are linked by way of a prime — foreign Key relationship.  reality table could be having international keys of all the Dimension tables together with the records whereas a Dimension desk could be having attributes which describe the Dimension.

    Snowflake Schema:

    in this logical association of tables, we have dimension tables linked to different dimension tables which in flip are linked to truth desk by way of a first-rate-foreign key relationship.  Else we will say that the Dimension tables in Snowflake schema are normalized.  on account of the normalization, the statistics redundancy could be decreased and lots of cupboard space is saved.

    reality Constellation Schema:

    A fact constellation schema, not like the start or snowflake, can have numerous truth tables. it's also referred to as as Galaxy Schema.

    big name Schema example

    in this schema shown, a fact table (earnings) is connected to dissimilar dimension tables merchandise, location, time and branch. each dimension desk has attributes describing the dimension and truth table has the overseas key of all dimension tables together with information like dollars_sold and gadgets sold.

    Snowflake Schema example:

    during this schema, a reality desk (revenue) is related to diverse dimensions, whereas the dimensions item and placement are once again related to city and organisation dimension respectively.

    fact Constellation schema instance:

    during this schema, we can locate two truth table income and transport connecting with each and every different and are once more linked to diverse dimension tables individually.

    Now we're jumping into the precise topic “Cognos company Intelligence”.

    average DWH architecture

    We may be coping with Cognos reporting device and Cognos Metadata managing device and different advantageous tools that Cognos BI has.

    Let’s move through the place BI will really come into photo

    The below photo shows basic statistics warehouse architecture. From facts load to the use of the information loaded into the tables for reinforcing the business of your corporation. From the entire facts sources (may also it's Operational databases, Flat info and so forth.) we load the data into facts warehouse via different ETL system and creating cubes and using the facts for mining purpose.

    The beneath determine will describe the business Intelligence move how the facts will be used for company optimization. company intelligence is a manner from where you might be capable of derive how you can boost your enterprise with the facts that you've got. the usage of BI one might be able to analyze their statistics at distinctive stages and should be able to make selections to make their company more advantageous.

    Cognos BI might be used to generate studies (list, crosstab, and charts). The beneath determine will display you the complete circulate that happens with Cognos.

    the 1st step in the use of Cognos BI is to gather requirements. here's feasible when you bear in mind your business structure and the statistics that drives your enterprise. as soon as the requirement is finalized the subsequent step is to create your framework metadata mannequin. that you would be able to choose both to you superstar schema or snowflake schema otherwise you can use the present relational model.

    After this step is completed with we should submit the model that we now have created. Publishing the mannequin will make your metadata mannequin obtainable for reporting intention. as soon as your mannequin is posted, we can use the model to create different types of reports as per your requirement and we will time table the studies basing your requirement.

    distinctive types of Cognos

    Cognos ReportNet

    Cognos ReportNet (CRN) is an internet-based software product for creating and managing ad-hoc and customized-made experiences. ReportNet is developed with the aid of the Ottawa-based business Cognos (formerly Cognos incorporated), an IBM enterprise. The web-primarily based reporting tool turned into launched in September 2003. due to the fact IBM’s acquisition of Cognos, ReportNet has been renamed IBM Cognos ReportNet like every other Cognos items.

    accessories:

  • Cognos file Studio — an internet-based product for creating complicated expert searching studies
  • Cognos query Studio — an internet-primarily based product for developing ad-hoc experiences.
  • Cognos Framework manager — A metadata modeling device to create BI metadata for reporting and dashboard applications.
  • Cognos Connection — the leading portal used to access experiences, schedule experiences and operate administrator actions.
  • Cognos eight.x

    IBM Cognos eight BI, at the start launched in September 2005, mixed the elements of a few old items, including ReportNet, PowerPlay, Metrics manager. There are also categorical and extended types of Cognos 8 BI. Full points:

    components:

  • record Studio (knowledgeable report authoring tool formatted for the internet)
  • query Studio (advert hoc file authoring tool with speedy information preview)
  • evaluation Studio (explore multi-dimensional dice information to reply company questions)
  • Metric Studio (display screen, analyze, and report on KPIs)
  • Metric dressmaker (outline, load, and retain metrics to be purchasable in Metric Studio)
  • event Studio (motion based brokers to notify determination makers as events ensue)
  • Framework manager (Semantic metadata layer tool which creates fashions or programs)
  • PowerPlay Studio (formerly PowerPlay web)
  • Analytic functions (Packaged BI applications, built on an adaptable platform and extensible into business Analytics)
  • Cognos 10.x

    we will see distinctive components and how they are fitting in. The accurate most layer, the place we will see Cognos Connection, Administrator, company insight and different studios. all of them are internet-based and conclusion-user needs not to set up any client aspect application if he has the latest web browser put in.

    The bottom layer is really data layer where you may additionally have homogeneous or heterogeneous database methods. data may be relational or multi-dimensional. On properly of it, we are able to see three modeling equipment there — Framework supervisor, Transformer, and Metric designer. All of them are customer based installation.

    We’ll retain the movement of add-ons from appropriate to backside as proven in beneath BI accessories determine.

    earlier than getting into the exact topics about the way you create the mannequin, install it and use it to create reviews we should first understand the architecture of Cognos.

    Cognos architecture:

    Cognos business Intelligence framework is 3- tier architecture. the primary tier of the structure could be having the internet server which is answerable for the getting access to the user interfaces. The second tier will be having IBM Cognos BI Server which may be having gateways and dispatchers to route requests from different UI’s to Database and speak returned to the server.

    The Third Tier could have the statistics sources. that you may have distinctive data sources and they're going to connect with the Cognos server the use of JDBC or API’s.

    The beneath determine shows the architecture of Cognos BI:

    within the statistics tier, that you can see one other element known as the “content material save” which is a repository for the Cognos server. each time you retailer any objects stories, models every thing the records is saved in content material save database which once you reopen or reuse the latest object created will fetch the statistics from the content save. All this work should be performed with the aid of the “content material supervisor”.

    content material supervisor: The content supervisor is liable for connecting with the content store database and saving any file or model lower back to the content material shop.

    Dispatcher: The job of dispatchers is to route requests sent from and to the web UI/home windows based mostly add-ons.

    internet-based mostly and windows based mostly components of Cognos

    Cognos BI has two forms of accessories,

    1. internet-primarily based accessories.

    2. Window based add-ons.

    web-primarily based components includes

  • Cognos Connection: here's the portal from where that you may access all the net-primarily based components of IBM Cognos BI.
  • Cognos Administration: using this net-based UI that you could perform administrative initiatives like granting access, revoking entry, growing clients, corporations and defining roles etc.,
  • Cognos record Studio: the usage of report studio tool we are able to create reviews and layout the stories. we can create numerous sorts of experiences like checklist document, crosstab reports, graph’s and so on.,
  • Cognos query Studio: right here we create advert-hoc experiences. advert-hoc studies are constructive when a consumer wants to see the file without any prompts and fewer formatting. using query studio we can create a document straight.
  • Cognos Metric Studio: This studio is used to construct custom-made scorecards experiences to display screen and analyze metrics.
  • Cognos evaluation studio: This studio is used to investigate records from diverse dimensions and also evaluate developments.
  • Cognos company perception: This tool is used to build dashboards. A dashboard enables a user to without delay seem to be into the records and raise the choice making.
  • Cognos enterprise perception advanced: With this tool, we can build more powerful dashboards and in addition to standard reviews.
  • Window-primarily based accessories:

  • Cognos Framework manager: Cognos framework manager is a device used to create metadata models which we can use in document studio or analysis studio.
  • Cognos Map manager: the use of this tool we can create maps as a way to permit you to create a new location through the use of the current areas.
  • http://www.abclearn.com/classes/cognos/cognos-tutorial/cognos-framework-manager


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    Mega-vendors dominate Gartner's 2010 BI, data warehouse Magic Quadrants | killexams.com real questions and Pass4sure dumps

    Mega-vendors still dominate the business intelligence (BI) software market, but their stranglehold may be loosening as disgruntled customers begin to look for lower-priced, more flexible options from BI pure-plays.

    SAP and IBM BI customers, in particular, report lower levels of satisfaction as the mega-vendors continue to iron out their BI product roadmaps and adjust support and maintenance fees, according to Gartner's BI Magic Quadrant report, released last week.

    With a weak economy, customers are increasingly looking for cheaper, quicker-to-deploy options like BI embedded within packaged analytic applications and, to a lesser extent, SaaS BI to meet their reporting and analytic needs, said Rita Sallam, an analyst with the Stamford, Conn.-based research firm and co-author of the report.

    "Many [BI] pure-plays are leveraging business user impatience with enterprise BI systems and are delivering alternative easy-to-use-and-deploy architectures, alternative business models, and a strong customer focus -- so much so that in many cases business users continued to buy non-stack solutions even when a stack standard is in place," Sallam said in an email interview.

    The related data warehouse market is experiencing a similar phenomenon. According to Gartner's Data Warehouse Magic Quadrant, also released last week, smaller data marts and virtual data warehouses that use federation technology are enjoying renewed popularity as companies look to increase flexibility while keeping costs down.

    Higher performance requirements from customers are also pushing data warehouse vendors to improve the power of their products through the addition of in-database analytics and column architectures, according to Gartner's Donald Feinberg, who co-authored the report on data warehouses.

    Data warehouse appliances -- server and storage hardware bundled with analytic databases -- also continue to gain in popularity thanks to their ease of implementation and support, he said.

    "The bottom line is [customers] want to call one person for support" when there is a problem with performance, Feinberg said.

    Still, in both markets, neither customer unrest nor the desire for quicker, cheaper deployments made much of a dent in the mega-vendors' dominant market share positions, Gartner said. Mega-vendors are themselves responding with more BI and DW deployment options, either developed internally or through acquisition.

    "I think there are a few reasons why customers continue to buy into the stack despite an increase in dissatisfaction," Sallam said. "Stack-centric buying is largely IT driven, and stack vendors promise a one-stop shop. They often offer deep discounts or bundle BI platform capabilities with other parts of the mega-vendor stack (e.g., applications or information infrastructure) and offer the promise of integration and optimization with their respective applications and information management stacks."

    Three mega-vendors top both BI and data warehouse markets

    Appearing in the leaders' quadrants in both the BI and data warehouse Magic Quadrant reports were Oracle, IBM and Microsoft.

    Oracle's BI platform has the widest range of capabilities among the top vendors, including reporting, dashboarding and ad hoc query functionality. The vendor also offers three distinct data warehousing options, -- Oracle Database 11g, Oracle Reference Configurations, and Oracle Exadata v2 -- "giving customers a wide variety of choices," the report said.

    IBM's core BI platform, IBM Cognos 8, was cited for its architectural consistency, "with IBM Cognos customers reporting that they need only three administration staff per thousand users on average," the BI Magic Quadrant report states. Like Oracle, IBM has a number of data warehouse products to meet varying needs.

    Microsoft, meanwhile, continues to make inroads in both markets thanks to the ubiquity of its tools -- mainly Excel, SQL Server, and SharePoint Server, according to Gartner. Microsoft will probably expand its market share in 2010 when it debuts PowerPivot, an Excel add-on for BI and analytics, and SQL Server 2008 R2 Relational Database, which is likely to take the form of an appliance.

    "The significance is that all three … have the full spectrum of information infrastructure technologies from the database [including hardware in the case of IBM and Oracle] through the middleware stack and -- in the case of Oracle, SAP and Microsoft -- the enterprise applications as well," Sallam said. "Stack vendors do have an advantage in that they can optimize their BI platforms for their respective application and information management stacks."

    While the top positions in the BI Magic Quadrant were bunched together -- Oracle and Microsoft earned virtually the same ranking -- Teradata was the clear leader in data warehousing. Gartner cited the vendor's products' ability to handle mixed workloads without hindering performance, including near real-time data loading, tactical business analytics and an increasing number of ad hoc queries.

    "Teradata's solutions, which include the data warehouse platform, data models and professional services dedicated to data warehousing, set it apart from the rest of the market," Feinberg wrote.

    Also earning a place in the BI leaders' quadrant were SAS Institute, Microstrategy, SAP, and Information Builders. Netezza and Sybase rounded out the leaders' quadrant for the data warehouse market.

    SaaS, open source BI deployments still small but increasing

    While still a small percentage of overall deployments, SaaS BI, delivered both from internal and the public cloud, is slowly but steadily gaining traction, according to Gartner.

    "This growth will be accelerated by organizations' increasing need to deploy intuitive BI tools and applications cost-effectively to more users, reduce time to value and time to scale, and lower capital expenditures," Sallam wrote.

    SAP and SAS Institute have the largest number of SaaS BI deployments, but smaller up-and-coming vendors like Birst, GoodData, Oco and PivotLink are also getting attention, the report states.

    Cloud-based data warehousing, on the other hand, has yet to take off, for a number of reasons, according to Gartner. "The issues around the security, multi-tenancy and latency of use of the Internet as a transport for data loading are among the reasons given for the reluctance to locate the data warehouse in the cloud," Feinberg wrote.

    Gartner predicted that both the BI and data warehouse markets would continue to grow this year as companies look to the software to turn their corporate data into competitive differentiators.


    MicroStrategy's (MSTR) CEO Michael Saylor on Q1 2018 Results - Earnings Call Transcript | killexams.com real questions and Pass4sure dumps

    No result found, try new keyword!Sales and marketing expenses increased to $11.8 million at 30% year-over-year and decreased $0.6 ... And so as SAP and IBM, and Oracle shift their commitments away from their BI enterprise platforms t...

    Deploying a Windows Data Science Virtual Machine to Azure with PowerShell easily | killexams.com real questions and Pass4sure dumps

    This weekend (10 December 2016), I went to Slovenia for a SQL Saturday. As always, it was an amazing event well organised by Mladen Prajdic, Dejan Sarka, and Matija Lah in a fabulous setting amongst fabulous scenery. I highly recommend it and, also, Ljubljana  is a wonderful place to be in December with all the lights and markets.

    WP_20161209_19_21_06_Pro.jpg

    Whilst I was there I was asked by someone if you could deploy data science virtual machines in Azure with PowerShell. I said I was sure that it could be done and agreed I would write a blog post, so here it is.

    According to the Azure documentation

    The Data Science Virtual Machine running on a Windows Server 2012 contains popular tools for data exploration, modeling and development activities. The main tools include Microsoft R Server Developer Edition (An enterprise ready scalable R framework) , Anaconda Python distribution, Jupyter notebooks for Python and R, Visual Studio Community Edition with Python, R and node.js tools, Power BI desktop, SQL Server 2016 Developer edition including support In-Database analytics using Microsoft R Server. It also includes open source deep learning tools like Microsoft Cognitive Toolkit (CNTK 2.0) and mxnet; ML algorithms like xgboost, Vowpal Wabbit. The Azure SDK and libraries on the VM allows you to build your applications using various services in the cloud that are part of the Cortana Analytics Suite which includes Azure Machine Learning, Azure data factory, Stream Analytics and SQL Datawarehouse, Hadoop, Data Lake, Spark and more.

    I have created a function to wrap around the process to make it easier for none PowerShell  people to do this. There are a series of steps to follow below and you should be able to create a machine in about 10 minutes once you have completed the pre-requisites.

    Enable Programmatically Deployment

    First, an annoyance. To be able to deploy Data Science virtual machines in Azure programmatically  you first have to login to the portal and click some buttons.

    In the Portal click new and then marketplace and then search for data science. Choose the Windows Data Science Machine and under the blue Create button you will see a link which says “Want to deploy programmatically? Get started” Clicking this will lead to the following blade.

    set-up-programmatically

    Click Enable and then save and you then move to PowerShell ??

    Azure PowerShell Cmdlets

    Follow the instructions here to install the Azure PowerShell modules. In the examples you see here I am using Windows 10 and PowerShell version 5.1.14393.479 and I installed the Azure modules using the Install-Module method

    Get the script

    To install a data science VM, we’ll use the New-WindowsDataScienceVM.ps1 script. In this script, I’m using version 1.2, but any version of this script published in PowerShell Gallery is fine.

    To install the New-WindowsDataScienceVM script from the PowerShell gallery, type:

    Install-Script New-WindowsDataScienceVM

    For  more information about using the PowerShellGet cmdlets to install scripts and modules from PowerShell Gallery, read this page. The PowerShellGet modules is included in PowerShell 5.0 and later on Windows 10, but you can install PowerShellGet for PowerShell 3.0 and 4.0. If you cannot connect to the gallery or prefer not to install the module, you can also find the script on GitHub.

    Login to Azure

    You can login to Azure using the command

    Login-AzureRMAccount

    which will pop-up a prompt for you to log into Azure

    login

    Enable Simple Mode

    The New-WindowsDataScienceVM function comes with a Simple switch parameter.

    If you use -Simple, the function prompts you only for the admin username and password for the virtual machine. It creates a randomly-named, standard_DS1_v2-sized machine in the ukwest data centre with standard, locally redundant storage in a randomly named Resource Group. All of the required objects have random names, too. If that is not what you want, there is more information at the end of this post. I am considering offering a pop-up to choose location in Simple Mode. Let me know here if that would be something you would like

    To create a simple data science VM, run:

    New-WindowsDataScienceVM -Simple Enter Local Admin Password

    When you run the function, it prompts for a local admin username and password to log into the virtual machine. The password must have 3 of the following 1 Upper case, 1 lower case, I special character and 1 number. Don’t lose it, you will need it.

    Local Admin.PNG

    Grab a cuppa, creating your VM and its resources will take 5 – 10 minutes. (In my testing it reliably took between 7 and 8 minutes)  The screen will look like this

    deploying.PNG

    When the script has finished running you will have deployed a set of resources like this

    portal

    Login to the Virtual Machine

    Copy and paste the correct code from the output at the end of the script to launch the RDP session and save the RDP file to your documents folder for later use.

    Or you can find the Virtual machine name in the portal or by running

    Get-AzureRmVM -ResourceGroupName <ResourceGroup> | Where-Object {$_.Name -like 'DSVM*'}

    You can then use the code below to download a RDP file and log into the virtual machine using this code

    Get-AzureRmRemoteDesktopFile -ResourceGroupName <ResourceGroup> -Name <VMName> -LocalPath C:\WIP\DataScienceVM.rdp -Launch

    rdp file.PNG

    You will need to login with the local admin account you set up previously, which means that you will need to click on more choices and then the machinename\Username. In this case the machine name is DSVMZIAgd

    You can copy the correct Virtual Machine name and Username from the output at the end of the script.

    login screen.PNG

    If you have forgotten your password, you can reset it in the Portal.

    Enjoy the Data Science Virtual Machine

    You are then logged in and can carry on. Once the Azure PowerShell modules and script are installed you would be able to have a machine up and running within 10 minutes.

    vm-desktop

    Cleaning Up

    To remove the resource group and ALL resources in the resource group, including the data science VM, run:

    Remove-AzureRmResourceGroup -Name <ResourceGroup> -Force

    This will remove ALL resources in that resource group, so be careful if you have deployed anything else.

    Customising the Deployment

    If you want to use different settings for the deployment or want to script the creation of a number of machines, you can run

    Get-Help New-WindowsDataScienceVM -Full

    and see all the options and further examples. Any questions please feel free to comment



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    Cognos BI0-122 Exam (Cognos 8 BI Administrator v2) Detailed Information



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