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COG-632 IBM Cognos 10 BI Metadata Model Developer

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COG-632 exam Dumps Source : IBM Cognos 10 BI Metadata Model Developer

Test Code : COG-632
Test designation : IBM Cognos 10 BI Metadata Model Developer
Vendor designation : IBM
: 53 actual Questions

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IBM IBM Cognos 10 BI

IBM Cognos company Intelligence 10.2.0 Reporting on IMS | killexams.com actual Questions and Pass4sure dumps

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IBM Cognos Analytics is named a winner within the 2019 huge trade Innovation Award | killexams.com actual Questions and Pass4sure dumps

The IBM Cloud, information and AI design crew is excited to publish that IBM Cognos Analytics has been named a winner within the 2019 great company Innovation Award. here's thrilling information for their group as IBM Cognos Analytics is certainly one of their most multifaceted products, with many designers and groups engaged on Gross aspects it. they are honored to beget this award prove off this mission, and the difficult toil their team has carried out on it.

what's IBM Cognos Analytics?

IBM Cognos Analytics is an AI-powered statistics exploration and presentation device that enhances the workflow of enterprise clients. The device contains the means to create compelling visualizations and dashboards, gain AI-powered selections for facts analysis, and scale the records analytics manner at the flat that a enterprise calls for.

IBM Cognos includes an AI coadjutant (left), and a aspect Panel (correct), to champion interpret the chart being considered. The coadjutant helps natural language exploration, while the aspect Panel offers extra device-generated plain-language suggestions to augment the chart. explore reduces the guesswork linked to facts exploration via surfacing the strongest relationships to your information, which is displayed in the network chart, and providing recommended genesis elements for more information exploration, which are proven within the thumbnails.

The AI-powered toolset in IBM Cognos Analytics enhances users’ skill to find the absolute best insights from their records. The toolset comprises innovations enjoy a herbal language based mostly digital assistant, as well as a knowledge exploration device that finds relationships within records and suggests information visualizations. These effective information outfit enable enterprise clients to find greater tips from their information and easily pinpoint deeper, underlying trends that may additionally in any other case require a better flat records skilled to decipher.

Storytelling elements in IBM Cognos Analytics present the capacity to imagine and monitor statistics with unique and fascinating visuals.

The presentation tools in IBM Cognos Analytics present advanced and visually engaging information visualizations, permitting clients to communicate the ideas in the back of their facts in innovative and compelling methods. The application includes outfit to create several charts and graphs in addition to motion pix, assisting to add a narrative aspect to the style the facts is displayed. These enjoyable experiences permit the person to communicate their insights in a more artistic approach, Cut back user interpretation error, and pomp the Story behind their facts rather than simply sharing floor stage counsel.

“IBM Cognos Analytics turned into at Gross times an excellent reporting and dashboarding tool. What made the most fresh release one of these soar forward become the introduction of predictive analytics, AI and a gross new means of exploring data and discovering patterns and relationships the clients didn’t comprehend existed. What i affection about it's that Cognos does so a gross lot of the weighty lifting when it involves analytics and soar-frogs you to the answers you requisite most.” — Anne Stevens, Design Lead

A win for design is a win for businessIBM Cognos Analytics being identified by using a trade award shows simply how a gross lot design can carry cost to the world of business. Their team’s heart of attention on outstanding person experiences and user research pushed design of this product, demonstrates that design manner and execution matters for company consequences. I’m excited to contemplate design proceed to beget an influence on their utility products and contemplate the broader implications this has for their users, valued clientele and partners.

in regards to the massive company Innovation AwardsThe company Intelligence group changed into situated with the mission of recognizing proper skill and superior performance within the company world. These awards are judged by using trade executives with immense suffer and skills. The firm’s entertaining scoring outfit selectively measures performance across diverse enterprise domains and rewards those organizations whose achievements in reality stand out.

Congrats to the IBM Cognos Analytics crew for Gross of your difficult toil and accomplishments.


IBM's Cognos 10: Combining Collaboration, Analytics for more suitable enterprise Intelligence | killexams.com actual Questions and Pass4sure dumps

IBM_logo_2009.jpg For a long time now, users had been asking what great Blue plans on doing with Gross the acquisitions in enterprise intelligence and analytics house (information, site) it made.

The unlock of Cognos 10, which combines sociable networking, collaboration, analytics and pursuits mobile workers, goes a way to answering that.

Pushed by means of the increase of cell transactions and estimates that the cellular workforce will globally achieve 1.19 billion by 2013, IBM has combined the technology of Cognos, a company they received in 2007, with Lotus Connections, launched in 2007 at Lotusphere, and is at present in version 2.5.

added to Gross this is the statistical engine, which IBM purchased Gross the way through the SPSS acquisition in 2009, which allows users to embed algorithms into their experiences from IBM's SPSS utility.

IBM and Analytics

IBM is besides releasing a number of different complimentary software applications that allows you to birth realizing the enterprise intelligence ambitions that the enterprise outlined when it bought Cognos Gross these years in the past. The other new applications encompass:

  • DB2 10: a brand new database that may fuse previous, latest and future data to deliver a single view of assistance via time
  • IBM InfoSphere Server: A server that gives users the potential to combine every kindly of records as smartly because the means to contemplate the fine of records before it is used.
  • The value of Cognos for BI

    The launch of Cognos 10 is so massive in IBM’s trade intelligence manner that it's hailing the unencumber as “probably the most tremendous analytics offering seeing that the acquisition of Cognos . . .” Taking a step back in time, when IBM bought Cognos it announced that within the trade they noticed the way forward for trade intelligence. They underlined that imaginative and prescient with the U.S.$ 5bn IBM paid for the business.

    enterprise intelligence refers to the shooting, integration and presentation of assistance. performance administration is using assistance to assess the performance of commercial enterprise sectors, divisions, product strains and every thing else which can besides subsist boiled down into numbers.

    the mixing of those two domains ensures a clearer photo of the position the firm basically stands, eradicating a stately deal of the wrong statistics and suggestions, which is unavoidable to distort the famous thing decision-makers vision. and that's what Cognos 10 does -- it gives the tools for determination champion and reporting equipment, in addition to Gross of the daily enterprise bells and whistles.

    IBM, Lotus Connection and Cognos

    Add to this Lotus Connection, which brings with it sociable collaboration, and besides you find some thought of what IBM is launching here.

    just to refresh your minds, IBM Lotus Connections is a sociable utility for company. It empowers trade professionals to advance,  nurture, and remain in contact with a community of their colleagues and respond privilege away to company alternatives. Future enhancements consist of:

  • improvements to how consumer generated content material is moderated
  • Compliance and auditing capabilities
  • Deeper integration with WebSphere Portal and SharePoint
  • extended cellular support
  • combine Lotus Connections utility with Cognos’s company analytics and one unified suffer with intuitive navigation capabilities and simplified person suffer looks.

    IBM, SPSS and Analytics

    The other foremost aspect during this free up comes from the mixing of IBM’s SPSS Modeler, which IBM bought in another acquisition, with the BI atmosphere. Now a allotment of Cognos' file-authoring capabilities, it enables clients to gain insights from historic statistics. With it, they could carry predictive indicators into dashboards to deliver forward-searching perception concerning risk, profitability and other situations.

    the new liberate strikes from traditional codecs of studies and charts, now supporting developed-in photographs and video clips that are interactive and supply broader analysis in a versatile format that can besides subsist accessed on most cell devices. With the brought analytics capabilities, users can besides check counsel from elementary, actual-time tips to superior, predictive “what if” analysis.

    in short, the release gives trade users not simply with counsel about what's occurring to their trade or products within the current, it besides integrates what has came about in the past, presenting an extrapolated version of what is going to revolve up sooner or later. Then, in the course of the sociable collaboration point, it shares the guidance with whoever wants it, each time they requisite it, and through cell guide, where ever they requisite it.

    the exhaust of interfaces taken from accepted Cognos environments, clients can engage with every different in precise time communities, wikis and blogs, submit documents and reports, tap into determination networks, and another collaborative feature obligatory to sprint counsel to clients who want it.

    over the last 5 years, IBM has invested greater than US$ 14 billion in 24 analytics acquisitions. introduced at the guidance on demand convention in Las Vegas, IBM illustrious Cognos 10 could subsist obtainable as of subsequent week when clients will subsist able to contemplate the position Gross that got firepower has been used.


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    IBM Cognos 10 BI Metadata Model Developer

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    Make latitude for Gartner's BI and Analytics Platforms MQ Leaders | killexams.com actual questions and Pass4sure dumps

    Make  latitude for Gartners BI and Analytics Platforms MQ Leaders

    Twenty four is a throng -- yet that’s how many vendors made it into Gartner’s Magic Quadrant for trade Intelligence (BI) and Analytics circa 2015.

    For enterprises that are shopping for BI and Analytics solutions, choice may seem enjoy a fine thing until you consider what evaluating that many vendors might contemplate enjoy -- most of us wouldn’t even consider trying on 24 pairs of jeans.

    To subsist fair, this is how many BI and analytics vendors met Gartner’s criteria, and that’s no simple feat. Gartner defines BI and Analytics as a “software platform that delivers 13 critical capabilities across three categories -- enable, yield and consume -- in champion of four exhaust cases for BI and analytics.”

    Use cases achieve from centralized BI Provisioning, to decentralized analytics, to governed data discovery to providing OEM embedded APIs. 

    Required capabilities in the “Enable” category include: trade User Data Mashup and Modeling, Internal Platform Integration, BI Platform Administration, Metadata Management, Cloud Deployment, and progress and Integration.

    The “Produce” category considers Free-Form Interactive Exploration, Analytic Dashboards and Content, IT-Developed Reporting and Dashboards, and Traditional Styles of Analysis.

    The “Consume” category looks at Mobile, Collaboration and sociable Integration, and Embedded BI. 

    The common trends Gartner analysts uncovered heart around decentralized and bimodal governed data discovery approaches to BI and a summon for self-service capabilities beyond data discovery and interactive visualization of IT-curated data sources.

    It’s worth noting that most BI and Analytics vendors are placed in the same quadrant as in 2014, but several beget changed positions within them because of their more limited vision or skill to execute.

    2015-19-March-Gartner-BI-Analytics-MQ.png

    Gartner Magic Quadrant for trade Intelligence and Analytics Platforms

    Tableau Leads the Pack

    It should near as no astound that Tableau’s results last quarter thrilled investors. Why? Because enterprise IT bosses, trade analysts and quants Gross affection the results its products produce. Tableau is the product to beat in the discovery space and no one else even comes close.

    Moreover, as we’ve written time and time again, Tableau users affection Tableau -- they are literally enthused when they talk about it. Not just that, but you can besides engage data from someplace enjoy an outdo file and gain sparkling visualizations that uncover stories.

    On the downside, Gartner’s report says that Tableau’s dashboards, traditional styles of analysis, metadata management, progress and integration, BI platform administration, embedded BI and collaboration are weaknesses. It could besides exhaust a few improvements when used in centralized environments.

    These may, or may not, subsist functions and features that Tableau wants to provide.

    It’s besides worth asking if any particular vendor can subsist the “go to” for every BI and Analytics endeavor.

    SAS Wins When it Comes to Completeness of Vision

    SAS has been around since 1966 so appearing as the most visionary vendor in the Leaders quadrant is impressive. Not only that, but SAS is besides further to the privilege on the “Completeness of Vision” axis than any other vendor, even those in the Visionaries quadrant. It’s worth considering, though, that SAS offers what Gartner calls a “vast array” of integrated components, so they’re not exactly comparing it apples-to-apples when it comes to vendors enjoy Tableau. 

    That being said, SAS Visual Analytics -- which has been around for just a few years -- is gaining traction against data discovery leaders partly because it has done a fine job of differentiating itself and partly because its pricing model is more appealing. Gartner besides praises SAS’s forward thinking in areas such as embedded advanced analytics within SAS Visual Analytics, ease of navigation between SAS Visual Analytics and SAS Visual Statistics, and integration across the SAS platform and other core analytics which makes governed data discovery simpler.

    Within the Enterprise, SAS doesn’t seem to subsist as widely deployed as it could subsist because of cost, but Gartner says the vendor is addressing the issue. This could subsist a win for SAS not only in deeper penetration at current customer sites but its skill to win trade at smaller companies.

    Improved Vision and Execution for SAP

    SAP sits almost neck and neck with SAS in the Leaders quadrant. Gartner likes its vision, citing the direction it is taking with SAP Lumira and its simplified strategy for BI platform components.

    What’s especially notable is that Gartner researchers organize that SAP is the BI criterion (81 percent) in the companies in which it is used. (In other words, it’s not one of several tools). SAP has an advantage in this regard, because in many cases those companies besides exhaust SAP’s widely deployed ERP systems. SAP does, however, title to beget BI and Analytics customers who don’t exhaust its ERP products.

    That being said, SAP’s BI tools offering leaves plenty of latitude for improvement in terms of customer suffer at almost every flat -- ranging from the buying process, to the UI, to providing trade user data discovery capabilities.

    We should note that SAP’s new management team has pledged to engage action on Gross of these fronts. They should beget an indication on how that’s going by this time next year.

    Watson Gives IBM Vision, But There’s More to Consider

    Gartner says that the Enterprise likes the pitch that IBM Watson analytics makes but that it’s still too early to contemplate how widely adopted it becomes. On the other hand, IBM Cognos, IBM’s integrated BI platform (it offers capabilities for Web-based ad hoc query, report and dashboard authoring and consumption, OLAP, scorecarding, production reporting, scheduling, alerting, data discovery and mobile) is embedded and sold in over 130 IBM applications. What a stately way to gain market presence.

    IBM’s challenge is the same one that many legacy vendors physiognomy with their existing (and quite often market leading) products -- delivering a positive customer suffer and meeting famous business-user-centric market requirements. 

    Qlik Rules Where It Plays

    Some of the vendors that rank lower (actually further left on the “Completeness of Vision" axis) in Gartner’s Leaders quadrant finish so because they are more narrowly focused. Qlik falls into that category. It has two BI & Analytics products based on an in-memory associative search engine.

    Gartner calls QlikView,

    a mature, self-contained, tightly integrated progress platform used by IT or more technical users for edifice intuitive and interactive dashboard applications faster and easier than traditional BI platforms.” 

    Qlik’s newer product, Qlik Sense (released in September 2014) is trade user friendly and provides a  way for them to build their own dashboards while giving IT the skill to govern, manage, scale and embed them. Gartner likes what it sees in it.

    Qlik’s challenge, according to Gartner, will subsist in selling and supporting two products at once without sacrificing customer experience.

    Business-User Enablement, A limpid Focus for Microsoft

    Microsoft is stately enough and smart enough to leverage SQL Server and SharePoint to champion IT-developer-centric data management, reporting and administration requirements and to provide Power BI for stay users. Gartner likes the approach and the possibilities that it holds, stating that Microsoft has,

    a limpid understanding of the market's want for a platform that can champion systems-of-record requirements and deliver easy-to-use data discovery capabilities, with champion for promotability of business-user content and governance.”

    Though Power BI has yet to subsist widely adopted (its new availability in browser should help), it could alleviate a lot of problems. But Microsoft would still requisite to simplify its license models for that to happen.

    According to Gartner,

    the fact that many of the newer capabilities that are famous to buyers in this market require current versions of Office, SQL Server and SharePoint adds to the complexity and represents a barrier to adoption for many organizations that are on older versions and are not yet willing to buy Office 365 and deploy BI and analytics in the cloud.”

    Further, it says that the workflow between Power Query, Power View and Power Pivot is not yet completely seamless. Not only that, but there are besides questions about the role of SharePoint dashboards and Reporting and the fact that the latter is not supported in Azure.

    There besides seem to subsist difficulties with the customer experience. Gartner says that they beget “historically organize it difficult to engage directly with Microsoft during the sales cycle, which was a major complaint from IT but is an even greater concern for Microsoft as it attempts to appeal to trade buyers that beget tall expectations of simplified license models and purchase options.” 

    Other Leaders to Consider

    We should note that these are but six of the nine MQ Leaders (we took the top 3 from the “Completeness of Visions" Axis and the top 3 from the “Ability to Execute”). Oracle, Information Builders and Microstrategy were besides named Leaders.


    Cognos Basics Tutorial — ABC LEARN | killexams.com actual questions and Pass4sure dumps

    Data Warehouse Concepts

    What is a Data Warehouse?

    A warehouse in universal terms is a repository where they store their information. Coming to the data warehouse, it is a collection of data organized in a specific manner and categorized information.

    A data warehouse stores historical data of an organization so that they can dissect their performance over the past years and plot for the future.

    The approved definition of the data warehouse by WH Inmon:

    A data warehouse is:

  • Subject oriented: Data in the warehouse is categorized in different matter areas. For example, consider a KFC store. It has many branches Gross over the world. If they beget to dissect “sales” for India, this is termed as “subject”.
  • Integrated: A data warehouse has data coming from multiple sources which are integrated into the warehouse. For example, consider the same KFC store, stores in India may store date field as “dd/mm/yyyy” whereas the same data in another country will subsist stored as “MM/DD/YYYY”. The data warehouse will beget only one format fixed to allege “MM/DD/YYYY”.
  • Time-Variant: A data warehouse stores historical data with which they can identify patterns of sales over a time age of 3 months,6 months or 2 years of any organization which has a warehouse.
  • Non-Volatile: Data in the warehouse will not change once it is entered.
  • Another definition from Ralph Kimball is more precise:

    A data warehouse is a copy of transaction data specifically structured for query and analysis.

    What is a Data mart?

    A data mart is a subset of a data warehouse. Suppose they beget an organization established in many different locations and each location maintains a data warehouse which they summon it as data mart because a warehouse will beget Gross the data integrated and as far as data mart is considered it will subsist a allotment of the data warehouse.

    What are uses of having a Data Warehouse?

    A Data Warehouse, in general, is used to dissect trends over a age of time and enhances the decision making of an organization. Once the data is loaded into the warehouse will subsist creating an OLAP cube or directly exhaust the data to dissect trends. Basing on which the top flat management will approach their future trade strategies.

    Data warehouse Architecture

    We will multiple sources there Gross the operational data will subsist stored. Some may store them in flat files and some in databases. They will beget to read Gross the sources and fulfill ETL (Extract, Transform and Load operations) which will subsist used to integrate data from these sources and transform them into one unique structure and then load into their target data warehouse.

    Data Warehouse Architecture

    There are 2 approaches in designing your data warehouse. First one is “Top-Down approach” and second is “Bottom-up approach”.

    Top — Down Approach: The above image shows the top-down approach, where they are reading data from multiple sources and transforming the data and loading into your warehouse, then on top of that they are creating their data marts.

    Bottom-up Approach: The contradictory of the above approach is this. Here they will subsist creating data marts first and then they will create their data ware house on top of Gross the data marts.

    Data Warehouse Terms

  • Dimension: A Dimension is a categorical information which is stored in a data warehouse.
  • Fact: Fact is a measurable quantity by which they can actually pattern out what the dimension does.
  • Attribute: Attributes are the elements in a dimension  Ex: Suppose they beget product information classified in a dimension. The attributes of the “Product dimension” are: “Product_ID, Product_Name, Product_Color” etc.  Sales is a fact table where you store numeric values associated with the dimension attributes.For instance, they beget to compute the number of sales of each product for the current month.In this case, Product is the dimension and Month is a dimension and numbers of product sold is the fact.
  • OLAP: Online Analytical Processing is a multidimensional model using which users can view data in multiple dimensions at a single glance.
  • Types of OLAP’s:
  • Relational OLAP
  • Multi-dimensional OLAP
  • Hybrid OLAP
  • OLTP: Online transactional processing is a transaction based model where the main direct will subsist to retrieve data faster and update transactions quickly.
  • Schemas in Datawarehouse

    The schema is a logical arrangement of tables in a data warehouse. They beget schemas in relational databases. Very much enjoy same Data warehouse has schema’s namely Star schema, Snowflake schema, and Fact-Constellation.

    Star Schema

    In this logical arrangement of tables, the fact table will subsist at the heart of the schema surrounded by multiple dimension tables. Fact and dimension tables are linked via a Primary — Foreign Key relationship.  Fact table will subsist having curious keys of Gross the Dimension tables along with the facts whereas a Dimension table will subsist having attributes which describe the Dimension.

    Snowflake Schema:

    In this logical arrangement of tables, they beget dimension tables connected to other dimension tables which in revolve are connected to fact table via a primary-foreign key relationship.  Else they can allege that the Dimension tables in Snowflake schema are normalized.  Because of the normalization, the data redundancy will subsist reduced and a lot of storage space is saved.

    Fact Constellation Schema:

    A fact constellation schema, unlike the start or snowflake, will beget multiple fact tables. It is besides called as Galaxy Schema.

    Star Schema Example

    In this schema shown, a fact table (sales) is connected to multiple dimension tables item, location, time and branch. Each dimension table has attributes describing the dimension and fact table has the curious key of Gross dimension tables along with facts enjoy dollars_sold and units sold.

    Snowflake Schema Example:

    In this schema, a fact table (sales) is connected to multiple dimensions, whereas the dimensions item and location are again connected to City and Supplier dimension respectively.

    Fact Constellation schema Example:

    In this schema, they can find two fact table Sales and Shipping connecting with each other and are again connected to multiple dimension tables individually.

    Now they are jumping into the actual topic “Cognos trade Intelligence”.

    Overall DWH architecture

    We will subsist dealing with Cognos reporting implement and Cognos Metadata managing implement and other useful tools that Cognos BI has.

    Let’s Go through where BI will actually near into picture

    The below image shows overall Data warehouse Architecture. From data load to using the data loaded into the tables for enhancing the trade of your organization. From Gross the data sources (may it subsist Operational databases, Flat files etc.) they load the data into data warehouse through different ETL process and creating cubes and using the data for mining purpose.

    The below pattern will describe the trade Intelligence flux how the data will subsist used for trade optimization. trade intelligence is a process from where you will subsist able to derive methods to enhance your trade with the data that you have. Using BI one will subsist able to contemplate at their data at different levels and will subsist able to gain decisions to gain their trade better.

    Cognos BI will subsist used to generate reports (list, crosstab, and charts). The below pattern will prove you the entire flux that happens with Cognos.

    The first step in using Cognos BI is to accumulate requirements. This is feasible when you understand your trade structure and the data that drives your business. Once the requirement is finalized the next step is to create your framework metadata model. You can pick either to you star schema or snowflake schema or you can exhaust the existing relational model.

    After this step is done with they beget to publish the model that they beget created. Publishing the model will gain your metadata model available for reporting purpose. Once your model is published, they can exhaust the model to create different types of reports as per your requirement and they will schedule the reports basing your requirement.

    Different Versions of Cognos

    Cognos ReportNet

    Cognos ReportNet (CRN) is a web-based software product for creating and managing ad-hoc and custom-made reports. ReportNet is developed by the Ottawa-based company Cognos (formerly Cognos Incorporated), an IBM company. The web-based reporting implement was launched in September 2003. Since IBM’s acquisition of Cognos, ReportNet has been renamed IBM Cognos ReportNet enjoy Gross other Cognos products.

    Components:

  • Cognos Report Studio — A Web-based product for creating tangled professional looking reports
  • Cognos Query Studio — A Web-based product for creating ad-hoc reports.
  • Cognos Framework Manager — A metadata modeling implement to create BI metadata for reporting and dashboard applications.
  • Cognos Connection — the Main portal used to access reports, schedule reports and fulfill administrator activities.
  • Cognos 8.x

    IBM Cognos 8 BI, initially launched in September 2005, combined the features of several previous products, including ReportNet, PowerPlay, Metrics Manager. There are besides Express and Extended versions of Cognos 8 BI. complete features:

    Components:

  • Report Studio (Professional report authoring implement formatted for the web)
  • Query Studio (Ad hoc report authoring implement with instant data preview)
  • Analysis Studio (Explore multi-dimensional cube data to retort trade questions)
  • Metric Studio (Monitor, analyze, and report on KPIs)
  • Metric Designer (Define, load, and maintain metrics to subsist available in Metric Studio)
  • Event Studio (Action based agents to notify decision makers as events happen)
  • Framework Manager (Semantic metadata layer implement which creates models or packages)
  • PowerPlay Studio (formerly PowerPlay Web)
  • Analytic Applications (Packaged BI Applications, built on an adaptable platform and extensible into trade Analytics)
  • Cognos 10.x

    We can contemplate different components and how they are fitting in. The top most layer, where they can contemplate Cognos Connection, Administrator, trade Insight and different studios. They Gross are web-based and end-user needs not to install any client side software if he has the latest web browser installed.

    The bottom layer is basically data layer where you may beget homogeneous or heterogeneous database systems. Data may subsist relational or multi-dimensional. On top of it, they can contemplate three modeling tools there — Framework Manager, Transformer, and Metric Designer. Gross of them are client based installation.

    We’ll maintain the flux of components from top to bottom as shown in below BI components figure.

    Before getting into the actual topics about how you create the model, deploy it and exhaust it to create reports they beget to first know the architecture of Cognos.

    Cognos Architecture:

    Cognos trade Intelligence framework is 3- tier architecture. The first tier of the architecture will subsist having the Web server which is answerable for the accessing the user interfaces. The second tier will subsist having IBM Cognos BI Server which will subsist having gateways and dispatchers to route requests from different UI’s to Database and communicate back to the server.

    The Third Tier will beget the data sources. You can beget multiple data sources and they will connect to the Cognos server using JDBC or API’s.

    The Below pattern shows the architecture of Cognos BI:

    In the data tier, you can contemplate one more component called the “Content Store” which is a repository for the Cognos server. Whenever you reclaim any objects reports, models everything the data is stored in Content Store database which once you reopen or reuse the existing kick created will fetch the data from the Content Store. Gross this toil will subsist performed by the “Content Manager”.

    Content Manager: The content manager is answerable for connecting with the Content Store database and saving any report or model back to the Content Store.

    Dispatcher: The job of dispatchers is to route requests sent from and to the Web UI/Windows based components.

    Web-based and Windows Based components of Cognos

    Cognos BI has two types of components,

    1. Web-Based components.

    2. Window based components.

    Web-based components includes

  • Cognos Connection: This is the portal from where you can access Gross the web-based components of IBM Cognos BI.
  • Cognos Administration: Using this web-based UI you can fulfill administrative tasks enjoy granting access, revoking access, creating users, groups and defining roles etc.,
  • Cognos Report Studio: Using Report studio implement they can create reports and format the reports. They can create multiple types of reports enjoy list report, crosstab reports, graph’s etc.,
  • Cognos Query Studio: Here they create ad-hoc reports. Ad-hoc reports are useful when a user wants to contemplate the report without any prompts and less formatting. Using query studio they can create a report instantly.
  • Cognos Metric Studio: This studio is used to build customized scorecards reports to monitor and dissect metrics.
  • Cognos Analysis studio: This studio is used to dissect data from different dimensions and besides compare trends.
  • Cognos trade Insight: This implement is used to build dashboards. A dashboard allows a user to quickly contemplate into the data and enhance the decision making.
  • Cognos trade Insight Advanced: With this tool, they can build more powerful dashboards and as well as simple reports.
  • Window-based Components:

  • Cognos Framework Manager: Cognos framework manager is a implement used to create metadata models which they can exhaust in Report studio or analysis studio.
  • Cognos Map Manager: Using this implement they can create maps which will allow you to create a new region by using the existing regions.
  • http://www.abclearn.com/courses/cognos/cognos-tutorial/cognos-framework-manager



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