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C2020-632 IBM Cognos 10 BI Metadata Model Developer

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C2020-632 exam Dumps Source : IBM Cognos 10 BI Metadata Model Developer

Test Code : C2020-632
Test title : IBM Cognos 10 BI Metadata Model Developer
Vendor title : IBM
: 53 true Questions

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IBM IBM Cognos 10 BI

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IBM Cognos Analytics is named a winner within the 2019 great company Innovation Award | killexams.com true Questions and Pass4sure dumps

The IBM Cloud, records and AI design team is ecstatic to publish that IBM Cognos Analytics has been named a winner within the 2019 colossal enterprise Innovation Award. this is exciting information for their team as IBM Cognos Analytics is one in every of their most multifaceted items, with many designers and teams engaged on entire elements it. they are honored to maintain this award exhibit this challenge, and the complicated work their group has accomplished on it.

what is IBM Cognos Analytics?

IBM Cognos Analytics is an AI-powered data exploration and presentation tool that enhances the workflow of enterprise users. The device includes the capacity to create compelling visualizations and dashboards, develop AI-powered selections for information evaluation, and scale the records analytics process on the level that a enterprise calls for.

IBM Cognos contains an AI helper (left), and a detail Panel (right), to uphold interpret the chart being viewed. The helper supports natural language exploration, whereas the detail Panel offers extra equipment-generated simple-language tips to augment the chart. explore reduces the guesswork associated with information exploration through surfacing the strongest relationships to your data, which is displayed within the community chart, and providing counseled starting points for extra data exploration, which are shown in the thumbnails.

The AI-powered toolset in IBM Cognos Analytics enhances clients’ potential to win the very best insights from their information. The toolset contains improvements enjoy a natural language primarily based virtual assistant, in addition to a data exploration device that finds relationships within records and suggests information visualizations. These potent information tools enable enterprise users to win extra information from their information and simply pinpoint deeper, underlying developments that may otherwise require a far better level information knowledgeable to decipher.

Storytelling points in IBM Cognos Analytics proffer the aptitude to visualize and display statistics with specific and interesting visuals.

The presentation rig in IBM Cognos Analytics proffer involved and visually attractive records visualizations, permitting users to converse the ideas behind their records in ingenious and compelling methods. The utility contains rig to create exact charts and graphs as well as movement pics, helping so as to add a sage factor to the style the statistics is displayed. These pleasing experiences permit the person to communicate their insights in a extra creative approach, reduce person interpretation blunders, and screen the sage at the back of their records in preference to just sharing floor level suggestions.

“IBM Cognos Analytics turned into entire the time a very fine reporting and dashboarding device. What made the most fresh release any such bounce ahead become the introduction of predictive analytics, AI and a all original manner of exploring records and discovering patterns and relationships the users didn’t recognize existed. What i enjoy about it's that Cognos does so lots of the hefty lifting when it comes to analytics and start-frogs you to the answers you want most.” — Anne Stevens, Design Lead

A win for design is a win for businessIBM Cognos Analytics being diagnosed by a commerce award shows just how an terrible lot design can deliver cost to the belt of enterprise. Their crew’s hub of attention on incredible person experiences and person research pushed design of this product, demonstrates that design approach and execution matters for commerce results. I’m excited to survey design continue to maintain an repercussion on their utility items and spot the wider implications this has for their users, valued clientele and companions.

in regards to the massive enterprise Innovation AwardsThe commerce Intelligence group become situated with the mission of recognizing proper skill and superior performance in the commerce world. These awards are judged by enterprise executives with substantial sustain and expertise. The firm’s affecting scoring device selectively measures efficiency across distinctive company domains and rewards these companies whose achievements in reality stand out.

Congrats to the IBM Cognos Analytics crew for your all complicated work and accomplishments.


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

IBM_logo_2009.jpg For a long time now, users had been asking what huge Blue plans on doing with the entire acquisitions in company intelligence and analytics belt (information, web site) it made.

The unencumber of Cognos 10, which combines gregarious networking, collaboration, analytics and pursuits mobile people, goes some passage to answering that.

Pushed by the growth of mobile transactions and estimates that the cell workforce will globally attain 1.19 billion by means of 2013, IBM has mixed the technology of Cognos, a company they bought in 2007, with Lotus Connections, launched in 2007 at Lotusphere, and is at the minute in edition 2.5.

brought to entire that is the statistical engine, which IBM purchased entire through the SPSS acquisition in 2009, which makes it workable for users to embed algorithms into their reports from IBM's SPSS application.

IBM and Analytics

IBM is also releasing a yoke of other complimentary utility programs for you to dawn realizing the company intelligence ambitions that the company outlined when it bought Cognos entire these years in the past. The different original packages consist of:

  • DB2 10: a brand original database that can combine previous, present and future records to supply a lone view of counsel via time
  • IBM InfoSphere Server: A server that offers users the capability to combine entire kinds of information as neatly because the skill to survey the worthy of statistics earlier than it is used.
  • The significance of Cognos for BI

    The launch of Cognos 10 is so gigantic in IBM’s company intelligence strategy that it is hailing the free up as “probably the most gigantic analytics providing since the acquisition of Cognos . . .” Taking a step returned in time, when IBM bought Cognos it announced that in the company they saw the passage forward for commerce intelligence. They underlined that imaginative and prescient with the U.S.$ 5bn IBM paid for the business.

    company intelligence refers back to the taking pictures, integration and presentation of assistance. performance management is the usage of information to check the efficiency of commercial enterprise sectors, divisions, product lines and every small thing else which will also live boiled down into numbers.

    the mixing of those two domains ensures a clearer lifelike of the space the corporation basically stands, eradicating a fine deal of the spoiled statistics and tips, which is confident to twist the considerable thing determination-makers vision. and that's the understanding what Cognos 10 does -- it provides the tools for determination assist and reporting tools, as well as entire of the criterion commerce bells and whistles.

    IBM, Lotus Connection and Cognos

    Add to this Lotus Connection, which brings with it gregarious collaboration, and also you win some concept of what IBM is launching privilege here.

    just to refresh your minds, IBM Lotus Connections is a gregarious software for enterprise. It empowers company authorities to improve,  nurture, and abide in contact with a community of their colleagues and reply rapidly to enterprise opportunities. Future enhancements include:

  • advancements to how user generated content material is moderated
  • Compliance and auditing capabilities
  • Deeper integration with WebSphere Portal and SharePoint
  • multiplied cell help
  • mix Lotus Connections utility with Cognos’s company analytics and one unified journey with intuitive navigation capabilities and simplified consumer journey appears.

    IBM, SPSS and Analytics

    The other major point in this free up comes from the integration of IBM’s SPSS Modeler, which IBM purchased in a further acquisition, with the BI ambiance. Now allotment of Cognos' document-authoring capabilities, it permits users to gain insights from ancient facts. With it, they could deliver predictive indications into dashboards to deliver ahead-searching insight related to risk, profitability and different situations.

    the brand original unlock strikes from traditional formats of stories and charts, now assisting built-in photos and movies that are interactive and provide broader analysis in a all-around layout that can live accessed on most cellular instruments. With the brought analytics capabilities, users can also assess tips from simple, real-time information to advanced, predictive “what if” evaluation.

    in short, the free up gives company users not simply with assistance about what is happening to their commerce or items in the current, it additionally integrates what has happened in the past, featuring an extrapolated version of what will revolve up sooner or later. Then, throughout the gregarious collaboration factor, it shares the counsel with whoever wants it, whenever they want it, and thru mobile assist, where ever they want it.

    the expend of interfaces taken from familiar Cognos environments, users can engage with every different in suitable time communities, wikis and blogs, do up files and studies, tap into determination networks, and every other collaborative characteristic obligatory to flood assistance to clients who exigency it.

    during the last five years, IBM has invested greater than US$ 14 billion in 24 analytics acquisitions. introduced on the counsel on demand conference in Las Vegas, IBM said Cognos 10 might live accessible as of next week when clients should live able to survey the space entire that received firepower has been used.


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    IBM Cognos 10 BI Metadata Model Developer

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

    Make  scope for Gartners BI and Analytics Platforms MQ Leaders

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

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

    To live fair, this is how many BI and analytics vendors met Gartner’s criteria, and that’s no smooth feat. Gartner defines BI and Analytics as a “software platform that delivers 13 critical capabilities across three categories -- enable, bear and consume -- in uphold of four expend 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: commerce 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 gregarious Integration, and Embedded BI. 

    The common trends Gartner analysts uncovered hub around decentralized and bimodal governed data discovery approaches to BI and a call 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 very quadrant as in 2014, but several maintain changed positions within them because of their more limited vision or aptitude to execute.

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

    Gartner Magic Quadrant for commerce Intelligence and Analytics Platforms

    Tableau Leads the Pack

    It should achieve as no astound that Tableau’s results last quarter thrilled investors. Why? Because enterprise IT bosses, commerce analysts and quants entire savor 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 savor Tableau -- they are literally enthused when they talk about it. Not just that, but you can also win data from someplace enjoy an exceed file and develop comely visualizations that counsel 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 also expend a few improvements when used in centralized environments.

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

    It’s also worth asking if any particular vendor can live 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 also 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 also 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 appear to live as widely deployed as it could live because of cost, but Gartner says the vendor is addressing the issue. This could live a win for SAS not only in deeper penetration at current customer sites but its aptitude to win commerce 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 establish 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 odds in this regard, because in many cases those companies also expend SAP’s widely deployed ERP systems. SAP does, however, pretense to maintain BI and Analytics customers who don’t expend its ERP products.

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

    We should note that SAP’s original management team has pledged to win action on entire of these fronts. They should maintain 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 noiseless too early to survey 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 worthy passage to gain market presence.

    IBM’s challenge is the very one that many legacy vendors visage with their existing (and quite often market leading) products -- delivering a positive customer sustain and meeting considerable 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 carry out 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 commerce user friendly and provides a  passage for them to build their own dashboards while giving IT the aptitude to govern, manage, scale and embed them. Gartner likes what it sees in it.

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

    Business-User Enablement, A clear Focus for Microsoft

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

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

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

    According to Gartner,

    the fact that many of the newer capabilities that are considerable 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 also questions about the role of SharePoint dashboards and Reporting and the fact that the latter is not supported in Azure.

    There also appear to live difficulties with the customer experience. Gartner says that they maintain “historically establish 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 commerce buyers that maintain towering 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 also named Leaders.


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

    Data Warehouse Concepts

    What is a Data Warehouse?

    A warehouse in generic 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 analyze their performance over the past years and arrangement for the future.

    The common definition of the data warehouse by WH Inmon:

    A data warehouse is:

  • Subject oriented: Data in the warehouse is categorized in different theme areas. For example, deem a KFC store. It has many branches entire over the world. If they maintain to analyze “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, deem the very KFC store, stores in India may store date realm as “dd/mm/yyyy” whereas the very data in another country will live stored as “MM/DD/YYYY”. The data warehouse will maintain only one format fixed to converse “MM/DD/YYYY”.
  • Time-Variant: A data warehouse stores historical data with which they can identify patterns of sales over a time period 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 maintain an organization established in many different locations and each location maintains a data warehouse which they call it as data mart because a warehouse will maintain entire the data integrated and as far as data mart is considered it will live a allotment of the data warehouse.

    What are uses of having a Data Warehouse?

    A Data Warehouse, in general, is used to analyze trends over a period of time and enhances the determination making of an organization. Once the data is loaded into the warehouse will live creating an OLAP cube or directly expend the data to analyze trends. Basing on which the top level management will approach their future commerce strategies.

    Data warehouse Architecture

    We will multiple sources there entire the operational data will live stored. Some may store them in flat files and some in databases. They will maintain to read entire the sources and fulfill ETL (Extract, Transform and Load operations) which will live 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 opposite of the above approach is this. Here they will live creating data marts first and then they will create their data ware house on top of entire 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 device out what the dimension does.
  • Attribute: Attributes are the elements in a dimension  Ex: Suppose they maintain 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 maintain to calculate 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 lone glance.
  • Types of OLAP’s:
  • Relational OLAP
  • Multi-dimensional OLAP
  • Hybrid OLAP
  • OLTP: Online transactional processing is a transaction based model where the main point will live to retrieve data faster and update transactions quickly.
  • Schemas in Datawarehouse

    The schema is a logical arrangement of tables in a data warehouse. They maintain schemas in relational databases. Very much enjoy very 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 live at the hub of the schema surrounded by multiple dimension tables. Fact and dimension tables are linked via a Primary — Foreign Key relationship.  Fact table will live having alien keys of entire the Dimension tables along with the facts whereas a Dimension table will live having attributes which recount the Dimension.

    Snowflake Schema:

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

    Fact Constellation Schema:

    A fact constellation schema, unlike the start or snowflake, will maintain multiple fact tables. It is also 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 alien key of entire 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 particular 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 commerce Intelligence”.

    Overall DWH architecture

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

    Let’s fade through where BI will actually achieve into picture

    The below image shows overall Data warehouse Architecture. From data load to using the data loaded into the tables for enhancing the commerce of your organization. From entire the data sources (may it live 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 device will recount the commerce Intelligence flood how the data will live used for commerce optimization. commerce intelligence is a process from where you will live able to derive methods to enhance your commerce with the data that you have. Using BI one will live able to survey at their data at different levels and will live able to develop decisions to develop their commerce better.

    Cognos BI will live used to generate reports (list, crosstab, and charts). The below device will point to you the entire flood that happens with Cognos.

    The first step in using Cognos BI is to collect requirements. This is workable when you understand your commerce 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 elect either to you star schema or snowflake schema or you can expend the existing relational model.

    After this step is done with they maintain to publish the model that they maintain created. Publishing the model will develop your metadata model available for reporting purpose. Once your model is published, they can expend 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 tool was launched in September 2003. Since IBM’s acquisition of Cognos, ReportNet has been renamed IBM Cognos ReportNet enjoy entire other Cognos products.

    Components:

  • Cognos Report Studio — A Web-based product for creating involved professional looking reports
  • Cognos Query Studio — A Web-based product for creating ad-hoc reports.
  • Cognos Framework Manager — A metadata modeling tool 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 also Express and Extended versions of Cognos 8 BI. full features:

    Components:

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

    We can survey different components and how they are fitting in. The top most layer, where they can survey Cognos Connection, Administrator, commerce Insight and different studios. They entire 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 maintain homogeneous or heterogeneous database systems. Data may live relational or multi-dimensional. On top of it, they can survey three modeling tools there — Framework Manager, Transformer, and Metric Designer. entire of them are client based installation.

    We’ll maintain the flood 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 expend it to create reports they maintain to first know the architecture of Cognos.

    Cognos Architecture:

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

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

    The Below device shows the architecture of Cognos BI:

    In the data tier, you can survey one more component called the “Content Store” which is a repository for the Cognos server. Whenever you redeem any objects reports, models everything the data is stored in Content Store database which once you reopen or reuse the existing demur created will fetch the data from the Content Store. entire this work will live performed by the “Content Manager”.

    Content Manager: The content manager is accountable 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 entire 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 tool 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 survey 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 analyze metrics.
  • Cognos Analysis studio: This studio is used to analyze data from different dimensions and also compare trends.
  • Cognos commerce Insight: This tool is used to build dashboards. A dashboard allows a user to quickly survey into the data and enhance the determination making.
  • Cognos commerce 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 tool used to create metadata models which they can expend in Report studio or analysis studio.
  • Cognos Map Manager: Using this tool they can create maps which will allow you to create a original region by using the existing regions.
  • http://www.abclearn.com/courses/cognos/cognos-tutorial/cognos-framework-manager



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